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DTSTART;TZID=America/New_York:20190418T160000
DTEND;TZID=America/New_York:20190418T170000
DTSTAMP:20250328T150900Z
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SUMMARY:Yip Annual Lecture
DESCRIPTION:On April 18\, 2019 Harvard CMSA hosted the inaugural Yip lecture. The Yip Lecture takes place thanks to the support of Dr. Shing-Yiu Yip. This year’s speaker was Peter Galison (Harvard Physics). \nThe lecture was held from 4:00-5:00pm in Science Center\, Hall A.
URL:https://live-hu-cmsa-222.pantheonsite.io/event/yip-annual-lecture/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Event,Public Lecture,Special Lectures,Yip Lecture Series
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DTSTART;TZID=America/New_York:20190415T091500
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DTSTAMP:20250304T172154Z
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SUMMARY:Workshop on Invariance and Geometry in Sensation\, Action and Cognition
DESCRIPTION:As part of the program on Mathematical Biology a workshop on Invariance and Geometry in Sensation\, Action and Cognition will take place on April 15-17\, 2019. \nLegend has it that above the door to Plato’s Academy was inscribed “Μηδείς άγεωµέτρητος είσίτω µον τήν στέγην”\, translated as “Let no one ignorant of geometry enter my doors”. While geometry and invariance has always been a cornerstone of mathematics\, it has traditionally not been an important part of biology\, except in the context of aspects of structural biology. The premise of this meeting is a tantalizing sense that geometry and invariance are also likely to be important in (neuro)biology and cognition. Since all organisms interact with the physical world\, this implies that as neural systems extract information using the senses to guide action in the world\, they need appropriately invariant representations that are stable\, reproducible and capable of being learned. These invariances are a function of the nature and type of signal\, its corruption via noise\, and the method of storage and use. \nThis hypothesis suggests many puzzles and questions: What representational geometries are reflected in the brain? Are they learned or innate? What happens to the invariances under realistic assumptions about noise\, nonlinearity and finite computational resources? Can cases of mental disorders and consequences of brain damage be characterized as break downs in representational invariances? Can we harness these invariances and sensory contingencies to build more intelligent machines? The aim is to revisit these old neuro-cognitive problems using a series of modern lenses experimentally\, theoretically and computationally\, with some tutorials on how the mathematics and engineering of invariant representations in machines and algorithms might serve as useful null models. \nIn addition to talks\, there will be a set of tutorial talks on the mathematical description of invariance (P.J. Olver)\, the computer vision aspects of invariant algorithms (S. Soatto)\, and the neuroscientific and cognitive aspects of invariance (TBA). The workshop will be held in room G10 of the CMSA\, located at 20 Garden Street\, Cambridge\, MA. This workshop is organized by L. Mahadevan (Harvard)\, Talia Konkle (Harvard)\, Samuel Gershman (Harvard)\, and Vivek Jayaraman (HHMI). \nVideos\nTentative Speaker List: \n\nAlessandro Achille\, UCLA\nVijay Balasubramanian\, University of Pennsylvania\nJeannette Bohg\, Stanford\nEd Connor\, Johns Hopkins\nMoira Dillon\, NYU\nJacob Feldman\, Rutgers\nIla Fiete\, MIT\nSam Gershman\, Harvard\nGily Ginosar\, Weizmann Institute of Science\nLucia Jacobs\, UC Berkeley\nVivek Jayaraman\, HHMI\nTalia Konkle\, Harvard\nL. Mahadevan\, Harvard\nMichael McCloskey\, Johns Hopkins\nSam Ocko\, Stanford\nPeter Olver\, University of Minnesota\nAnitha Pasupathy\, University of Washington\nSandro Romani\, Janelia\nStefano Soatto\, UCLA\nTatyana Sharpee\,  Salk Institute\nDagmar Sternad\, Northeastern\nElizabeth Torres\, Rutgers\n\nSchedule:\nMonday\, April 15 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n8:30 – 9:00am\nBreakfast\n\n\n\n9:00 – 9:15am\nWelcome and Introduction\n\n\n\n9:15 – 10:00am\nVivek Jayaraman\nTitle: Insect cognition: Small tales of geometry & invariance \nAbstract: Decades of field and laboratory experiments have allowed ethologists to discover the remarkable sophistication of insect behavior. Over the past couple of decades\, physiologists have been able to peek under the hood to uncover sophistication in insect brain dynamics as well. In my talk\, I will describe phenomena that relate to the workshop’s theme of geometry and invariance. I will outline how studying insects —and flies in particular— may enable an understanding of the neural mechanisms underlying these intriguing phenomena.\n\n\n10:00 – 10:45am\nElizabeth Torres\nTitle: Connecting Cognition and Biophysical Motions Through Geometric Invariants and Motion Variability \nAbstract: In the 1930s Nikolai Bernstein defined the degrees of freedom (DoF) problem. He asked how the brain could control abundant DoF and produce consistent solutions\, when the internal space of bodily configurations had much higher dimensions than the space defining the purpose(s) of our actions. His question opened two fundamental problems in the field of motor control. One relates to the uniqueness or consistency of a solution to the DoF problem\, while the other refers to the characterization of the diverse patterns of variability that such solution produces. \nIn this talk I present a general geometric solution to Bernstein’s DoF problem and provide empirical evidence for symmetries and invariances that this solution provides during the coordination of complex naturalistic actions. I further introduce fundamentally different patterns of variability that emerge in deliberate vs. spontaneous movements discovered in my lab while studying athletes and dancers performing interactive actions. I here reformulate the DoF problem from the standpoint of the social brain and recast it considering graph theory and network connectivity analyses amenable to study one of the most poignant developmental disorders of our times: Autism Spectrum Disorders. \nI offer a new unifying framework to recast dynamic and complex cognitive and social behaviors of the full organism and to characterize biophysical motion patterns during migration of induced pluripotent stem cell colonies on their way to become neurons.\n\n\n10:45 – 11:15am\nCoffee Break\n\n\n\n11:15 – 12:00pm\nPeter Olver\nTitle: Symmetry and invariance in cognition — a mathematical perspective” \nAbstract: Symmetry recognition and appreciation is fundamental in human cognition.  (It is worth speculating as to why this may be so\, but that is not my intent.) The goal of these two talks is to survey old and new mathematical perspectives on symmetry and invariance.  Applications will arise from art\, computer vision\, geometry\, and beyond\, and will include recent work on 2D and 3D jigsaw puzzle assembly and an ongoing collaboration with anthropologists on the analysis and refitting of broken bones.  Mathematical prerequisites will be kept to a bare minimum.\n\n\n12:00 – 12:45pm\nStefano Soatto/Alessandro Achille\nTitle: Information in the Weights and Emergent Properties of Deep Neural Networks \nAbstract: We introduce the notion of information contained in the weights of a Deep Neural Network  and show that it can be used to control and describe the training process of DNNs\, and can explain how properties\, such as invariance to nuisance variability and disentanglement\, emerge naturally in the learned representation. Through its dynamics\, stochastic gradient descent (SGD) implicitly regularizes the information in the weights\, which can then be used to bound the generalization error through the PAC-Bayes bound. Moreover\, the information in the weights can be used to defined both a topology and an asymmetric distance in the space of tasks\, which can then be used to predict the training time and the performance on a new task given a solution to a pre-training task. \nWhile this information distance models difficulty of transfer in first approximation\, we show the existence of non-trivial irreversible dynamics during the initial transient phase of convergence when the network is acquiring information\, which makes the approximation fail. This is closely related to critical learning periods in biology\, and suggests that studying the initial convergence transient can yield important insight beyond those that can be gleaned from the well-studied asymptotics.\n\n\n12:45 – 2:00pm\nLunch\n\n\n\n2:00 – 2:45pm\nAnitha Pasupathy\nTitle: Invariant and non-invariant representations in mid-level ventral visual cortex \nMy laboratory investigates how visual form is encoded in area V4\, a critical mid-level stage of form processing in the macaque monkey. Our goal is to reveal how V4 representations underlie our ability to segment visual scenes and recognize objects. In my talk I will present results from two experiments that highlight the different strategies used by the visual to achieve these goals. First\, most V4 neurons exhibit form tuning that is exquisitely invariant to size and position\, properties likely important to support invariant object recognition. On the other hand\, form tuning in a majority of neurons is also highly dependent on the interior fill. Interestingly\, unlike primate V4 neurons\, units in a convolutional neural network trained to recognize objects (AlexNet) overwhelmingly exhibit fill-outline invariance. I will argue that this divergence between real and artificial circuits reflects the importance of local contrast in parsing visual scenes and overall scene understanding.\n\n\n2:45 – 3:30pm\nJacob Feldman\nTitle: Bayesian skeleton estimation for shape representation and perceptual organization \nAbstract: In this talk I will briefly summarize a framework in which shape representation and perceptual organization are reframed as probabilistic estimation problems. The approach centers around the goal of identifying the skeletal model that best “explains” a given shape. A Bayesian solution to this problem requires identifying a prior over shape skeletons\, which penalizes complexity\, and a likelihood model\, which quantifies how well any particular skeleton model fits the data observed in the image. The maximum-posterior skeletal model thus constitutes the most “rational” interpretation of the image data consistent with the given assumptions. This approach can easily be extended and generalized in a number of ways\, allowing a number of traditional problems in perceptual organization to be “probabilized.” I will briefly illustrate several such extensions\, including (1) figure/ground and grouping (3) 3D shape and (2) shape similarity.\n\n\n3:30 – 4:00pm\nTea Break\n\n\n\n4:00 – 4:45pm\nMoira Dillon\nTitle: Euclid’s Random Walk: Simulation as a tool for geometric reasoning through development \nAbstract: Formal geometry lies at the foundation of millennia of human achievement in domains such as mathematics\, science\, and art. While formal geometry’s propositions rely on abstract entities like dimensionless points and infinitely long lines\, the points and lines of our everyday world all have dimension and are finite. How\, then\, do we get to abstract geometric thought? In this talk\, I will provide evidence that evolutionarily ancient and developmentally precocious sensitivities to the geometry of our everyday world form the foundation of\, but also limit\, our mathematical reasoning. I will also suggest that successful geometric reasoning may emerge through development when children abandon incorrect\, axiomatic-based strategies and come to rely on dynamic simulations of physical entities. While problems in geometry may seem answerable by immediate inference or by deductive proof\, human geometric reasoning may instead rely on noisy\, dynamic simulations.\n\n\n4:45 – 5:30pm\nMichael McCloskey\nTitle: Axes and Coordinate Systems in Representing Object Shape and Orientation \nAbstract: I describe a theoretical perspective in which a) object shape is represented in an object-centered reference frame constructed around orthogonal axes; and b) object orientation is represented by mapping the object-centered frame onto an extrinsic (egocentric or environment-centered) frame.  I first show that this perspective is motivated by\, and sheds light on\, object orientation errors observed in neurotypical children and adults\, and in a remarkable case of impaired orientation perception. I then suggest that orientation errors can be used to address questions concerning how object axes are defined on the basis of object geometry—for example\, what aspects of object geometry (e.g.\, elongation\, symmetry\, structural centrality of parts) play a role in defining an object principal axis?\n\n\n5:30 – 6:30pm\nReception\n\n\n\n\n \nTuesday\, April 16 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n8:30 – 9:00am\nBreakfast\n\n\n\n9:00 – 9:45am\nPeter Olver\nTitle: Symmetry and invariance in cognition — a mathematical perspective” \nAbstract: Symmetry recognition and appreciation is fundamental in human cognition.  (It is worth speculating as to why this may be so\, but that is not my intent.) The goal of these two talks is to survey old and new mathematical perspectives on symmetry and invariance.  Applications will arise from art\, computer vision\, geometry\, and beyond\, and will include recent work on 2D and 3D jigsaw puzzle assembly and an ongoing collaboration with anthropologists on the analysis and refitting of broken bones.  Mathematical pre\n\n\n9:45 – 10:30am\nStefano Soatto/Alessandro Achille\nTitle: Information in the Weights and Emergent Properties of Deep Neural Networks \nAbstract: We introduce the notion of information contained in the weights of a Deep Neural Network  and show that it can be used to control and describe the training process of DNNs\, and can explain how properties\, such as invariance to nuisance variability and disentanglement\, emerge naturally in the learned representation. Through its dynamics\, stochastic gradient descent (SGD) implicitly regularizes the information in the weights\, which can then be used to bound the generalization error through the PAC-Bayes bound. Moreover\, the information in the weights can be used to defined both a topology and an asymmetric distance in the space of tasks\, which can then be used to predict the training time and the performance on a new task given a solution to a pre-training task. \nWhile this information distance models difficulty of transfer in first approximation\, we show the existence of non-trivial irreversible dynamics during the initial transient phase of convergence when the network is acquiring information\, which makes the approximation fail. This is closely related to critical learning periods in biology\, and suggests that studying the initial convergence transient can yield important insight beyond those that can be gleaned from the well-studied asymptotics.\n\n\n10:30 – 11:00am\nCoffee Break\n\n\n\n11:00 – 11:45am\nJeannette Bohg\nTitle: On perceptual representations and how they interact with actions and physical representations \nAbstract: I will discuss the hypothesis that perception is active and shaped by our task and our expectations on how the world behaves upon physical interaction. Recent approaches in robotics follow this insight that perception is facilitated by physical interaction with the environment. First\, interaction creates a rich sensory signal that would otherwise not be present. And second\, knowledge of the regularity in the combined space of sensory data and action parameters facilitate the prediction and interpretation of the signal. In this talk\, I will present two examples from our previous work where a predictive task facilitates autonomous robot manipulation by biasing the representation of the raw sensory data. I will present results on visual but also haptic data.\n\n\n11:45 – 12:30pm\nDagmar Sternad\nTitle: Exploiting the Geometry of the Solution Space to Reduce Sensitivity to Neuromotor Noise \nAbstract: Control and coordination of skilled action is frequently examined in isolation as a neuromuscular problem. However\, goal-directed actions are guided by information that creates solutions that are defined as a relation between the actor and the environment. We have developed a task-dynamic approach that starts with a physical model of the task and mathematical analysis of the solution spaces for the task. Based on this analysis we can trace how humans develop strategies that meet complex demands by exploiting the geometry of the solution space. Using three interactive tasks – throwing or bouncing a ball and transporting a “cup of coffee” – we show that humans develop skill by: 1) finding noise-tolerant strategies and channeling noise into task-irrelevant dimensions\, 2) exploiting solutions with dynamic stability\, and 3) optimizing predictability of the object dynamics. These findings are the basis for developing propositions about the controller: complex actions are generated with dynamic primitives\, attractors with few invariant types that overcome substantial delays and noise in the neuro-mechanical system.\n\n\n12:30 – 2:00pm\nLunch\n\n\n\n2:00 – 2:45pm\nSam Ocko\nTitle: Emergent Elasticity in the Neural Code for Space \nAbstract: To navigate a novel environment\, animals must construct an internal map of space by combining information from two distinct sources: self-motion cues and sensory perception of landmarks. How do known aspects of neural circuit dynamics and synaptic plasticity conspire to construct such internal maps\, and how are these maps used to maintain representations of an animal’s position within an environment. We demonstrate analytically how a neural attractor model that combines path integration of self-motion with Hebbian plasticity in synaptic weights from landmark cells can self-organize a consistent internal map of space as the animal explores an environment. Intriguingly\, the emergence of this map can be understood as an elastic relaxation process between landmark cells mediated by the attractor network during exploration. Moreover\, we verify several experimentally testable predictions of our model\, including: (1) systematic deformations of grid cells in irregular environments\, (2) path-dependent shifts in grid cells towards the most recently encountered landmark\, (3) a dynamical phase transition in which grid cells can break free of landmarks in altered virtual reality environments and (4) the creation of topological defects in grid cells. Taken together\, our results conceptually link known biophysical aspects of neurons and synapses to an emergent solution of a fundamental computational problem in navigation\, while providing a unified account of disparate experimental observations.\n\n\n2:45 – 3:30pm\nTatyana Sharpee\nTitle: Hyperbolic geometry of the olfactory space \nAbstract: The sense of smell can be used to avoid poisons or estimate a food’s nutrition content because biochemical reactions create many by-products. Thus\, the production of a specific poison by a plant or bacteria will be accompanied by the emission of certain sets of volatile compounds. An animal can therefore judge the presence of poisons in the food by how the food smells. This perspective suggests that the nervous system can classify odors based on statistics of their co-occurrence within natural mixtures rather than from the chemical structures of the ligands themselves. We show that this statistical perspective makes it possible to map odors to points in a hyperbolic space. Hyperbolic coordinates have a long but often underappreciated history of relevance to biology. For example\, these coordinates approximate distance between species computed along dendrograms\, and more generally between points within hierarchical tree-like networks. We find that both natural odors and human perceptual descriptions of smells can be described using a three-dimensional hyperbolic space. This match in geometries can avoid distortions that would otherwise arise when mapping odors to perception. We identify three axes in the perceptual space that are aligned with odor pleasantness\, its molecular boiling point and acidity. Because the perceptual space is curved\, one can predict odor pleasantness by knowing the coordinates along the molecular boiling point and acidity axes.\n\n\n3:30 – 4:00pm\nTea Break\n\n\n\n4:00 – 4:45pm\nEd Connor\nTitle: Representation of solid geometry in object vision cortex \nAbstract: There is a fundamental tension in object vision between the 2D nature of retinal images and the 3D nature of physical reality. Studies of object processing in the ventral pathway of primate visual cortex have focused mainly on 2D image information. Our latest results\, however\, show that representations of 3D geometry predominate even in V4\, the first object-specific stage in the ventral pathway. The majority of V4 neurons exhibit strong responses and clear selectivity for solid\, 3D shape fragments. These responses are remarkably invariant across radically different image cues for 3D shape: shading\, specularity\, reflection\, refraction\, and binocular disparity (stereopsis). In V4 and in subsequent stages of the ventral pathway\, solid shape geometry is represented in terms of surface fragments and medial axis fragments. Whole objects are represented by ensembles of neurons signaling the shapes and relative positions of their constituent parts. The neural tuning dimensionality of these representations includes principal surface curvatures and their orientations\, surface normal orientation\, medial axis orientation\, axial curvature\, axial topology\, and position relative to object center of mass. Thus\, the ventral pathway implements a rapid transformation of 2D image data into explicit representations 3D geometry\, providing cognitive access to the detailed structure of physical reality.\n\n\n4:45 – 5:30pm\nL. Mahadevan\nTitle: Simple aspects of geometry and probability in perception \nAbstract: Inspired by problems associated with noisy perception\, I will discuss two questions: (i) how might we test people’s perception of probability in a geometric context ? (ii) can one construct invariant descriptions of 2D images using simple notions of probabilistic geometry? Along the way\, I will highlight other questions that the intertwining of geometry and probability raises in a broader perceptual context.\n\n\n\n\nWednesday\, April 17 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n8:30 – 9:00am\nBreakfast\n\n\n\n9:00 – 9:45am\nGily Ginosar\nTitle: The 3D geometry of grid cells in flying bats \nAbstract: The medial entorhinal cortex (MEC) contains a variety of spatial cells\, including grid cells and border cells. In 2D\, grid cells fire when the animal passes near the vertices of a 2D spatial lattice (or grid)\, which is characterized by circular firing-fields separated by fixed distances\, and 60 local angles – resulting in a hexagonal structure. Although many animals navigate in 3D space\, no studies have examined the 3D volumetric firing of MEC neurons. Here we addressed this by training Egyptian fruit bats to fly in a large room (5.84.62.7m)\, while we wirelessly recorded single neurons in MEC. We found 3D border cells and 3D head-direction cells\, as well as many neurons with multiple spherical firing-fields. 20% of the multi-field neurons were 3D grid cells\, exhibiting a narrow distribution of characteristic distances between neighboring fields – but not a perfect 3D global lattice. The 3D grid cells formed a functional continuum with less structured multi-field neurons. Both 3D grid cells and multi-field cells exhibited an anatomical gradient of spatial scale along the dorso-ventral axis of MEC\, with inter-field spacing increasing ventrally – similar to 2D grid cells in rodents. We modeled 3D grid cells and multi-field cells as emerging from pairwise-interactions between fields\, using an energy potential that induces repulsion at short distances and attraction at long distances. Our analysis shows that the model explains the data significantly better than a random arrangement of fields. Interestingly\, simulating the exact same model in 2D yielded a hexagonal-like structure\, akin to grid cells in rodents. Together\, the experimental data and preliminary modeling suggest that the global property of grid cells is multiple fields that repel each other with a characteristic distance-scale between adjacent fields – which in 2D yields a global hexagonal lattice while in 3D yields only local structure but no global lattice. \nGily Ginosar 1 \, Johnatan Aljadeff 2 \, Yoram Burak 3 \, Haim Sompolinsky 3 \, Liora Las 1 \, Nachum Ulanovsky 1 \n(1) Department of Neurobiology\, Weizmann Institute of Science\, Rehovot 76100\, Israel \n(2) Department of Bioengineering\, Imperial College London\, London\, SW7 2AZ\, UK \n(3) The Edmond and Lily Safra Center for Brain Sciences\, and Racah Institute of Physics\, The Hebrew \nUniversity of Jerusalem\, Jerusalem\, 91904\, Israel\n\n\n9:45 – 10:30am\nSandro Romani\nTitle: Neural networks for 3D rotations \nAbstract: Studies in rodents\, bats\, and humans have uncovered the existence of neurons that encode the orientation of the head in 3D. Classical theories of the head-direction (HD) system in 2D rely on continuous attractor neural networks\, where neurons with similar heading preference excite each other\, while inhibiting other HD neurons. Local excitation and long-range inhibition promote the formation of a stable “bump” of activity that maintains a representation of heading. The extension of HD models to 3D is hindered by complications (i) 3D rotations are non-commutative (ii) the space described by all possible rotations of an object has a non-trivial topology. This topology is not captured by standard parametrizations such as Euler angles (e.g. yaw\, pitch\, roll). For instance\, with these parametrizations\, a small change of the orientation of the head could result in a dramatic change of neural representation. We used methods from the representation theory of groups to develop neural network models that exhibit patterns of persistent activity of neurons mapped continuously to the group of 3D rotations. I will further discuss how these networks can (i) integrate vestibular inputs to update the representation of heading\, and (ii) be used to interpret “mental rotation” experiments in humans. \nThis is joint work with Hervé Rouault (CENTURI) and Alon Rubin (Weizmann Institute of Science).\n\n\n10:30 – 11:00am\nCoffee Break\n\n\n\n11:00 – 11:45am\nSam Gershman\nTitle: The hippocampus as a predictive map \nAbstract: A cognitive map has long been the dominant metaphor for hippocampal function\, embracing the idea that place cells encode a geometric representation of space. However\, evidence for predictive coding\, reward sensitivity and policy dependence in place cells suggests that the representation is not purely spatial. I approach this puzzle from a reinforcement learning perspective: what kind of spatial representation is most useful for maximizing future reward? I show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. Furthermore\, I argue that entorhinal grid cells encode a low-dimensionality basis set for the predictive representation\, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.\n\n\n11:45 – 12:30pm\nLucia Jacobs\nTitle: The adaptive geometry of a chemosensor: the origin and function of the vertebrate nose \nAbstract: A defining feature of a living organism\, from prokaryotes to plants and animals\, is the ability to orient to chemicals. The distribution of chemicals\, whether in water\, air or on land\, is used by organisms to locate and exploit spatially distributed resources\, such as nutrients and reproductive partners. In animals\, the evolution of a nervous system coincided with the evolution of paired chemosensors. In contemporary insects\, crustaceans\, mollusks and vertebrates\, including humans\, paired chemosensors confer a stereo olfaction advantage on the animal’s ability to orient in space. Among vertebrates\, however\, this function faced a new challenge with the invasion of land. Locomotion on land created a new conflict between respiration and spatial olfaction in vertebrates. The need to resolve this conflict could explain the current diversity of vertebrate nose geometries\, which could have arisen due to species differences in the demand for stereo olfaction. I will examine this idea in more detail in the order Primates\, focusing on Old World primates\, in particular\, the evolution of an external nose in the genus Homo.\n\n\n12:30 – 1:30pm\nLunch\n\n\n\n1:30 – 2:15pm\nTalia Konkle\nTitle: The shape of things and the organization of object-selective cortex \nAbstract: When we look at the world\, we effortlessly recognize the objects around us and can bring to mind a wealth of knowledge about their properties. In part 1\, I’ll present evidence that neural responses to objects are organized by high-level dimensions of animacy and size\, but with underlying neural tuning to mid-level shape features. In part 2\, I’ll present evidence that representational structure across much of the visual system has the requisite structure to predict visual behavior. Together\, these projects suggest that there is a ubiquitous “shape space” mapped across all of occipitotemporal cortex that underlies our visual object processing capacities. Based on these findings\, I’ll speculate that the large-scale spatial topography of these neural responses is critical for pulling explicit content out of a representational geometry.\n\n\n2:15 – 3:00pm\nVijay Balasubramanian\nTitle: Becoming what you smell: adaptive sensing in the olfactory system \nAbstract: I will argue that the circuit architecture of the early olfactory system provides an adaptive\, efficient mechanism for compressing the vast space of odor mixtures into the responses of a small number of sensors.  In this view\, the olfactory sensory repertoire employs a disordered code to compress a high dimensional olfactory space into a low dimensional receptor response space while preserving distance relations between odors.  The resulting representation is dynamically adapted to efficiently encode the changing environment of volatile molecules.  I will show that this adaptive combinatorial code can be efficiently decoded by systematically eliminating candidate odorants that bind to silent receptors.  The resulting algorithm for “estimation by elimination” can be implemented by a neural network that is remarkably similar to the early olfactory pathway in the brain.  The theory predicts a relation between the diversity of olfactory receptors and the sparsity of their responses that matches animals from flies to humans.   It also predicts specific deficits in olfactory behavior that should result from optogenetic manipulation of the olfactory bulb.\n\n\n3:00 – 3:45pm\nIla Feite\nTitle: Invariance\, stability\, geometry\, and flexibility in spatial navigation circuits \nAbstract: I will describe how the geometric invariances or symmetries of the external world are reflected in the symmetries of neural circuits that represent it\, using the example of the brain’s networks for spatial navigation. I will discuss how these symmetries enable spatial memory\, evidence integration\, and robust representation. At the same time\, I will discuss how these seemingly rigid circuits with their inscribed symmetries can be harnessed to represent a range of spatial and non-spatial cognitive variables with high flexibility.\n\n\n3:45 – 4:00pm\nL Mahadevan – summary
URL:https://live-hu-cmsa-222.pantheonsite.io/event/workshop-on-invariance-and-geometry-in-sensation-action-and-cognition/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190409T160000
DTEND;TZID=America/New_York:20190409T170000
DTSTAMP:20250328T150617Z
CREATED:20240212T100146Z
LAST-MODIFIED:20250328T150617Z
UID:10001950-1554825600-1554829200@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Math Science Lectures in Honor of Raoul Bott: Mina Aganagic
DESCRIPTION:On April 9 and 10\, 2019 the CMSA hosted two lectures by Mina Aganagic (UC Berkeley).  This was the second annual Math Science Lecture Series held in honor of Raoul Bott. \nThe lectures took place in Science Center\, Hall C \n“Two math lessons from string theory”\n\n\n\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n \n \nApril 9\, 2019 \nLecture 1 \nTitle: “Lesson on Integrability” \nAbstract: The quantum Knizhnik-Zamolodchikov (qKZ) equation is a difference generalization of the famous Knizhnik-Zamolodchikov (KZ) equation. The problem to explicitly capture the monodromy of the qKZ equation has been open for over 25 years. I will describe the solution to this problem\, discovered jointly with Andrei Okounkov. The solution comes from the geometry of Nakajima quiver varieties and has a string theory origin. \nPart of the interest in the qKZ monodromy problem is that its solution leads to integrable lattice models\, in parallel to how monodromy matrices of the KZ equation lead to knot invariants. Thus\, our solution of the problem leads to a new\, geometric approach\, to integrable lattice models. There are two other approaches to integrable lattice models\, due to Nekrasov and Shatashvili and to Costello\, Witten and Yamazaki. I’ll describe joint work with Nikita Nekrasov which explains how string theory unifies the three approaches to integrable lattice models.\n\n\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n \n \nApril 10\, 2019 \nLecture 2 \nTitle: “Lesson on Knot Categorification” \nAbstract: An old problem is to find a unified approach to the knot categorification problem. The new string theory perspective on the qKZ equation I described in the first talk can be used to derive two geometric approaches to the problem. \nThe first approach is based on a category of B-type branes on resolutions of slices in affine Grassmannians. The second is based on a category of A-branes in a Landau-Ginzburg theory. The relation between them is two dimensional (equivariant) mirror symmetry. String theory also predicts that a third approach to categorification\, based on counting solutions to five dimensional Haydys-Witten equations\, is equivalent to the first two. \nThis talk is mostly based on joint work with Andrei Okounkov.\n\n\n\n  \n  \n 
URL:https://live-hu-cmsa-222.pantheonsite.io/event/math-science-lectures-in-honor-of-raoul-bott-mina-aganagic/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Event,Math Science Lectures in Honor of Raoul Bott,Public Lecture,Special Lectures
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Aganagic-791x1024-1-232x300-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190327T090000
DTEND;TZID=America/New_York:20190329T160000
DTSTAMP:20250304T213810Z
CREATED:20230715T172858Z
LAST-MODIFIED:20250304T213810Z
UID:10000110-1553677200-1553875200@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Machine Learning for Multiscale Model Reduction Workshop
DESCRIPTION:The Machine Learning for Multiscale Model Reduction Workshop will take place on March 27-29\, 2019. This is the second of two workshops organized by Michael Brenner\, Shmuel Rubinstein\, and Tom Hou.  The first\, Fluid turbulence and Singularities of the Euler/ Navier Stokes equations\, will take place on March 13-15\, 2019. Both workshops will be held in room G10 of the CMSA\, located at 20 Garden Street\, Cambridge\, MA.  \n  \nSpeakers:\n\nJoan Bruna\, Courant Institute\nPredrag Cvitanovic\, Georgia Tech\nStephan Hoyer\, Google Research\nDe Huang\, Caltech\nGeorge Karniadakis\, Brown University\nRichard Kerswell\, Cambridge University\nStephane Mallat\, ENS\nStanley Osher\, UCLA\nJacob Page\, Cambridge University\nHouman Owhadi\, Caltech\nZuowei Shen\, National University of Singapore\nJack Xin\, UC Irvine\nJinchao Xu\, Penn State University\nLexing Ying\, Stanford University and Facebook AI Research\nPengchuan Zhang\, Microsoft Research
URL:https://live-hu-cmsa-222.pantheonsite.io/event/machine-learning-for-multiscale-model-reduction-workshop/
LOCATION:Virtual
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Machine-Learning-Poster.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190318T090000
DTEND;TZID=America/New_York:20190320T170000
DTSTAMP:20250304T213630Z
CREATED:20230715T091111Z
LAST-MODIFIED:20250304T213630Z
UID:10000109-1552899600-1553101200@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Workshop on Mirror Symmetry and Stability
DESCRIPTION:This three-day workshop will take place at Harvard University on March 18-20\, 2019 in Science Center room 507. The main topic will be stability conditions in homological mirror symmetry. This workshop is funded by the Simons Collaboration in Homological Mirror Symmetry. \nOrganizers: Denis Auroux\, Yu-Wei Fan\, Hansol Hong\, Siu-Cheong Lau\, Bong Lian\, Shing-Tung Yau\, Jingyu Zhao \nSpeakers: \nDylan Allegretti (Sheffield)\nTristan Collins (MIT)\nNaoki Koseki (Tokyo)\nChunyi Li (Warwick)\nJason Lo (CSU Northridge)\nEmanuele Macrì (NEU & IHES)\nGenki Ouchi (Riken iTHEMS)\nPranav Pandit (ICTS)\nLaura Pertusi (Edinburgh)\nJacopo Stoppa (SISSA)\nAlex Takeda (UC Berkeley)\nXiaolei Zhao (UC Santa Barbara) \nMore details will be added later. \nVisit the event page for more information.  \n  \n 
URL:https://live-hu-cmsa-222.pantheonsite.io/event/workshop-on-mirror-symmetry-and-stability/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/HMS-2019-1-768x994-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190313T090000
DTEND;TZID=America/New_York:20190315T170000
DTSTAMP:20250305T192752Z
CREATED:20230717T174351Z
LAST-MODIFIED:20250305T192752Z
UID:10000046-1552467600-1552669200@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Fluid turbulence and Singularities of the Euler/ Navier Stokes equations
DESCRIPTION:The Workshop on Fluid turbulence and Singularities of the Euler/ Navier Stokes equations will take place on March 13-15\, 2019. This is the first of two workshop organized by Michael Brenner\, Shmuel Rubinstein\, and Tom Hou. The second\, Machine Learning for Multiscale Model Reduction\, will take place on March 27-29\, 2019. Both workshops will be held in room G10 of the CMSA\, located at 20 Garden Street\, Cambridge\, MA. \n  \nSpeakers: \n\nClaude Bardos\, University of Paris\nJiajie Chen\, Caltech\nPeter Constantin\, Princeton\nDiego Cordoba\, ICMAT\nTarek Elgindi\, UCSD\nSusumu Goto\, Osaka\nAlexander Kiselev\, Duke University\nAlain Pumir\, ENS Lyon\nShmuel Rubinstein\, Harvard SEAS\nVladimir Sverak\, University of Minnesota\nEdriss S. Titi\, TAMU\nVlad Vicol\, Courant\nSijue Wu\, University of Michigan\nAndrej Zlatos\, UCSD
URL:https://live-hu-cmsa-222.pantheonsite.io/event/fluid-turbulence-and-singularities-of-the-euler-navier-stokes-equations/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Fluid-Turbulence-Poster-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190225T093000
DTEND;TZID=America/New_York:20190301T170000
DTSTAMP:20240209T214453Z
CREATED:20230715T090551Z
LAST-MODIFIED:20240209T214453Z
UID:10000108-1551087000-1551459600@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Growth and zero sets of eigenfunctions and of solutions to elliptic partial differential equations
DESCRIPTION:From February 25 to March 1\, the CMSA will be hosting a workshop on Growth and zero sets of eigenfunctions and of solutions to elliptic partial differential equations.  \nKey participants of this workshop include David Jerison (MIT)\, Alexander Logunov (IAS)\, and Eugenia Malinnikova (IAS).  This workshop will have morning sessions on Monday-Friday of this week from 9:30-11:30am\, and afternoon sessions on Monday\, Tuesday\, and Thursday from 3:00-5:00pm.\nThe sessions will be held in  \(G02\) (downstairs) at 20 Garden\, except for Tuesday afternoon\, when the talk will be in \(G10\).
URL:https://live-hu-cmsa-222.pantheonsite.io/event/growth-and-zero-sets-of-eigenfunctions-and-of-solutions-to-elliptic-partial-differential-equations/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190118T083000
DTEND;TZID=America/New_York:20190121T173000
DTSTAMP:20241212T192232Z
CREATED:20230715T090318Z
LAST-MODIFIED:20241212T192232Z
UID:10000105-1547800200-1548091800@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Geometric Analysis Approach to AI Workshop
DESCRIPTION:Due to inclement weather on Sunday\, the second half of the workshop has been moved forward one day. Sunday and Monday’s talks will now take place on Monday and Tuesday.\nOn January 18-21\, 2019 the Center of Mathematical Sciences and Applications will be hosting a workshop on the Geometric Analysis Approach to AI. \nThis workshop will focus on the theoretic foundations of AI\, especially various methods in Deep Learning. The topics will cover the relationship between deep learning and optimal transportation theory\, DL and information geometry\, DL Learning and information bottle neck and renormalization theory\, DL and manifold embedding and so on. Furthermore\, the recent advancements\, novel methods\, and real world applications of Deep Learning will also be reported and discussed. \nThe workshop will take place from January 18th to January 23rd\, 2019. In the first four days\, from January 18th to January 21\, the speakers will give short courses; On the 22nd and 23rd\, the speakers will give conference representations. This workshop is organized by Xianfeng Gu and Shing-Tung Yau. \nThe workshop will be held in room G10 of the CMSA\, located at 20 Garden Street\, Cambridge\, MA.  \nSpeakers:  \n\nSarah Adel Bargal\, Boston University\nGuy Bresler\, MIT\nTina Eliassi-Rad\, Northeastern\nYun Raymond Fu\, Northeastern\nBrian Kulis\, Boston University\nNa Lei\, Dalian University of Technology\nYi Ma\, UC Berkeley\nMinh Hoai Nguyen\, Stony Brook\nFrancesco Orabona\, Boston University\nCengiz Pehlevan\, Harvard SEAS\nTomaso Poggio\, MIT\nZhiwei Qin\, DiDi Research America\nKate Saenko\, Boston University\nDimitris Samaras\, Stony Brook\nJohannes Schmidt-Hieber\, University of Twente\nSteven Skiena\, Stony Brook\nVivienne Sze\, MIT\nNaftali Tishby\, ICNC\nJiajun Wu\, MIT\nYing Nian Wu\, UCLA\nGangqiang Xia\, Morgan Stanley\nEric Xing\, Carnegie Mellon\nDonghui Yan\, UMass Dartmouth\nAlan Yuille\, Johns Hopkins\nJuhua Zhu\,  Argus
URL:https://live-hu-cmsa-222.pantheonsite.io/event/geometric-analysis-approach-to-ai-workshop/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Geo-Analysis-Poster-final-e1547584167900.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181203T083000
DTEND;TZID=America/New_York:20181205T143000
DTSTAMP:20250305T212541Z
CREATED:20230715T090021Z
LAST-MODIFIED:20250305T212541Z
UID:10000103-1543825800-1544020200@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Morphogenesis: Geometry and Physics
DESCRIPTION:Just over a century ago\, the biologist\, mathematician and philologist D’Arcy Thompson wrote “On growth and form”. The book – a literary masterpiece – is a visionary synthesis of the geometric biology of form. It also served as a call for mathematical and physical approaches to understanding the evolution and development of shape. In the century since its publication\, we have seen a revolution in biology following the discovery of the genetic code\, which has uncovered the molecular and cellular basis for life\, combined with the ability to probe the chemical\, structural\, and dynamical nature of molecules\, cells\, tissues and organs across scales. In parallel\, we have seen a blossoming of our understanding of spatiotemporal patterning in physical systems\, and a gradual unveiling of the complexity of physical form. So\, how far are we from realizing the century-old vision that “Cell and tissue\, shell and bone\, leaf and flower\, are so many portions of matter\, and it is in obedience to the laws of physics that their particles have been moved\, moulded and conformed ?” \nTo address this requires an appreciation of the enormous ‘morphospace’ in terms of the potential shapes and sizes that living forms take\, using the language of mathematics. In parallel\, we need to consider the biological processes that determine form in mathematical terms is based on understanding how instabilities and patterns in physical systems might be harnessed by evolution. \nIn Fall 2018\, CMSA will focus on a program that aims at recent mathematical advances in describing shape using geometry and statistics in a biological context\, while also considering a range of physical theories that can predict biological shape at scales ranging from macromolecular assemblies to whole organ systems.\nThe first workshop will focus on the interface between Morphometrics and Mathematics\, while the second will focus on the interface between Morphogenesis and Physics.The workshop is organized by L. Mahadevan (Harvard)\, O. Pourquie (Harvard)\, A. Srivastava (Florida). \nAs part of the program on Mathematical Biology a workshop on Morphogenesis: Geometry and Physics will take place on December 3-5\, 2018.  The workshop will be held in room G10 of the CMSA\, located at 20 Garden Street\, Cambridge\, MA. \nVideos\nSpeakers:\n\nArkhat Abzhanov\, Imperial College\nYohanns Bellaiche\, Paris\nCheng Ming Chuong\, USC\nZev Gartner\, UCSF\nThomas Gregor\, Princeton\nDagmar Iber\, Zurich\nIan Jermyn\, Durham University\nRaymond Keller\, UVA\nAllon Klein\, HMS\nLisa Manning\, Syracuse\nCristina Marchetti\, UCSB\nSean Megason\, HMS\nElliot Meyerowitz\, Caltech\nMichel Milinkovitch\, Geneva\nLeonardo Morsut\, USC\nOlivier Pourquié\, HMS\nEric Siggia\, Rockefeller University\nBen Simons\, Cambridge\nSebastian Streichan\, UCSB\nAryeh Warmflash\, Rice
URL:https://live-hu-cmsa-222.pantheonsite.io/event/morphogenesis-geometry-and-physics/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Programs
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181116T080000
DTEND;TZID=America/New_York:20181117T170000
DTSTAMP:20241212T191652Z
CREATED:20230715T085736Z
LAST-MODIFIED:20241212T191652Z
UID:10000102-1542355200-1542474000@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Current Developments In Mathematics 2018
DESCRIPTION:Current Developments in Mathematics 2018 Conference. \nFriday\, Nov. 16\, 2018 2:15 pm – 6:00 pm \nSaturday\, Nov. 17\, 2018  9:00 am – 5:00 pm \nHarvard University Science Center\, Hall B \nYoutube Playlist
URL:https://live-hu-cmsa-222.pantheonsite.io/event/current-developments-in-mathematics-2018/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/jpeg:https://live-hu-cmsa-222.pantheonsite.io/media/cdm-2018-poster.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181024T150000
DTEND;TZID=America/New_York:20181024T160000
DTSTAMP:20250328T150854Z
CREATED:20230715T085247Z
LAST-MODIFIED:20250328T150854Z
UID:10000101-1540393200-1540396800@live-hu-cmsa-222.pantheonsite.io
SUMMARY:2018 Ding Shum Lecture
DESCRIPTION:  \n \nOn October 24\, 2018\, the CMSA hosted the second annual Ding Shum lecture. This event was made possible by the generous funding of Ding Lei and Harry Shum. Last year featured Leslie Valiant\, who spoke on “learning as a Theory of Everything.” \nThis year will feature Eric Maskin\, who will speak on “How to Improve Presidential Elections: the Mathematics of Voting.” This lecture will take place from 5:00-6:00pm in Science Center\, Hall D.  \nPictures of the event can be found here.
URL:https://live-hu-cmsa-222.pantheonsite.io/event/2018-ding-shum-lecture/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Ding Shum Lecture,Event,Public Lecture,Special Lectures
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Ding-Shum-lecture-2018.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181022T083000
DTEND;TZID=America/New_York:20181024T140000
DTSTAMP:20250305T212456Z
CREATED:20230715T084844Z
LAST-MODIFIED:20250305T212456Z
UID:10000099-1540197000-1540389600@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Workshop on Morphometrics\, Morphogenesis and Mathematics
DESCRIPTION:In Fall 2018\, the CMSA will host a Program on Mathematical Biology\, which aims to describe recent mathematical advances in using geometry and statistics in a biological context\, while also considering a range of physical theories that can predict biological shape at scales ranging from macromolecular assemblies to whole organ systems. \nThe plethora of natural shapes that surround us at every scale is both bewildering and astounding – from the electron micrograph of a polyhedral virus\, to the branching pattern of a gnarled tree to the convolutions in the brain. Even at the human scale\, the   shapes seen in a garden at the scale of a pollen grain\, a seed\, a sapling\, a root\, a flower or leaf are so numerous that “it is enough to drive the sanest man mad\,” wrote Darwin. Can we classify these shapes and understand their origins quantitatively? \nIn biology\, there is growing interest in and ability to quantify growth and form in the context of the size and shape of bacteria and other protists\, to understand how polymeric assemblies grow and shrink (in the cytoskeleton)\, and how cells divide\, change size and shape\, and move to organize tissues\, change their topology and geometry\, and link multiple scales and connect biochemical to mechanical aspects of these problems\, all in a self-regulated setting. \nTo understand these questions\, we need to describe shape (biomathematics)\, predict shape (biophysics)\, and design shape (bioengineering). \nFor example\, in mathematics there are some beautiful links to Nash’s embedding theorem\,  connections to quasi-conformal geometry\, Ricci flows and geometric PDE\, to Gromov’s h principle\, to geometrical singularities and singular geometries\, discrete and computational differential geometry\, to stochastic geometry and shape characterization (a la Grenander\, Mumford etc.). A nice question here is to use the large datasets (in 4D) and analyze them using ideas from statistical geometry (a la Taylor\, Adler) to look for similarities and differences across species during development\, and across evolution. \nIn physics\, there are questions of generalizing classical theories to include activity\, break the usual Galilean invariance\, as well as isotropy\, frame indifference\, homogeneity\, and create both agent (cell)-based and continuum theories for ordered\, active machines\, linking statistical to continuum mechanics\, and understanding the instabilities and patterns that arise. Active generalizations of liquid crystals\, polar materials\, polymers etc. are only just beginning to be explored and there are some nice physical analogs of biological growth/form that are yet to be studied. \nThe CMSA will be hosting a Workshop on Morphometrics\, Morphogenesis and Mathematics from October 22-24\, 2018 at the Center of Mathematical Sciences and Applications\, located at 20 Garden Street\, Cambridge\, MA. \nThe workshop is organized by L. Mahadevan (Harvard)\, O. Pourquie (Harvard)\, A. Srivastava (Florida). \nVideos of the talks\nConfirmed Speakers:\n\nArkhat Abzhanov\, Imperial College\nSiobhan Braybrook\, UCLA\nCassandra Extavour\, Harvard\nAnjali Goswami\, University College London\nDavid Gu\, Stony Brook\nJukka Jernvall\, Helsinki\nEric Klassen\, Florida State\nSayan Mukherjee\, Duke\nPeter Olver\, U Minnesota\nNipam Patel\, Berkeley\nStephanie Pierce\, Harvard\nKaren Sears\, UCLA\nAlain Trouve\, ENS-Cachan\, France\nLaurent Younes\, Johns Hopkins
URL:https://live-hu-cmsa-222.pantheonsite.io/event/workshop-on-morphometrics-morphogenesis-and-mathematics/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Programs,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180929T083000
DTEND;TZID=America/New_York:20180930T150000
DTSTAMP:20250328T145116Z
CREATED:20230715T084506Z
LAST-MODIFIED:20250328T145116Z
UID:10000090-1538209800-1538319600@live-hu-cmsa-222.pantheonsite.io
SUMMARY:F-Theory Workshop
DESCRIPTION:The CMSA hosted an F-Theory workshop September 29-30\, 2018. The workshop was held in room G10 of the CMSA\, located at 20 Garden Street\, Cambridge\, MA. \nYoutube Playlist  \nOrganizers: \n\nPaolo Aluffi (Florida State)\nLara B. Anderson (Virginia Tech)\nMboyo Esole (Northeastern)\nShing-Tung Yau (Harvard)\n\nSpeakers: \n\nMirjam Cvetic\, University of Pennsylvania\nTommaso de Fernex\, University of Utah\nJames Gray\, Virginia Tech\nJonathan Heckman\, University of Pennsylvania\nMonica Kang\, Harvard University\nSándor Kovács\, University of Washington\nAnatoly Libgober\, UIC\nMatilde Marcolli\, Caltech\, University of Toronto\, and Perimeter Institute\nWashington Taylor\, MIT\nCumrun Vafa\, Harvard University
URL:https://live-hu-cmsa-222.pantheonsite.io/event/f-theory-conference/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180827T092000
DTEND;TZID=America/New_York:20180828T151500
DTSTAMP:20250305T184118Z
CREATED:20230715T084116Z
LAST-MODIFIED:20250305T184118Z
UID:10000089-1535361600-1535469300@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Kickoff Workshop on Topology and Quantum Phases of Matter
DESCRIPTION:On August 27-28\, 2018\, the CMSA will be hosting a Kickoff workshop on Topology and Quantum Phases of Matter. New ideas rooted in topology have recently had a big impact on condensed matter physics\, and have highlighted new connections with high energy physics\, mathematics and quantum information theory. Additionally\, these ideas have found applications in the design of photonic systems and of materials with novel mechanical properties. The aim of this program will be to deepen these connections by fostering discussion and seeding new collaborations within and across disciplines. \nThis workshop is a part of the CMSA’s program on Program on Topological Aspects of Condensed Matter\,  and will be the first of two workshops\, in addition to a visitor program and seminars. \nThe workshop will be held in room G10 of the CMSA\, located at 20 Garden Street\, Cambridge\, MA. \nSpeakers:  \n\nZhen Bi\, MIT\nMeng Cheng\, Yale\nDima Feldman\, Brown\nDominic Else\, UCSB\nLiang Fu\, MIT\nFabian Grusdt\, Harvard\nYing Fei Gu\, Harvard\nBert Halperin\, Harvard\nAnton Kapustin\, Caltech\nPatrick Lee\, MIT\nL. Mahadevan\, Harvard\nBrad Marston\, Brown\nMax Metlitski\, MIT\nEmil V. Prodan\, Yeshiva\nAchim Rosch\, University of Cologne\nMathias Scheurer\, Harvard\nMarin Soljacic\, MIT\nX. G. Wen\, MIT\nCenke Xu\, UCSB\nFrank Zhang\, Cornell
URL:https://live-hu-cmsa-222.pantheonsite.io/event/kickoff-workshop-on-topology-and-quantum-phases-of-matter/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Topological-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180823T083000
DTEND;TZID=America/New_York:20180824T163000
DTSTAMP:20250415T154139Z
CREATED:20230715T083801Z
LAST-MODIFIED:20250415T154139Z
UID:10000086-1535013000-1535128200@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Big Data Conference 2018
DESCRIPTION:On August 23-24\, 2018 the CMSA hosted the fourth annual Conference on Big Data. The Conference featured speakers from the Harvard community as well as scholars from across the globe\, with talks focusing on computer science\, statistics\, math and physics\, and economics. \nThe talks were held in Science Center Hall B\, 1 Oxford Street. \nSpeakers:  \n\nMohammad Akbarpour\, Stanford\nEmily Breza\, Harvard\nFrancesca Dominici\, Harvard\nChiara Farronato\, Harvard\nKobi Gal\, Ben Gurion\nJonah Kallenbach\, Reverie Labs\nSamuel Kou\, Harvard\nLaura Kreidberg\, Harvard\nDanielle Li\, MIT\nLibby Mishkin\, Uber\nJosh Speagle\, Harvard\nWilliam Stein\, University of Washington\nAlex Teyltelboym\, University of Oxford\nSergiy Verstyuk\, CMSA/Harvard\n\nOrganizers:  \n\nShing-Tung Yau\, William Caspar Graustein Professor of Mathematics\, Harvard University\nScott Duke Kominers\, MBA Class of 1960 Associate Professor\, Harvard Business\nRichard Freeman\, Herbert Ascherman Professor of Economics\, Harvard University\nJun Liu\, Professor of Statistics\, Harvard University\nHorng-Tzer Yau\, Professor of Mathematics\, Harvard University
URL:https://live-hu-cmsa-222.pantheonsite.io/event/2018-big-data-conference-2/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Big Data Conference,Conference,Event
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Big-Data-2018-4.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180818T083000
DTEND;TZID=America/New_York:20180820T172000
DTSTAMP:20250304T213419Z
CREATED:20230715T083526Z
LAST-MODIFIED:20250304T213419Z
UID:10000084-1534581000-1534785600@live-hu-cmsa-222.pantheonsite.io
SUMMARY:From Algebraic Geometry to Vision and AI: A Symposium Celebrating the Mathematical Work of David Mumford
DESCRIPTION:On August 18 and 20\, 2018\, the Center of Mathematic Sciences and Applications and the Harvard University Mathematics Department hosted a conference on From Algebraic Geometry to Vision and AI: A Symposium Celebrating the Mathematical Work of David Mumford. The talks took place in Science Center\, Hall B. \nSaturday\, August 18th:  A day of talks on Vision\, AI and brain sciences \nMonday\, August 20th: a day of talks on Math \nSpeakers: \n\nStuart Geman\, Brown\nJanos Kollar\, Princeton\nTai Sing Lee\, CMU\nEmanuele Macri\, Northeastern\nJitendra Malik\, Berkeley / FAIR\nPeter Michor\, University of Vienna\nMichael Miller\, Johns Hopkins\nAaron Pixton\, MIT\nJayant Shah\, Northeastern\nJosh Tenenbaum\, MIT\nBurt Totaro\, UCLA\nAvi Wigderson\, IAS\nYing Nian Wu\, UCLA\nLaurent Younes\, Johns Hopkins\nSong-Chun Zhu\, UCLA\n\nOrganizers:\n\nChing-Li Chai\, University of Pennsylvania\nDavid Gu\, Stony Brook University\nAmnon Neeman\, Australian National University\nMark Nitzberg\, University of California at Berkeley\nYang Wang\, Hong Kong University of Science and Technology\nShing-Tung Yau\, Harvard University\nSong-Chun Zhu\, University of California\, Los Angeles\n\nPublication: \nPure and Applied Mathematics Quarterly\nSpecial Issue: In Honor of David Mumford\nGuest Editors: Ching-Li Chai\, Amnon Neeman \n 
URL:https://live-hu-cmsa-222.pantheonsite.io/event/from-algebraic-geometry-to-vision-and-ai-a-symposium-celebrating-the-mathematical-work-of-david-mumford/
LOCATION:Common Room\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Mumford-3.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180409T090000
DTEND;TZID=America/New_York:20180413T153000
DTSTAMP:20250305T214334Z
CREATED:20230717T175359Z
LAST-MODIFIED:20250305T214334Z
UID:10000079-1523264400-1523633400@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Workshop on Coding and Information Theory
DESCRIPTION:The workshop on coding and information theory will take place April 9-13\, 2018 at the Center of Mathematical Sciences and Applications\, located at 20 Garden Street\, Cambridge\, MA. \nThis workshop will focus on new developments in coding and information theory that sit at the intersection of combinatorics and complexity\, and will bring together researchers from several communities — coding theory\, information theory\, combinatorics\, and complexity theory — to exchange ideas and form collaborations to attack these problems. \nSquarely in this intersection of combinatorics and complexity\, locally testable/correctable codes and list-decodable codes both have deep connections to (and in some cases\, direct motivation from) complexity theory and pseudorandomness\, and recent progress in these areas has directly exploited and explored connections to combinatorics and graph theory.  One goal of this workshop is to push ahead on these and other topics that are in the purview of the year-long program.  Another goal is to highlight (a subset of) topics in coding and information theory which are especially ripe for collaboration between these communities.  Examples of such topics include polar codes; new results on Reed-Muller codes and their thresholds; coding for distributed storage and for DNA memories; coding for deletions and synchronization errors; storage capacity of graphs; zero-error information theory; bounds on codes using semidefinite programming; tensorization in distributed source and channel coding; and applications of information-theoretic methods in probability and combinatorics.  All these topics have attracted a great deal of recent interest in the coding and information theory communities\, and have rich connections to combinatorics and complexity which could benefit from further exploration and collaboration. \nParticipation: The workshop is open to participation by all interested researchers\, subject to capacity. \nA list of lodging options convenient to the Center can also be found on our recommended lodgings page. \nConfirmed participants include: \n\nEmmanuel Abbe\, Princeton University\nSimeon Ball\, Universitat Politècnica de Catalunya\nBoris Bukh\, Carnegie Mellon University\nMahdi Cheraghchi\, Imperial College London\nSivakanth Gopi\, Princeton University\nElena Grigorescu\, University of Purdue\nHamed Hassani\, University of Pennsylvania\nNavin Kashyap\, Indian Institute of Science\nYoung-Han Kim\, University of California\, San Diego\nSwastik Kopparty\, Rutgers University\nNati Linial\, Hebrew University of Jerusalem\nShachar Lovett\, University of California\, San Diego\nWilliam Martin\, Worcester Polytechnic Institute\nArya Mazumdar\, University of Massachusetts at Amherst\nOr Meir\, University of Haifa\nOlgica Milenkovic\, ECE Illinois\nChandra Nair\, Chinese University of Hong Kong\nYuval Peres\, Microsoft Research\nYury Polyanskiy\, Massachusetts Institute of Technology\nMaxim Raginsky\, University of Illinois at Urbana-Champaign\nSankeerth Rao Karingula\, UC San Diego\nAnkit Singh Rawat\, MIT\nNoga Ron-Zewi\, University of Haifa\nRon Roth\, Israel Institute of Technology\nAtri Rudra\, State University of New York\, Buffalo\nAlex Samorodnitsky\, Hebrew University of Jerusalem\nItzhak Tamo\, Tel Aviv University\nAmnon Ta-Shma\, Tel Aviv University\nHimanshu Tyagi\, Indian Institute of Science\nDavid Zuckerman\, University of Texas at Austin
URL:https://live-hu-cmsa-222.pantheonsite.io/event/workshop-on-coding-and-information-theory/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180405T090000
DTEND;TZID=America/New_York:20180407T170000
DTSTAMP:20250304T212649Z
CREATED:20230717T175058Z
LAST-MODIFIED:20250304T212649Z
UID:10000078-1522918800-1523120400@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Simons Collaboration Workshop\, April 5-7\, 2018
DESCRIPTION:The CMSA will be hosting a three-day Simons Collaboration Workshop on Homological Mirror Symmetry and Hodge Theory on April 5-7\, 2018. The workshop will be held in room G10 of the CMSA\, located at 20 Garden Street\, Cambridge\, MA. \nPlease click here to register for this event.  We have space for up to 30 registrants on a first come\, first serve basis. \nWe may be able to provide some financial support for grad students and postdocs interested in this event.  If you are interested in funding\, please send a letter of support from your mentor to Hansol Hong. \nConfirmed Speakers: \n\nJacob Bourjaily (Niels Bohr Institute)\nMandy Cheung (Havard University)\nTristan Collins (Harvard University)\nYoosik Kim (Boston University)\nYu-Shen Lin (Harvard University)\nCheuk-Yu Mak (Cambridge University)\nYu Pan (MIT)\nMauricio Romo (Tsinghua University)\nShu-Heng Shao (IAS)\nZack Sylvan (Columbia University)\nDmitry Vaintrob (IAS)
URL:https://live-hu-cmsa-222.pantheonsite.io/event/simons-collaboration-workshop-april-5-7-2018/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Amplituhedron-0c.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180402T163000
DTEND;TZID=America/New_York:20180403T180000
DTSTAMP:20260218T203218Z
CREATED:20230717T174857Z
LAST-MODIFIED:20260218T203218Z
UID:10000076-1522686600-1522778400@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Math Science Lectures in Honor of Raoul Bott\, April 2-3
DESCRIPTION:On April 2-3\, the CMSA will be hosting two lectures by Freddy Cachazo (Perimeter Institute) on “Geometry and Combinatorics in Particle Interactions.”  This will be the first of the new annual Bott Math Science Lecture Series hosted by the CMSA. \nThe lectures will take place from 4:30-5:30pm in Science Center\, Hall D. \n \n \n  \n 
URL:https://live-hu-cmsa-222.pantheonsite.io/event/math-science-lectures-in-honor-of-raoul-bott-april-2-3/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Event,Math Science Lectures in Honor of Raoul Bott,Special Lectures
ATTACH;FMTTYPE=image/jpeg:https://live-hu-cmsa-222.pantheonsite.io/media/Cachazo-e1519325938458.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180324T090000
DTEND;TZID=America/New_York:20180326T181500
DTSTAMP:20250304T212149Z
CREATED:20230717T174646Z
LAST-MODIFIED:20250304T212149Z
UID:10000074-1521882000-1522088100@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Workshop on Geometry\, Imaging\, and Computing
DESCRIPTION:On March 24-26\, The Center of Mathematical Sciences and Applications will be hosting a workshop on Geometry\, Imaging\, and Computing\, based off  the journal of the same name. The workshop will take place in CMSA building\, G10. \nThe organizing committee consists of Yang Wang (HKUST)\, Ronald Lui (CUHK)\, David Gu (Stony Brook)\, and Shing-Tung Yau (Harvard). \nConfirmed Speakers: \n\nJianfeng Cai (HKUST)\nShikui Chen (Stony Brook)\nJerome Darbon (Brown University)\nLaurent Demanet (MIT)\nDavid Gu (Stony Brook)\nMonica Hurdal (Florida State University)\nRongjie Lai (RPI)\nYue Lu (Harvard)\nRonald Lok Ming Lui (CUHK)\nLakshminarayanan Mahadevan (Harvard)\nEric Miller (Tufts)\nAshley Prater  (AFOSR)\nLixin Shen (Syracuse University)\nAllen Tannenbaum (Stony Brook)\nGuowei Wei (Michigan State)\nStephen Wong (Houston Methodist)\nJun Zhang (University of Michigan\, Ann Arbor)\nSong Zhang (Purdue University)\nHongkai Zhao (University of California\, Irvine)
URL:https://live-hu-cmsa-222.pantheonsite.io/event/workshop-on-geometry-imaging-and-computing/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/GIC-Poster-2-e1520002551865.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180205T090000
DTEND;TZID=America/New_York:20180209T170000
DTSTAMP:20250304T211916Z
CREATED:20230717T174149Z
LAST-MODIFIED:20250304T211916Z
UID:10000044-1517821200-1518195600@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Workshop on Probabilistic and Extremal Combinatorics
DESCRIPTION:The workshop on Probabilistic and Extremal Combinatorics will take place February 5-9\, 2018 at the Center of Mathematical Sciences and Applications\, located at 20 Garden Street\, Cambridge\, MA. \nExtremal and Probabilistic Combinatorics are two of the most central branches of modern combinatorial theory. Extremal Combinatorics deals with problems of determining or estimating the maximum or minimum possible cardinality of a collection of finite objects satisfying certain requirements. Such problems are often related to other areas including Computer Science\, Information Theory\, Number Theory and Geometry. This branch of Combinatorics has developed spectacularly over the last few decades. Probabilistic Combinatorics can be described informally as a (very successful) hybrid between Combinatorics and Probability\, whose main object of study is probability distributions on discrete structures. \nThere are many points of interaction between these fields. There are deep similarities in methodology. Both subjects are mostly asymptotic in nature. Quite a few important results from Extremal Combinatorics have been proven applying probabilistic methods\, and vice versa. Such emerging subjects as Extremal Problems in Random Graphs or the theory of graph limits stand explicitly at the intersection of the two fields and indicate their natural symbiosis. \nThe symposia will focus on the interactions between the above areas. These topics include Extremal Problems for Graphs and Set Systems\, Ramsey Theory\, Combinatorial Number Theory\, Combinatorial Geometry\, Random Graphs\, Probabilistic Methods and Graph Limits. \nParticipation: The workshop is open to participation by all interested researchers\, subject to capacity. \nConfirmed participants include: \n\nJozsef Balogh\, University of Illinois\, Urbana\nFan Chung (Graham)\, University of California\, San Diego\nAsaf Ferber\, Massachusetts Institute of Technology\nJacob Fox\, Stanford Unviersity\nDavid Gamarnik\, Massachusetts Institute of Technology\nPenny Haxell\, University of Waterloo\nHao Huang\, Emory University\nJeff Kahn\, Rutgers University\nPeter Keevash\, Oxford University\nMichael Krivelevich\, Tel Aviv University\nDaniela Kühn\, University of Birmingham\nShoham Letzer\, ITS Zürich\nShachar Lovett\, University of California\, San Diego\nEyal Lubetzky\, Courant Institute\nRob Morris\, IMPA\nBhargav Narayanan\, Rutgers University\nDeryk Osthus\, University of Birmingham\nJanos Pach\, NYU\nYuval Peres\, Microsoft Redmond\nAlexey Pokryovskyi\, ETH Zürich\nWojciech Samotij\, Tel Aviv University\nLisa Sauermann\, Stanford University\nMathias Schacht\, University of Hamburg\nAlexander Scott\, University of Oxford\nAsaf Shapira\, Tel Aviv University\nJozef Skokan\, London School of Economics\nJoel Spencer\, New York University\nAngelika Steger\, ETH Zurich\nJacques Verstraete\, University of California\, San Diego\nYufei Zhao\, Massachusetts Institute of Technology\nDavid Zuckerman\, University of Texas at Austin\n\nCo-organizers of this workshop include Benny Sudakov and David Conlon.  More details about this event\, including participants\, will be updated soon.
URL:https://live-hu-cmsa-222.pantheonsite.io/event/workshop-on-probabilistic-and-extremal-combinatorics/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180124T090000
DTEND;TZID=America/New_York:20180125T170000
DTSTAMP:20250305T214037Z
CREATED:20230717T173945Z
LAST-MODIFIED:20250305T214037Z
UID:10000042-1516784400-1516899600@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Blockchain Conference
DESCRIPTION:On January 24-25\, 2019 the Center of Mathematical Sciences will be hosting a conference on distributed-ledger (blockchain) technology. The conference is intended to cover a broad range of topics\, from abstract mathematical aspects (cryptography\, game theory\, graph theory\, theoretical computer science) to concrete applications (in accounting\, government\, economics\, finance\, management\, medicine). The talks will take place in Science Center\, Hall D. \nhttps://youtu.be/FyKCCutxMYo \nPhotos\n \nSpeakers: \n\nJoseph Abadi\, Princeton University\nBenedikt Bunz\, Stanford University\nJake Cacciapaglia\, Nebula Genomics/Harvard Medical School\nEduardo Castello\, Massachusetts Institute of Technology\nAlisa DiCaprio\, R3\nZhiguo He\, University of Chicago\nSteven Kou\, Boston University\nAnne Lafarre\, Tilburg University\nJacob Leshno\, University of Chicago\nBruce Schneier\, Harvard Kennedy School\nDavid Schwartz\, Ripple\nElaine Shi\, Cornell University/Thunder Research\nHong Wan\, NCSU
URL:https://live-hu-cmsa-222.pantheonsite.io/event/blockchain-conference/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/jpeg:https://live-hu-cmsa-222.pantheonsite.io/media/Blockchain-Final-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180110T090000
DTEND;TZID=America/New_York:20180113T170000
DTSTAMP:20250305T181650Z
CREATED:20230717T173545Z
LAST-MODIFIED:20250305T181650Z
UID:10000041-1515574800-1515862800@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Simons Collaboration Workshop
DESCRIPTION:The CMSA will be hosting a four-day Simons Collaboration Workshop on Homological Mirror Symmetry and Hodge Theory on January 10-13\, 2018. The workshop will be held in room G10 of the CMSA\, located at 20 Garden Street\, Cambridge\, MA. \n  \nConfirmed Participants: \n\nMohammed Abouzaid (Columbia University)\nSergueï Barannikov (Paris Diderot University)\nCheol-Hyun Cho (Seoul National University)\nYoung-Hoon Kiem (Seoul National University)\nThomas Lam (University of Michigan)\nSiu-Cheong Lau (Boston University)\nRadu Laza (Stony Brook University)\nSi Li (Tsinghua University)\nKaoru Ono (Kyoto University)\nTony Pantev (University of Pennsylvania)\nColleen Robles (Duke University)\nYan Soibelman (Kansas State University)\nKazushi Ueda (University of Tokyo)\nChenglong Yu (Harvard University)\nEric Zaslow (Northwestern University)
URL:https://live-hu-cmsa-222.pantheonsite.io/event/simons-collaboration-workshop-jan-10-13-2018/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/jpeg:https://live-hu-cmsa-222.pantheonsite.io/media/default-harvard-university-center-of-mathematical-sciences-and-applications.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171113T090000
DTEND;TZID=America/New_York:20171117T160000
DTSTAMP:20250304T211529Z
CREATED:20230717T173740Z
LAST-MODIFIED:20250304T211529Z
UID:10000040-1510563600-1510934400@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Workshop on Algebraic Methods in Combinatorics
DESCRIPTION:The workshop on Algebraic Methods in Combinatorics will take place November 13-17\, 2017 at the Center of Mathematical Sciences and Applications\, located at 20 Garden Street\, Cambridge\, MA. \nThe main focus of the workshop is the application of algebraic method to study problems in combinatorics.  In recent years there has been a large number of results in which the use of algebraic technique has resulted in significant improvements to long standing open problems. Such problems include the finite field Kakeya problem\, the distinct distance problem of Erdos and\, more recently\, the cap-set problem. The workshop will include talks on all of the above mentioned problem as well as on recent development in related areas combining combinatorics and algebra. \nConfirmed participants include: \n\nAbdul Basit\, Rutgers\nBoris Bukh\, Carnegie Mellon University\nPete L. Clark\, University of Georgia\nDavid Conlon\, University of Oxford\nFrank de Zeeuw\, EPFL\nThao Thi Thu Do\, MIT\nNoam Elkies\, Harvard University\nJordan Ellenberg\, University of Wisconsin\nDion Gijswijt\, Delft Institute of Technology\nSivankanth Gopi\, Princeton University\nVenkatesan Guruswami\, Carnegie Mellon University\nMarina Iliopoulou\, University of California\, Berkeley\nRobert Kleinberg\, Cornell University\nMichael Krivelevich\, Tel Aviv University\nVsevelod Lev\, University of Haifa at Oranim\nLászló Miklós Lovász\, UCLA\nBen Lund\, Rutgers\nPéter Pach\, Budapest University of Technology and Economics\nJános Pach\, New York University\nZuzana Patáková\, Institute of Science and Technology Austria\nOrit Raz\, Institute for Advanced Study\nOliver Roche-Newton\, Johannes Kepler University\nMisha Rudnev\, University of Bristol\nAdam Sheffer\, California Institute of Technology\nAmir Shpilka\, Tel-Aviv University\nNoam Solomon\, Harvard CMSA\nJozsef Solymosi\, University of British Columbia\nBenny Sudakov\, ETH\, Zurich\nAndrew Suk\, University of California\, San Diego\nTibor Szabó\, Freie Universität Berlin\nChris Umans\, California Institute of Technology\nAvi Wigderson\, Princeton University\nJosh Zahl\, University of British Columbia\n\nCo-organizers of this workshop include Zeev Dvir\, Larry Guth\, and Shubhangi Saraf. \nMonday\, Nov. 13 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n9:00-9:30am\nBreakfast\n\n\n\n9:30-10:30am \nVideo\nJozsef Solymosi \n \n\nOn the unit distance problem \nAbstract: Erdos’ Unit Distances conjecture states that the maximum number of unit distances determined by n points in the plane is almost linear\, it is O(n^{1+c}) where c goes to zero as n goes to infinity. In this talk I will survey the relevant results and propose some questions which would imply that the maximum number of unit distances is o(n^{4/3}).  \n\n\n\n10:30-11:00am\nCoffee Break\n\n\n\n11:00-12:00pm \nVideo \n \nOrit Raz\nIntersection of linear subspaces in R^d and instances of the PIT problem  \nAbstract: In the talk I will tell about a new deterministic\, strongly polynomial time algorithm which can be viewed in two ways. The first is as solving a derandomization problem\, providing a deterministic algorithm to a new special case of the PIT (Polynomial Identity Testing) problem. The second is as computing the dimension of the span of a collection of flats in high dimensional space. The talk is based on a joint work with Avi Wigderson.\n\n\n12:00-1:30pm\nLunch\n\n\n\n1:30-2:30pm \nVideo\nAndrew Hoon Suk\n\nRamsey numbers: combinatorial and geometric \nAbstract:  In this talk\, I will discuss several results on determining the tower growth rate of Ramsey numbers arising in combinatorics and in geometry.  These results are joint work with David Conlon\, Jacob Fox\, Dhruv Mubayi\, Janos Pach\, and Benny Sudakov. \n\n\n\n2:30-3:00pm\nCoffee Break\n\n\n\n3:00-4:00pm \nVideo\nJosh Zahl\n\nCutting curves into segments and incidence geometry \n\n\n\n4:00-6:00pm\nWelcome Reception\n\n\n\n\nTuesday\, Nov. 14 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n9:00-9:30am\nBreakfast\n\n\n\n9:30-10:30am \nVideo\nPéter Pál Pach\n\nPolynomials\, rank and cap sets \nAbstract: In this talk we will look at a new variant of the polynomial method which was first used to prove that sets avoiding 3-term arithmetic progressions in groups like $\mathbb{Z}_4^n$ and $\mathbb{F}_q^n$ are exponentially small (compared to the size of the group). We will discuss lower and upper bounds for the size of the extremal subsets and mention further applications of the method. \n\n\n\n10:30-11:00am\nCoffee Break\n\n\n\n11:00-12:00pm\nJordan Ellenberg\n\nThe Degeneration Method \nAbstract:  In algebraic geometry\, a very popular way to study (nice\, innocent\, nonsingular) varieties is to degenerate them to (weird-looking\, badly singular\, nonreduced) varieties (which are actually not even varieties but schemes.)  I will talk about some results in combinatorics using this approach (joint with Daniel Erman) and some ideas for future applications of the method. \n\n\n\n12:00-1:30pm\nLunch\n\n\n\n1:30-2:30pm \nVideo\nLarry Guth\nThe polynomial method in Fourier analysis \nAbstract: This will be a survey talk about how the polynomial method helps to understand problems in Fourier analysis.  We will review some applications of the polynomial method to problems in combinatorial geometry.  Then we’ll discuss some problems in Fourier analysis\, explain the analogy with combinatorial problems\, and discuss how to adapt the polynomial method to the Fourier analysis setting.\n\n\n  \n2:30-3:00pm\nCoffee Break\n\n\n\n3:00-4:00pm\nOpen Problem\n\n\n\n\nWednesday\, Nov. 15 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n9:00-9:30am\nBreakfast\n\n\n\n9:30-10:30am \n \nAvi Wigderson\n\nThe “rank method” in arithmetic complexity: Lower bounds and barriers to lower bounds \nAbstract: Why is it so hard to find a hard function? No one has a clue! In despair\, we turn to excuses called barriers. A barrier is a collection of lower bound techniques\, encompassing as much as possible from those in use\, together with a  proof that these techniques cannot prove any lower bound better than the state-of-art (which is often pathetic\, and always very far from what we expect for complexity of random functions). \nIn the setting of  Boolean computation of Boolean functions (where P vs. NP is the central open problem)\,  there are several famous barriers which provide satisfactory excuses\, and point to directions in which techniques may be strengthened. \nIn the setting of Arithmetic computation of polynomials and tensors (where  VP vs. VNP is the central open problem) we have no satisfactory barriers\, despite some recent interesting  attempts. \nThis talk will describe a new barrier for the Rank Method in arithmetic complexity\, which encompass most lower bounds in this field. It also encompass most lower bounds on tensor rank in algebraic geometry (where the the rank method is called Flattening). \nI will describe the rank method\, explain how it is used to prove lower bounds\, and then explain its limits via the new barrier result. As an example\, it shows that while the best lower bound on the tensor rank of any explicit 3-dimensional tensor of side n (which is achieved by a rank method) is 2n\, no rank method can prove a lower bound which exceeds 8n \n(despite the fact that a random such tensor has rank quadratic in n). \nNo special background knowledge is assumed. The audience is expected to come up with new lower bounds\, or else\, with new excuses for their absence. \n\n\n\n10:30-11:00am\nCoffee Break\n\n\n\n11:00-12:00pm \nVideo\nVenkat Guruswami\n\nSubspace evasion\, list decoding\, and dimension expanders \n Abstract: A subspace design is a collection of subspaces of F^n (F = finite field) most of which are disjoint from every low-dimensional subspace of F^n. This notion was put forth in the context of algebraic list decoding where it enabled the construction of optimal redundancy list-decodable codes over small alphabets as well as for error-correction in the rank-metric. Explicit subspace designs with near-optimal parameters have been constructed over large fields based on polynomials with structured roots. (Over small fields\, a construction via cyclotomic function fields with slightly worse parameters is known.) Both the analysis of the list decoding algorithm as well as the subspace designs crucially rely on the *polynomial method*. \nSubspace designs have since enabled progress on linear-algebraic analogs of Boolean pseudorandom objects where the rank of subspaces plays the role of the size of subsets. In particular\, they yield an explicit construction of constant-degree dimension expanders over large fields. While constructions of such dimension expanders are known over any field\, they are based on a reduction to a highly non-trivial form of vertex expanders called monotone expanders. In contrast\, the subspace design approach is simpler and works entirely within the linear-algebraic realm. Further\, in recent (ongoing) work\, their combination with rank-metric codes yields dimension expanders with expansion proportional to the degree. \nThis talk will survey these developments revolving around subspace designs\, their motivation\, construction\, analysis\, and connections. \n(Based on several joint works whose co-authors include Chaoping Xing\, Swastik Kopparty\, Michael Forbes\, Nicolas Resch\, and Chen Yuan.) \n\n\n\n12:00-1:30pm\nLunch\n\n\n\n1:30-2:30pm \n \nDavid Conlon\n\nFinite reflection groups and graph norms \nAbstract: For any given graph $H$\, we may define a natural corresponding functional $\|.\|_H$. We then say that $H$ is norming if $\|.\|_H$ is a semi-norm. A similar notion $\|.\|_{r(H)}$ is defined by $\| f \|_{r(H)} := \| | f | \|_H$ and $H$ is said to be weakly norming if $\|.\|_{r(H)}$ is a norm. Classical results show that weakly norming graphs are necessarily bipartite. In the other direction\, Hatami showed that even cycles\, complete bipartite graphs\, and hypercubes are all weakly norming. Using results from the theory of finite reflection groups\, we identify a much larger class of weakly norming graphs. This result includes all previous examples of weakly norming graphs and adds many more. We also discuss several applications of our results. In particular\, we define and compare a number of generalisations of Gowers’ octahedral norms and we prove some new instances of Sidorenko’s conjecture. Joint work with Joonkyung Lee. \n \n\n\n2:30-3:00pm\nCoffee Break\n\n\n\n3:00-4:00pm \nVideo\nLaszlo Miklós Lovasz\n\nRemoval lemmas for triangles and k-cycles. \nAbstract: Let p be a fixed prime. A k-cycle in F_p^n is an ordered k-tuple of points that sum to zero; we also call a 3-cycle a triangle. Let N=p^n\, (the size of F_p^n). Green proved an arithmetic removal lemma which says that for every k\, epsilon>0 and prime p\, there is a delta>0 such that if we have a collection of k sets in F_p^n\, and the number of k-cycles in their cross product is at most a delta fraction of all possible k-cycles in F_p^n\, then we can delete epsilon times N elements from the sets and remove all k-cycles. Green posed the problem of improving the quantitative bounds on the arithmetic triangle removal lemma\, and\, in particular\, asked whether a polynomial bound holds. Despite considerable attention\, prior to our work\, the best known bound for any k\, due to Fox\, showed that 1/delta can be taken to be an exponential tower of twos of height logarithmic in 1/epsilon (for a fixed k). \nIn this talk\, we will discuss recent work on Green’s problem. For triangles\, we prove an essentially tight bound for Green’s arithmetic triangle removal lemma in F_p^n\, using the recent breakthroughs with the polynomial method. For k-cycles\, we also prove a polynomial bound\, however\, the question of the optimal exponent is still open. \nThe triangle case is joint work with Jacob Fox\, and the k-cycle case with Jacob Fox and Lisa Sauermann. \n\n\n\n\nThursday\, Nov. 16 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n9:00-9:30am\nBreakfast\n\n\n\n9:30-10:30am \nVideo\nJanos Pach\nLet’s talk about multiple crossings \nAbstract: Let k>1 be a fixed integer. It is conjectured that any graph on n vertices that can be drawn in the plane without k pairwise crossing edges has O(n) edges. Two edges of a hypergraph cross each other if neither of them contains the other\, they have a nonempty intersection\, and their union is not the whole vertex set. It is conjectured that any hypergraph on n vertices that contains no k pairwise crossing edges has at most O(n) edges. We discuss the relationship between the above conjectures and explain some partial answers\, including a recent result of Kupavskii\, Tomon\, and the speaker\, improving a 40 years old bound of Lomonosov.\n\n\n10:30-11:00am\nCoffee Break\n\n\n\n11:00-12:00pm \nVideo\nMisha Rudnev\n\nFew products\, many sums \nAbstract: This is what I like calling “weak Erd\H os-Szemer\’edi conjecture”\, still wide open over the reals and in positive characteristic. The talk will focus on some recent progress\, largely based on the ideas of I. D. Shkredov over the past 5-6 years of how to use linear algebra to get the best out of the Szemer\’edi-Trotter theorem for its sum-product applications. One of the new results is strengthening (modulo the log term hidden in the $\lesssim$ symbol) the textbook Elekes inequality \n$$ \n|A|^{10} \ll |A-A|^4|AA|^4 \n$$ \nto \n$$|A|^{10}\lesssim |A-A|^3|AA|^5.$$ \nThe other is the bound  \n$$E(H) \lesssim |H|^{2+\frac{9}{20}}$$ for additive energy of sufficiently small multiplicative subgroups in $\mathbb F_p$. \n\n\n\n12:00-1:30pm\nLunch\n\n\n\n1:30-2:30pm \nVideo\nAdam Sheffer\n\nGeometric Energies: Between Discrete Geometry and Additive Combinatorics \nAbstract: We will discuss the rise of geometric variants of the concept of Additive energy. In recent years such variants are becoming more common in the study of Discrete Geometry problems. We will survey this development and then focus on a recent work with Cosmin Pohoata. This work studies geometric variants of additive higher moment energies\, and uses those to derive new bounds for several problems in Discrete Geometry.   \n\n\n\n2:30-3:00pm\nCoffee Break\n\n\n\n3:00-4:00pm \nVideo\nBoris Bukh\n\nRanks of matrices with few distinct entries \nAbstract: Many applications of linear algebra method to combinatorics rely on the bounds on ranks of matrices with few distinct entries and constant diagonal. In this talk\, I will explain some of these application. I will also present a classification of sets L for which no low-rank matrix with entries in L exists. \n\n\n\n\nFriday\, Nov. 17 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n9:00-9:30am\nBreakfast\n\n\n\n9:30-10:30am \nVideo\nBenny Sudakov\n\nSubmodular minimization and set-systems with restricted intersections \nAbstract: Submodular function minimization is a fundamental and efficiently solvable problem class in combinatorial optimization with a multitude of applications in various fields. Surprisingly\, there is only very little known about constraint types under which it remains efficiently solvable. The arguably most relevant non-trivial constraint class for which polynomial algorithms are known are parity constraints\, i.e.\, optimizing submodular function only over sets of odd (or even) cardinality. Parity constraints capture classical combinatorial optimization problems like the odd-cut problem\, and they are a key tool in a recent technique to efficiently solve integer programs with a constraint matrix whose subdeter-minants are bounded by two in absolute value. \nWe show that efficient submodular function minimization is possible even for a significantly larger class than parity constraints\, i.e.\, over all sets (of any given lattice) of cardinality r mod m\, as long as m is a constant prime power. To obtain our results\, we combine tools from Combinatorial Optimization\, Combinatorics\, and Number Theory. In particular\, we establish an interesting connection between the correctness of a natural algorithm\, and the non-existence of set systems with specific intersection properties. \nJoint work with M. Nagele and R. Zenklusen \n\n\n\n10:30-11:00am\nCoffee Break\n\n\n\n11:00-12:00pm \nVideo\nRobert Kleinberg\n  \nExplicit sum-of-squares lower bounds via the polynomial method \nAbstract: The sum-of-squares (a.k.a. Positivstellensatz) proof system is a powerful method for refuting systems of multivariate polynomial inequalities\, i.e. proving that they have no solutions. These refutations themselves involve sum-of-squares (sos) polynomials\, and while any unsatisfiable system of inequalities has a sum-of-squares refutation\, the sos polynomials involved might have arbitrarily high degree. However\, if a system admits a refutation where all polynomials involved have degree at most d\, then the refutation can be found by an algorithm with running time polynomial in N^d\, where N is the combined number of variables and inequalities in the system. \nLow-degree sum-of-squares refutations appear throughout mathematics. For example\, the above proof search algorithm captures as a special case many a priori unrelated algorithms from theoretical computer science; one example is Goemans and Williamson’s algorithm to approximate the maximum cut in a graph. Specialized to extremal graph theory\, they become equivalent to flag algebras. They have also seen practical use in robotics and optimal control. \nTherefore\, it is of interest to identify “hard” systems of low-degree polynomial inequalities that have no solutions but also have no low-degree sum-of-squares refutations. Until recently\, the only known examples were either not explicit (i.e.\, known to exist by non-constructive means such as the probabilistic method) or not robust (i.e.\, a system is constructed which is not refutable by degree d sos polynomials\, but becomes refutable when perturbed by an amount tending to zero with d). We present a new family of instances derived from the cap-set problem\, and we show a super-constant lower bound on the degree of its sum-of-squares refutations. Our instances are both explicit and robust. \nThis is joint work with Sam Hopkins. \n\n\n\n12:00-1:30pm\nLunch\n\n\n\n\n  \n\n\n\nEvents\,Past Events\,Programs
URL:https://live-hu-cmsa-222.pantheonsite.io/event/workshop-on-algebraic-methods-in-combinatorics/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171102T170000
DTEND;TZID=America/New_York:20171102T180000
DTSTAMP:20250305T151232Z
CREATED:20230717T173530Z
LAST-MODIFIED:20250305T151232Z
UID:10000039-1509642000-1509645600@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Jennifer Chayes Public Talk
DESCRIPTION:Jennifer Chayes (Microsoft Research) will be giving a public talk on November 02\, 2017\, as part of the Program on combinatorics and complexity hosted by the CMSA during AY17-18.  The talk will be at 5:00pm in Askwith Hall\, 13 Appian Way\, Cambridge\, MA. \nTitle: Network Science: From the Online World to Cancer Genomics \nAbstract: Everywhere we turn these days\, we find that networks can be used to describe relevant interactions. In the high tech world\, we see the Internet\, the World Wide Web\, mobile phone networks\, and a variety of online social networks. In economics\, we are increasingly experiencing both the positive and negative effects of a global networked economy. In epidemiology\, we find disease spreading over our ever growing social networks\, complicated by mutation of the disease agents. In biomedical research\, we are beginning to understand the structure of gene regulatory networks\, with the prospect of using this understanding to manage many human diseases. In this talk\, I look quite generally at some of the models we are using to describe these networks\, processes we are studying on the networks\, algorithms we have devised for the networks\, and finally\, methods we are developing to indirectly infer network structure from measured data. I’ll discuss in some detail particular applications to cancer genomics\, applying network algorithms to suggest possible drug targets for certain kinds of cancer. \n 
URL:https://live-hu-cmsa-222.pantheonsite.io/event/jennifer-chayes-public-talk-11-02-17/
LOCATION:Askwith Hall\, Harvard University
CATEGORIES:Event,Public Lecture
ATTACH;FMTTYPE=application/pdf:https://live-hu-cmsa-222.pantheonsite.io/media/Chayes-public-talk.pdf
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171010T170000
DTEND;TZID=America/New_York:20171010T180000
DTSTAMP:20250328T150724Z
CREATED:20230717T173349Z
LAST-MODIFIED:20250328T150724Z
UID:10000038-1507654800-1507658400@live-hu-cmsa-222.pantheonsite.io
SUMMARY:2017 Ding Shum Lecture
DESCRIPTION:Leslie Valiant will be giving the inaugural talk of the Ding Shum Lectures on Tuesday\, October 10 at 5:00 pm in Science Center Hall D\, Cambridge\, MA. \nLearning as a Theory of Everything \nAbstract: We start from the hypothesis that all the information that resides in living organisms was initially acquired either through learning by an individual or through evolution. Then any unified theory of evolution and learning should be able to characterize the capabilities that humans and other living organisms can possess or acquire. Characterizing these capabilities would tell us about the nature of humans\, and would also inform us about feasible targets for automation. With this purpose we review some background in the mathematical theory of learning. We go on to explain how Darwinian evolution can be formulated as a form of learning. We observe that our current mathematical understanding of learning is incomplete in certain important directions\, and conclude by indicating one direction in which further progress would likely enable broader phenomena of intelligence and cognition to be realized than is possible at present. \n 
URL:https://live-hu-cmsa-222.pantheonsite.io/event/2017-ding-shum-lecture/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Ding Shum Lecture,Event,Public Lecture,Special Lectures
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Ding-Shum-lecture-3.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171002T091500
DTEND;TZID=America/New_York:20171002T173000
DTSTAMP:20250328T150846Z
CREATED:20230717T172938Z
LAST-MODIFIED:20250328T150846Z
UID:10000036-1506935700-1506965400@live-hu-cmsa-222.pantheonsite.io
SUMMARY:The 2017 Charles River Lectures
DESCRIPTION:Charles River with Bench at Sunset\nJointly organized by Harvard University\, Massachusetts Institute of Technology\, and Microsoft Research New England\, the Charles River Lectures on Probability and Related Topics is a one-day event for the benefit of the greater Boston area mathematics community. \nThe 2017 lectures will take place 9:15am – 5:30pm on Monday\, October 2 at Harvard University  in the Harvard Science Center. \n\n\n\n*************************************************** \nUPDATED LOCATION\nHarvard University\nHarvard Science Center (Halls C & E)\n1 Oxford Street\, Cambridge\, MA 02138 (Map)\nMonday\, October 2\, 2017\n9:15 AM – 5:30 PM\n************************************************** \nPlease note that registration has closed. \nSpeakers:\n\nPaul Bourgade (Courant Institute\, NYU)\nMassimiliano Gubinelli (University of Bonn)\nAndrea Montanari (Stanford University)\nRoman Vershynin (University of California\, Irvine)\nOfer Zeitouni (Weizmann Institute)\n\nAgenda:\nIn Harvard Science Center Hall C: \n8:45 am – 9:15 am: Coffee/light breakfast \n9:15 am – 10:15 am: Ofer Zeitouni \nTitle: Noise stability of the spectrum of large matrices \nAbstract: The spectrum of large non-normal matrices is notoriously sensitive to perturbations\, as the example of nilpotent matrices shows. Remarkably\, the spectrum of these matrices perturbed by polynomially (in the dimension) vanishing additive noise is remarkably stable. I will describe some results and the beginning of a theory. \nThe talk is based on joint work with Anirban Basak and Elliot Paquette\, and earlier works with Feldheim\, Guionnet\, Paquette and Wood.\n\n10:20 am – 11:20 am: Andrea Montanari \nTitle: Algorithms for estimating low-rank matrices  \nAbstract: Many interesting problems in statistics can be formulated as follows. The signal of interest is a large low-rank matrix with additional structure\, and we are given a single noisy view of this matrix. We would like to estimate the low rank signal by taking into account optimally the signal structure. I will discuss two types of efficient estimation procedures based on message-passing algorithms and semidefinite programming relaxations\, with an emphasis on asymptotically exact results. \n11:20 am – 11:45 am: Break \n11:45 am – 12:45 pm: Paul Bourgade \nTitle: Random matrices\, the Riemann zeta function and trees \nAbstract: Fyodorov\, Hiary & Keating have conjectured that the maximum of the characteristic polynomial of random unitary matrices behaves like extremes of log-correlated Gaussian fields. This allowed them to predict the typical size of local maxima of the Riemann zeta function along the critical axis. I will first explain the origins of this conjecture\, and then outline the proof for the leading order of the maximum\, for unitary matrices and the zeta function. This talk is based on joint works with Arguin\, Belius\, Radziwill and Soundararajan. \n1:00 pm – 2:30 pm: Lunch \nIn Harvard Science Center Hall E: \n2:45 pm – 3:45 pm: Roman Vershynin \nTitle: Deviations of random matrices and applications \nAbstract: Uniform laws of large numbers provide theoretical foundations for statistical learning theory. This lecture will focus on quantitative uniform laws of large numbers for random matrices. A range of illustrations will be given in high dimensional geometry and data science. \n3:45 pm – 4:15 pm: Break \n4:15 pm – 5:15 pm: Massimiliano Gubinelli \nTitle: Weak universality and Singular SPDEs \nAbstract: Mesoscopic fluctuations of microscopic (discrete or continuous) dynamics can be described in terms of nonlinear stochastic partial differential equations which are universal: they depend on very few details of the microscopic model. This universality comes at a price: due to the extreme irregular nature of the random field sample paths\, these equations turn out to not be well-posed in any classical analytic sense. I will review recent progress in the mathematical understanding of such singular equations and of their (weak) universality and their relation with the Wilsonian renormalisation group framework of theoretical physics. \nOrganizers:\n Alexei Borodin\, Henry Cohn\, Vadim Gorin\, Elchanan Mossel\, Philippe Rigollet\, Scott Sheffield\, and H.T. Yau
URL:https://live-hu-cmsa-222.pantheonsite.io/event/the-2017-charles-river-lectures/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Event,Public Lecture,Special Lectures
ATTACH;FMTTYPE=image/jpeg:https://live-hu-cmsa-222.pantheonsite.io/media/Charles-River-Lectures-2017-pdf.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171002T090000
DTEND;TZID=America/New_York:20171006T160000
DTSTAMP:20250304T211134Z
CREATED:20230717T173144Z
LAST-MODIFIED:20250304T211134Z
UID:10000037-1506934800-1507305600@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Workshop on Additive Combinatorics\, Oct. 2-6\, 2017
DESCRIPTION:The workshop on additive combinatorics will take place October 2-6\, 2017 at the Center of Mathematical Sciences and Applications\, located at 20 Garden Street\, Cambridge\, MA. \nAdditive combinatorics is a mathematical area bordering on number theory\, discrete mathematics\, harmonic analysis and ergodic theory. It has achieved a number of successes in pure mathematics in the last two decades in quite diverse directions\, such as: \n\nThe first sensible bounds for Szemerédi’s theorem on progressions (Gowers);\nLinear patterns in the primes (Green\, Tao\, Ziegler);\nConstruction of expanding sets in groups and expander graphs (Bourgain\, Gamburd);\nThe Kakeya Problem in Euclidean harmonic analysis (Bourgain\, Katz\, Tao).\n\nIdeas and techniques from additive combinatorics have also had an impact in theoretical computer science\, for example \n\nConstructions of pseudorandom objects (eg. extractors and expanders);\nConstructions of extremal objects (eg. BCH codes);\nProperty testing (eg. testing linearity);\nAlgebraic algorithms (eg. matrix multiplication).\n\nThe main focus of this workshop will be to bring together researchers involved in additive combinatorics\, with a particular inclination towards the links with theoretical computer science. Thus it is expected that a major focus will be additive combinatorics on the boolean cube (Z/2Z)^n \, which is the object where the exchange of ideas between pure additive combinatorics and theoretical computer science is most fruitful. Another major focus will be the study of pseudorandom phenomena in additive combinatorics\, which has been an important contributor to modern methods of generating provably good randomness through deterministic methods. Other likely topics of discussion include the status of major open problems (the polynomial Freiman-Ruzsa conjecture\, inverse theorems for the Gowers norms with bounds\, explicit correlation bounds against low degree polynomials) as well as the impact of new methods such as the introduction of algebraic techniques by Croot–Pach–Lev and Ellenberg–Gijswijt. \nConfirmed participants include: \n\nArnab Bhattacharyya (Indian Institute of Science)\nThomas Bloom (University of Bristol)\nJop Briët (Centrum Wiskunde & Informatica\, Amsterdam)\nMei-Chu Chang (University of California\, Riverside)\nNoam Elkies (Harvard University)\nAsaf Ferber (MIT)\nJacob Fox (Stanford University)\nShafi Goldwasser (MIT)\nElena Grigorescu (Purdue University)\nHamed Hatami (McGill University)\nPooya Hatami (Institute for Advanced Study)\nKaave Hosseini (University of California\, San Diego)\nGuy Kindler (Hebrew University of Jerusalem)\nVsevolod Lev (University of Haifa at Oranim)\nSean Prendiville (University of Manchester)\nRonitt Rubinfeld (MIT)\nWill Sawin (ETH Zürich)\nFernando Shao (Oxford University)\nOlof Sisask (KTH Royal Institute of Technology)\nMadhur Tulsiani (University of Chicago)\nJulia Wolf (University of Bristol)\nEmanuele Viola (Northeastern University)\nYufei Zhao (MIT)\n\nCo-organizers of this workshop include Ben Green\, Swastik Kopparty\, Ryan O’Donnell\, Tamar Ziegler. \nMonday\, October 2 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n9:00-9:30am\nBreakfast\n \n\n\n9:30-10:20am\nJacob Fox\nTower-type bounds for Roth’s theorem with popular differences \nAbstract: A famous theorem of Roth states that for any $\alpha > 0$ and $n$ sufficiently large in terms of $\alpha$\, any subset of $\{1\, \dots\, n\}$ with density $\alpha$ contains a 3-term arithmetic progression. Green developed an arithmetic regularity lemma and used it to prove that not only is there one arithmetic progression\, but in fact there is some integer $d > 0$ for which the density of 3-term arithmetic progressions with common difference $d$ is at least roughly what is expected in a random set with density $\alpha$. That is\, for every $\epsilon > 0$\, there is some $n(\epsilon)$ such that for all $n > n(\epsilon)$ and any subset $A$ of $\{1\, \dots\, n\}$ with density $\alpha$\, there is some integer $d > 0$ for which the number of 3-term arithmetic progressions in $A$ with common difference $d$ is at least $(\alpha^3-\epsilon)n$. We prove that $n(\epsilon)$ grows as an exponential tower of 2’s of height on the order of $\log(1/\epsilon)$. We show that the same is true in any abelian group of odd order $n$. These results are the first applications of regularity lemmas for which the tower-type bounds are shown to be necessary. \nThe first part of the talk by Jacob Fox includes an overview and discusses the upper bound. The second part of the talk by Yufei Zhao focuses on the lower bound construction and proof. These results are all joint work with Huy Tuan Pham.\n\n\n10:20-11:00am\nCoffee Break\n \n\n\n11:00-11:50am\nYufei Zhao\nTower-type bounds for Roth’s theorem with popular differences \nAbstract:  Continuation of first talk by Jacob Fox. The first part of the talk by Jacob Fox includes an overview and discusses the upper bound. The second part of the talk by Yufei Zhao focuses on the lower bound construction and proof. These results are all joint work with Huy Tuan Pham.\n\n\n12:00-1:30pm\nLunch\n \n\n\n1:30-2:20pm\nJop Briët\nLocally decodable codes and arithmetic progressions in random settings \nAbstract: This talk is about a common feature of special types of error correcting codes\, so-called locally decodable codes (LDCs)\, and two problems on arithmetic progressions in random settings\, random differences in Szemerédi’s theorem and upper tails for arithmetic progressions in a random set in particular. It turns out that all three can be studied in terms of the Gaussian width of a set of vectors given by a collection of certain polynomials. Using a matrix version of the Khintchine inequality and a lemma that turns such polynomials into matrices\, we give an alternative proof for the best-known lower bounds on LDCs and improved versions of prior results due to Frantzikinakis et al. and Bhattacharya et al. on arithmetic progressions in the aforementioned random settings. \nJoint work with Sivakanth Gopi\n\n\n2:20-3:00pm\nCoffee Break\n \n\n\n3:00-3:50pm\nFernando Shao\n\nLarge deviations for arithmetic progressions \nAbstract: We determine the asymptotics of the log-probability that the number of k-term arithmetic progressions in a random subset of integers exceeds its expectation by a constant factor. This is the arithmetic analog of subgraph counts in a random graph. I will highlight some open problems in additive combinatorics that we encountered in our work\, namely concerning the “complexity” of the dual functions of AP-counts. \n\n\n\n4:00-6:00pm\nWelcome Reception\n\n\n\n\nTuesday\, October 3 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n9:00-9:30am\nBreakfast\n\n\n\n9:30-10:20am\nEmanuele Viola\nInterleaved group products \nAuthors: Timothy Gowers and Emanuele Viola \nAbstract: Let G be the special linear group SL(2\,q). We show that if (a1\,a2) and (b1\,b2) are sampled uniformly from large subsets A and B of G^2 then their interleaved product a1 b1 a2 b2 is nearly uniform over G. This extends a result of Gowers (2008) which corresponds to the independent case where A and B are product sets. We obtain a number of other results. For example\, we show that if X is a probability distribution on G^m such that any two coordinates are uniform in G^2\, then a pointwise product of s independent copies of X is nearly uniform in G^m\, where s depends on m only. Similar statements can be made for other groups as well. \nThese results have applications in computer science\, which is the area where they were first sought by Miles and Viola (2013).\n\n\n10:20-11:00am\nCoffee Break\n\n\n\n11:00-11:50am\nVsevolod Lev\nOn Isoperimetric Stability \nAbstract: We show that a non-empty subset of an abelian group with a small edge boundary must be large; in particular\, if $A$ and $S$ are finite\, non-empty subsets of an abelian group such that $S$ is independent\, and the edge boundary of $A$ with respect to $S$ does not exceed $(1-c)|S||A|$ with a real $c\in(0\,1]$\, then $|A|\ge4^{(1-1/d)c|S|}$\, where $d$ is the smallest order of an element of $S$. Here the constant $4$ is best possible. \nAs a corollary\, we derive an upper bound for the size of the largest independent subset of the set of popular differences of a finite subset of an abelian group. For groups of exponent $2$ and $3$\, our bound translates into a sharp estimate for the additive  dimension of the popular difference set. \nWe also prove\, as an auxiliary result\, the following estimate of possible independent interest: if $A\subseteq{\mathbb Z}^n$ is a finite\, non-empty downset\, then\, denoting by $w(z)$ the number of non-zero components of the vector $z\in\mathbb{Z}^n$\, we have   $$ \frac1{|A|} \sum_{a\in A} w(a) \le \frac12\\, \log_2 |A|. $$\n\n\n12:00-1:30pm\nLunch\n\n\n\n1:30-2:20pm\nElena Grigorescu\nNP-Hardness of Reed-Solomon Decoding and the Prouhet-Tarry-Escott Problem \nAbstract: I will discuss the complexity of decoding Reed-Solomon codes\, and some results establishing NP-hardness for asymptotically smaller decoding radii than the maximum likelihood decoding radius. These results follow from the study of a generalization of the classical Subset Sum problem to higher moments\, which may be of independent interest. I will further discuss a connection with the Prouhet-Tarry-Escott problem studied in Number Theory\, which turns out to capture a main barrier in extending our techniques to smaller radii. \nJoint work with Venkata Gandikota and Badih Ghazi.\n\n\n2:20-3:00pm\nCoffee Break\n\n\n\n3:00-3:50pm\nSean Prendiville\nPartition regularity of certain non-linear Diophantine equations. \nAbstract:  We survey some results in additive Ramsey theory which remain valid when variables are restricted to sparse sets of arithmetic interest\, in particular the partition regularity of a class of non-linear Diophantine equations in many variables.\n\n\n\nWednesday\, October 4 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n9:00-9:30am\nBreakfast\n \n\n\n9:30-10:20am\nOlof Sisask\nBounds on capsets via properties of spectra \nAbstract: A capset in F_3^n is a subset A containing no three distinct elements x\, y\, z satisfying x+z=2y. Determining how large capsets can be has been a longstanding problem in additive combinatorics\, particularly motivated by the corresponding question for subsets of {1\,2\,…\,N}. While the problem in the former setting has seen spectacular progress recently through the polynomial method of Croot–Lev–Pach and Ellenberg–Gijswijt\, such progress has not been forthcoming in the setting of the integers. Motivated by an attempt to make progress in this setting\, we shall revisit the approach to bounding the sizes of capsets using Fourier analysis\, and in particular the properties of large spectra. This will be a two part talk\, in which many of the ideas will be outlined in the first talk\, modulo the proof of a structural result for sets with large additive energy. This structural result will be discussed in the second talk\, by Thomas Bloom\, together with ideas on how one might hope to achieve Behrend-style bounds using this method. \nJoint work with Thomas Bloom.\n\n\n10:20-11:00am\nCoffee Break\n \n\n\n11:00-11:50am\nThomas Bloom\nBounds on capsets via properties of spectra \nThis is a continuation of the previous talk by Olof Sisask.\n\n\n12:00-1:30pm\nLunch\n \n\n\n1:30-2:20pm\nHamed Hatami\nPolynomial method and graph bootstrap percolation \nAbstract: We introduce a simple method for proving lower bounds for the size of the smallest percolating set in a certain graph bootstrap process. We apply this method to determine the sizes of the smallest percolating sets in multidimensional tori and multidimensional grids (in particular hypercubes). The former answers a question of Morrison and Noel\, and the latter provides an alternative and simpler proof for one of their main results. This is based on a joint work with Lianna Hambardzumyan and Yingjie Qian.\n\n\n2:20-3:00pm\nCoffee Break\n\n\n\n3:00-3:50pm\nArnab Bhattacharyya\nAlgorithmic Polynomial Decomposition \nAbstract: Fix a prime p. Given a positive integer k\, a vector of positive integers D = (D_1\, …\, D_k) and a function G: F_p^k → F_p\, we say a function P: F_p^n → F_p admits a (k\, D\, G)-decomposition if there exist polynomials P_1\, …\, P_k: F_p^n -> F_p with each deg(P_i) <= D_i such that for all x in F_p^n\, P(x) = G(P_1(x)\, …\, P_k(x)). For instance\, an n-variate polynomial of total degree d factors nontrivially exactly when it has a (2\, (d-1\, d-1)\, prod)-decomposition where prod(a\,b) = ab. \nWhen show that for any fixed k\, D\, G\, and fixed bound d\, we can decide whether a given polynomial P(x_1\, …\, x_n) of degree d admits a (k\,D\,G)-decomposition and if so\, find a witnessing decomposition\, in poly(n) time. Our approach is based on higher-order Fourier analysis. We will also discuss improved analyses and algorithms for special classes of decompositions. \nJoint work with Pooya Hatami\, Chetan Gupta and Madhur Tulsiani.\n\n\n\nThursday\, October 5 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n9:00-9:30am\nBreakfast\n\n\n\n9:30-10:20am\nMadhur Tulsiani\nHigher-order Fourier analysis and approximate decoding of Reed-Muller codes \n Abstract: Decomposition theorems proved by Gowers and Wolf provide an appropriate notion of “Fourier transform” for higher-order Fourier analysis. I will discuss some questions and techniques that arise from trying to develop polynomial time algorithms for computing these decompositions. \nI will discuss constructive proofs of these decompositions based on boosting\, which reduce the problem of computing these decompositions to a certain kind of approximate decoding problem for codes. I will also discuss some earlier and recent works on this decoding problem. \nBased on joint works with Arnab Bhattacharyya\, Eli Ben-Sasson\, Pooya Hatami\, Noga Ron-Zewi and Julia Wolf.\n\n\n10:20-11:00am\nCoffee Break\n\n\n\n11:00-11:50am\nJulia Wolf\nStable arithmetic regularity \nThe arithmetic regularity lemma in the finite-field model\, proved by Green in 2005\, states that given a subset A of a finite-dimensional vector space over a prime field\, there exists a subspace H of bounded codimension such that A is Fourier-uniform with respect to almost all cosets of H. It is known that in general\, the growth of the codimension of H is required to be of tower type depending on the degree of uniformity\, and that one must allow for a small number of non-uniform cosets. \nOur main result is that\, under a natural model-theoretic assumption of stability\, the tower-type bound and non-uniform cosets in the arithmetic regularity lemma are not necessary.  Specifically\, we prove an arithmetic regularity lemma for k-stable subsets in which the bound on the codimension of the subspace is a polynomial (depending on k) in the degree of uniformity\, and in which there are no non-uniform cosets. \nThis is joint work with Caroline Terry. \n\n\n\n12:00-1:30pm\nLunch\n \n\n\n1:30-2:20pm\nWill Sawin\n\nConstructions of Additive Matchings \nAbstract: I will explain my work\, with Robert Kleinberg and David Speyer\, constructing large tri-colored sum-free sets in vector spaces over finite fields\, and how it shows that some additive combinatorics problems over finite fields are harder than corresponding problems over the integers.  \n\n\n\n2:20-3:00pm\nCoffee Break\n\n\n\n3:00-3:50pm\nMei-Chu Chang\nArithmetic progressions in multiplicative groups of finite fields \nAbstract:   Let G be a multiplicative subgroup of the prime field F_p of size |G|> p^{1-\kappa} and r an arbitrarily fixed positive integer. Assuming \kappa=\kappa(r)>0 and p large enough\, it is shown that any proportional subset A of G contains non-trivial arithmetic progressions of length r.\n\n\n\nFriday\, October 6 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n9:00-9:30am\nBreakfast\n\n\n\n9:30-10:20am\nAsaf Ferber\nOn a resilience version of the Littlewood-Offord problem \nAbstract:  In this talk we consider a resilience version of the classical Littlewood-Offord problem. That is\, consider the sum X=a_1x_1+…a_nx_n\, where the a_i-s are non-zero reals and x_i-s are i.i.d. random variables with     (x_1=1)= P(x_1=-1)=1/2. Motivated by some problems from random matrices\, we consider the question: how many of the x_i-s  can we typically allow an adversary to change without making X=0? We solve this problem up to a constant factor and present a few interesting open problems. \nJoint with: Afonso Bandeira (NYU) and Matthew Kwan (ETH\, Zurich).\n\n\n10:20-11:00am\nCoffee Break\n\n\n\n11:00-11:50am\nKaave Hosseini\nProtocols for XOR functions and Entropy decrement \nAbstract: Let f:F_2^n –> {0\,1} be a function and suppose the matrix M defined by M(x\,y) = f(x+y) is partitioned into k monochromatic rectangles.  We show that F_2^n can be partitioned into affine subspaces of co-dimension polylog(k) such that f is constant on each subspace. In other words\, up to polynomial factors\, deterministic communication complexity and parity decision tree complexity are equivalent. \nThis relies on a novel technique of entropy decrement combined with Sanders’ Bogolyubov-Ruzsa lemma. \nJoint work with Hamed Hatami and Shachar Lovett\n\n\n12:00-1:30pm\nLunch\n\n\n\n1:30-2:20pm\nGuy Kindler\n\nFrom the Grassmann graph to Two-to-Two games \nAbstract: In this work we show a relation between the structure of the so called Grassmann graph over Z_2 and the Two-to-Two conjecture in computational complexity. Specifically\, we present a structural conjecture concerning the Grassmann graph (together with an observation by Barak et. al.\, one can view this as a conjecture about the structure of non-expanding sets in that graph) which turns out to imply the Two-to-Two conjecture. \nThe latter conjecture its the lesser-known and weaker sibling of the Unique-Games conjecture [Khot02]\, which states that unique games (a.k.a. one-to-one games) are hard to approximate. Indeed\, if the Grassmann-Graph conjecture its true\, it would also rule out some attempts to refute the Unique-Games conjecture\, as these attempts provide potentially efficient algorithms to solve unique games\, that would actually also solve two-to-two games if they work at all. \nThese new connections between the structural properties of the Grassmann graph and complexity theoretic conjectures highlight the Grassmann graph as an interesting and worthy object of study. We may indicate some initial results towards analyzing its structure. \nThis is joint work with Irit Dinur\, Subhash Khot\, Dror Minzer\, and Muli Safra. \n\n\n\n\n\n\n\nEvents\,Past Events
URL:https://live-hu-cmsa-222.pantheonsite.io/event/workshop-on-additive-combinatorics-oct-2-6-2017/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170907T170000
DTEND;TZID=America/New_York:20170907T180000
DTSTAMP:20250305T183135Z
CREATED:20230717T172748Z
LAST-MODIFIED:20250305T183135Z
UID:10000035-1504803600-1504807200@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Noga Alon Public Talk
DESCRIPTION:Noga Alon (Tel Aviv University) will be giving a public talk on September 7\, 2017\,as part of the program on combinatorics and complexity hosted by the CMSA during AY17-18.  The talk will be at 5:00pm in Askwith Hall\, 13 Appian Way\, Cambridge\, MA. \nTitle: Graph Coloring: Local and Global \nAbstract: Graph Coloring is arguably the most popular subject in Discrete Mathematics\, and its combinatorial\, algorithmic and computational aspects have been studied intensively. The most basic notion in the area\, the chromatic number of a graph\, is an inherently global property. This is demonstrated by the hardness of computation or approximation of this invariant as well as by the existence of graphs with arbitrarily high chromatic number and no short cycles. The investigation of these graphs had a profound impact on Graph Theory and Combinatorics. It combines combinatorial\, probabilistic\, algebraic and topological techniques with number theoretic tools. I will describe the rich history of the subject focusing on some recent results. \n 
URL:https://live-hu-cmsa-222.pantheonsite.io/event/noga-alon-public-talk-9-7-17/
LOCATION:MA
CATEGORIES:Event,Public Lecture
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Noga-Poster-2-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170818T154700
DTEND;TZID=America/New_York:20170819T154700
DTSTAMP:20250328T144515Z
CREATED:20230717T172600Z
LAST-MODIFIED:20250328T144515Z
UID:10000034-1503071220-1503157620@live-hu-cmsa-222.pantheonsite.io
SUMMARY:2017 Big Data Conference
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a conference on Big Data from August 18 – 19\, 2017\, in Hall D of the Science Center at Harvard University.\nThe Big Data Conference features many speakers from the Harvard community as well as scholars from across the globe\, with talks focusing on computer science\, statistics\, math and physics\, and economics. This is the third conference on Big Data the Center will host as part of our annual events\, and is co-organized by Richard Freeman\, Scott Kominers\, Jun Liu\, Horng-Tzer Yau and Shing-Tung Yau. \nConfirmed Speakers: \n\nMohammad Akbarpour\, Stanford University\nAlbert-László Barabási\, Northeastern University\nNoureddine El Karoui\, University of California\, Berkeley\nRavi Jagadeesan\, Harvard University\nLucas Janson\, Harvard University\nTracy Ke\, University of Chicago\nTze Leung Lai\, Stanford University\nAnnie Liang\, University of Pennsylvania\nMarena Lin\, Harvard University\nNikhil Naik\, Harvard University\nAlex Peysakhovich\, Facebook\nNatesh Pillai\, Harvard University\nJann Spiess\, Harvard University\nBradly Stadie\, Open AI\, University of California\, Berkeley\nZak Stone\, Google\nHau-Tieng Wu\, University of Toronto\nSifan Zhou\, Xiamen University\n\n  \nFollowing the conference\, there will be a two-day workshop from August 20-21. The workshop is organized by Scott Kominers\, and will feature: \n\nJörn Boehnke\, Harvard University\nNikhil Naik\, Harvard University\nBradly Stadie\, Open AI\, University of California\, Berkeley\n\n  \nConference Schedule \nA PDF version of the schedule below can also be downloaded here. \nAugust 18\, Friday (Full day)\n\n\n\nTime\nSpeaker\nTopic\n\n\n8:30 am – 9:00 am\n\nBreakfast\n\n\n9:00 am – 9:40 am\nMohammad Akbarpour \nVideo\nTitle: Information aggregation in overlapping generations and the emergence of experts \nAbstract: We study a model of social learning with “overlapping generations”\, where agents meet others and share data about an underlying state over time. We examine under what conditions the society will produce individuals with precise knowledge about the state of the world. There are two information sharing regimes in our model: Under the full information sharing technology\, individuals exchange the information about their point estimates of an underlying state\, as well as their sources (or the precision of their signals) and update their beliefs by taking a weighted average. Under the limited information sharing technology\, agents only observe the information about the point estimates of those they meet\, and update their beliefs by taking a weighted average\, where weights can depend on the sequence of meetings\, as well as the labels. Our main result shows that\, unlike most social learning settings\, using such linear learning rules do not guide the society (or even a fraction of its members) to learn the truth\, and having access to\, and exploiting knowledge of the precision of a source signal are essential for efficient social learning (joint with Amin Saberi & Ali Shameli).\n\n\n9:40 am – 10:20 am\nLucas Janson \nVideo\nTitle: Model-Free Knockoffs For High-Dimensional Controlled Variable Selection \nAbstract: Many contemporary large-scale applications involve building interpretable models linking a large set of potential covariates to a response in a nonlinear fashion\, such as when the response is binary. Although this modeling problem has been extensively studied\, it remains unclear how to effectively control the fraction of false discoveries even in high-dimensional logistic regression\, not to mention general high-dimensional nonlinear models. To address such a practical problem\, we propose a new framework of model-free knockoffs\, which reads from a different perspective the knockoff procedure (Barber and Candès\, 2015) originally designed for controlling the false discovery rate in linear models. The key innovation of our method is to construct knockoff variables probabilistically instead of geometrically. This enables model-free knockoffs to deal with arbitrary (and unknown) conditional models and any dimensions\, including when the dimensionality p exceeds the sample size n\, while the original knockoffs procedure is constrained to homoscedastic linear models with n greater than or equal to p. Our approach requires the design matrix be random (independent and identically distributed rows) with a covariate distribution that is known\, although we show our procedure to be robust to unknown/estimated distributions. As we require no knowledge/assumptions about the conditional distribution of the response\, we effectively shift the burden of knowledge from the response to the covariates\, in contrast to the canonical model-based approach which assumes a parametric model for the response but very little about the covariates. To our knowledge\, no other procedure solves the controlled variable selection problem in such generality\, but in the restricted settings where competitors exist\, we demonstrate the superior power of knockoffs through simulations. Finally\, we apply our procedure to data from a case-control study of Crohn’s disease in the United Kingdom\, making twice as many discoveries as the original analysis of the same data. \nSlides\n\n\n10:20 am – 10:50 am\n\nBreak\n\n\n10:50 pm – 11:30 pm\nNoureddine El Karoui \nVideo\nTitle: Random matrices and high-dimensional statistics: beyond covariance matrices \nAbstract: Random matrices have played a central role in understanding very important statistical methods linked to covariance matrices (such as Principal Components Analysis\, Canonical Correlation Analysis etc…) for several decades. In this talk\, I’ll show that one can adopt a random-matrix-inspired point of view to understand the performance of other widely used tools in statistics\, such as M-estimators\, and very common methods such as the bootstrap. I will focus on the high-dimensional case\, which captures well the situation of “moderately” difficult statistical problems\, arguably one of the most relevant in practice. In this setting\, I will show that random matrix ideas help upend conventional theoretical thinking (for instance about maximum likelihood methods) and highlight very serious practical problems with resampling methods.\n\n\n11:30 am – 12:10 pm\nNikhil Naik \nVideo\nTitle: Understanding Urban Change with Computer Vision and Street-level Imagery \nAbstract: Which neighborhoods experience physical improvements? In this work\, we introduce a computer vision method to measure changes in the physical appearances of neighborhoods from time-series street-level imagery. We connect changes in the physical appearance of five US cities with economic and demographic data and find three factors that predict neighborhood improvement. First\, neighborhoods that are densely populated by college-educated adults are more likely to experience physical improvements. Second\, neighborhoods with better initial appearances experience\, on average\, larger positive improvements. Third\, neighborhood improvement correlates positively with physical proximity to the central business district and to other physically attractive neighborhoods. Together\, our results illustrate the value of using computer vision methods and street-level imagery to understand the physical dynamics of cities. \n(Joint work with Edward L. Glaeser\, Cesar A. Hidalgo\, Scott Duke Kominers\, and Ramesh Raskar.)\n\n\n12:10 pm – 12:25 pm\nVideo #1 \nVideo #2\nData Science Lightning Talks\n\n\n12:25 pm – 1:30 pm\n\nLunch\n\n\n1:30 pm – 2:10 pm\nTracy Ke \nVideo\nTitle: A new SVD approach to optimal topic estimation \nAbstract: In the probabilistic topic models\, the quantity of interest—a low-rank matrix consisting of topic vectors—is hidden in the text corpus matrix\, masked by noise\, and Singular Value Decomposition (SVD) is a potentially useful tool for learning such a low-rank matrix. However\, the connection between this low-rank matrix and the singular vectors of the text corpus matrix are usually complicated and hard to spell out\, so how to use SVD for learning topic models faces challenges. \nWe overcome the challenge by revealing a surprising insight: there is a low-dimensional simplex structure which can be viewed as a bridge between the low-rank matrix of interest and the SVD of the text corpus matrix\, and which allows us to conveniently reconstruct the former using the latter. Such an insight motivates a new SVD-based approach to learning topic models. \nFor asymptotic analysis\, we show that under a popular topic model (Hofmann\, 1999)\, the convergence rate of the l1-error of our method matches that of the minimax lower bound\, up to a multi-logarithmic term. In showing these results\, we have derived new element-wise bounds on the singular vectors and several large deviation bounds for weakly dependent multinomial data. Our results on the convergence rate and asymptotical minimaxity are new. We have applied our method to two data sets\, Associated Process (AP) and Statistics Literature Abstract (SLA)\, with encouraging results. In particular\, there is a clear simplex structure associated with the SVD of the data matrices\, which largely validates our discovery.\n\n\n2:10 pm – 2:50 pm\nAlbert-László Barabási \nVideo\nTitle: Taming Complexity: From Network Science to Controlling Networks \nAbstract: The ultimate proof of our understanding of biological or technological systems is reflected in our ability to control them. While control theory offers mathematical tools to steer engineered and natural systems towards a desired state\, we lack a framework to control complex self-organized systems. Here we explore the controllability of an arbitrary complex network\, identifying the set of driver nodes whose time-dependent control can guide the system’s entire dynamics. We apply these tools to several real networks\, unveiling how the network topology determines its controllability. Virtually all technological and biological networks must be able to control their internal processes. Given that\, issues related to control deeply shape the topology and the vulnerability of real systems. Consequently unveiling the control principles of real networks\, the goal of our research\, forces us to address series of fundamental questions pertaining to our understanding of complex systems. \n \n\n\n2:50 pm – 3:20 pm\n\nBreak\n\n\n3:20 pm – 4:00 pm\nMarena Lin \nVideo\nTitle: Optimizing climate variables for human impact studies \nAbstract: Estimates of the relationship between climate variability and socio-economic outcomes are often limited by the spatial resolution of the data. As studies aim to generalize the connection between climate and socio-economic outcomes across countries\, the best available socio-economic data is at the national level (e.g. food production quantities\, the incidence of warfare\, averages of crime incidence\, gender birth ratios). While these statistics may be trusted from government censuses\, the appropriate metric for the corresponding climate or weather for a given year in a country is less obvious. For example\, how do we estimate the temperatures in a country relevant to national food production and therefore food security? We demonstrate that high-resolution spatiotemporal satellite data for vegetation can be used to estimate the weather variables that may be most relevant to food security and related socio-economic outcomes. In particular\, satellite proxies for vegetation over the African continent reflect the seasonal movement of the Intertropical Convergence Zone\, a band of intense convection and rainfall. We also show that agricultural sensitivity to climate variability differs significantly between countries. This work is an example of the ways in which in-situ and satellite-based observations are invaluable to both estimates of future climate variability and to continued monitoring of the earth-human system. We discuss the current state of these records and potential challenges to their continuity.\n\n\n4:00 pm – 4:40 pm\nAlex Peysakhovich\n Title: Building a cooperator \nAbstract: A major goal of modern AI is to construct agents that can perform complex tasks. Much of this work deals with single agent decision problems. However\, agents are rarely alone in the world. In this talk I will discuss how to combine ideas from deep reinforcement learning and game theory to construct artificial agents that can communicate\, collaborate and cooperate in productive positive sum interactions.\n\n\n4:40 pm – 5:20 pm\nTze Leung Lai \nVideo\nTitle: Gradient boosting: Its role in big data analytics\, underlying mathematical theory\, and recent refinements \nAbstract: We begin with a review of the history of gradient boosting\, dating back to the LMS algorithm of Widrow and Hoff in 1960 and culminating in Freund and Schapire’s AdaBoost and Friedman’s gradient boosting and stochastic gradient boosting algorithms in the period 1999-2002 that heralded the big data era. The role played by gradient boosting in big data analytics\, particularly with respect to deep learning\, is then discussed. We also present some recent work on the mathematical theory of gradient boosting\, which has led to some refinements that greatly improves the convergence properties and prediction performance of the methodology.\n\n\n\nAugust 19\, Saturday (Full day)\n\n\n\nTime\nSpeaker\nTopic\n\n\n8:30 am – 9:00 am\n\nBreakfast\n\n\n9:00 am – 9:40 am\nNatesh Pillai \nVideo\nTitle: Accelerating MCMC algorithms for Computationally Intensive Models via Local Approximations \nAbstract: We construct a new framework for accelerating Markov chain Monte Carlo in posterior sampling problems where standard methods are limited by the computational cost of the likelihood\, or of numerical models embedded therein. Our approach introduces local approximations of these models into the Metropolis–Hastings kernel\, borrowing ideas from deterministic approximation theory\, optimization\, and experimental design. Previous efforts at integrating approximate models into inference typically sacrifice either the sampler’s exactness or efficiency; our work seeks to address these limitations by exploiting useful convergence characteristics of local approximations. We prove the ergodicity of our approximate Markov chain\, showing that it samples asymptotically from the exact posterior distribution of interest. We describe variations of the algorithm that employ either local polynomial approximations or local Gaussian process regressors. Our theoretical results reinforce the key observation underlying this article: when the likelihood has some local regularity\, the number of model evaluations per Markov chain Monte Carlo (MCMC) step can be greatly reduced without biasing the Monte Carlo average. Numerical experiments demonstrate multiple order-of-magnitude reductions in the number of forward model evaluations used in representative ordinary differential equation (ODE) and partial differential equation (PDE) inference problems\, with both synthetic and real data.\n\n\n9:40 am – 10:20 am\nRavi Jagadeesan \nVideo\nTitle: Designs for estimating the treatment effect in networks with interference \nAbstract: In this paper we introduce new\, easily implementable designs for drawing causal inference from randomized experiments on networks with interference. Inspired by the idea of matching in observational studies\, we introduce the notion of considering a treatment assignment as a quasi-coloring” on a graph. Our idea of a perfect quasi-coloring strives to match every treated unit on a given network with a distinct control unit that has identical number of treated and control neighbors. For a wide range of interference functions encountered in applications\, we show both by theory and simulations that the classical Neymanian estimator for the direct effect has desirable properties for our designs. This further extends to settings where homophily is present in addition to interference.\n\n\n10:20 am – 10:50 am\n\nBreak\n\n\n10:50 am – 11:30 am\nAnnie Liang \nVideo\nTitle: The Theory is Predictive\, but is it Complete? An Application to Human Generation of Randomness \nAbstract: When we test a theory using data\, it is common to focus on correctness: do the predictions of the theory match what we see in the data? But we also care about completeness: how much of the predictable variation in the data is captured by the theory? This question is difficult to answer\, because in general we do not know how much “predictable variation” there is in the problem. In this paper\, we consider approaches motivated by machine learning algorithms as a means of constructing a benchmark for the best attainable level of prediction.  We illustrate our methods on the task of predicting human-generated random sequences. Relative to a theoretical machine learning algorithm benchmark\, we find that existing behavioral models explain roughly 15 percent of the predictable variation in this problem. This fraction is robust across several variations on the problem. We also consider a version of this approach for analyzing field data from domains in which human perception and generation of randomness has been used as a conceptual framework; these include sequential decision-making and repeated zero-sum games. In these domains\, our framework for testing the completeness of theories provides a way of assessing their effectiveness over different contexts; we find that despite some differences\, the existing theories are fairly stable across our field domains in their performance relative to the benchmark. Overall\, our results indicate that (i) there is a significant amount of structure in this problem that existing models have yet to capture and (ii) there are rich domains in which machine learning may provide a viable approach to testing completeness (joint with Jon Kleinberg and Sendhil Mullainathan).\n\n\n11:30 am – 12:10 pm\nZak Stone \nVideo\nTitle: TensorFlow: Machine Learning for Everyone \nAbstract: We’ve witnessed extraordinary breakthroughs in machine learning over the past several years. What kinds of things are possible now that weren’t possible before? How are open-source platforms like TensorFlow and hardware platforms like GPUs and Cloud TPUs accelerating machine learning progress? If these tools are new to you\, how should you get started? In this session\, you’ll hear about all of this and more from Zak Stone\, the Product Manager for TensorFlow on the Google Brain team.\n\n\n12:10 pm – 1:30 pm\n\nLunch\n\n\n1:30 pm – 2:10 pm\nJann Spiess \nVideo\nTitle: (Machine) Learning to Control in Experiments \nAbstract: Machine learning focuses on high-quality prediction rather than on (unbiased) parameter estimation\, limiting its direct use in typical program evaluation applications. Still\, many estimation tasks have implicit prediction components. In this talk\, I discuss accounting for controls in treatment effect estimation as a prediction problem. In a canonical linear regression framework with high-dimensional controls\, I argue that OLS is dominated by a natural shrinkage estimator even for unbiased estimation when treatment is random; suggest a generalization that relaxes some parametric assumptions; and contrast my results with that for another implicit prediction problem\, namely the first stage of an instrumental variables regression.\n\n\n2:10 pm – 2:50 pm\nBradly Stadie\nTitle: Learning to Learn Quickly: One-Shot Imitation and Meta Learning \nAbstract: Many reinforcement learning algorithms are bottlenecked by data collection costs and the brittleness of their solutions when faced with novel scenarios.\nWe will discuss two techniques for overcoming these shortcomings. In one-shot imitation\, we train a module that encodes a single demonstration of a desired behavior into a vector containing the essence of the demo. This vector can subsequently be utilized to recover the demonstrated behavior. In meta-learning\, we optimize a policy under the objective of learning to learn new tasks quickly. We show meta-learning methods can be accelerated with the use of auxiliary objectives. Results are presented on grid worlds\, robotics tasks\, and video game playing tasks.\n\n\n2:50 pm – 3:20 pm\n\nBreak\n\n\n3:20 pm – 4:00 pm\nHau-Tieng Wu \nVideo\nTitle: When Medical Challenges Meet Modern Data Science \nAbstract: Adaptive acquisition of correct features from massive datasets is at the core of modern data analysis. One particular interest in medicine is the extraction of hidden dynamics from a single observed time series composed of multiple oscillatory signals\, which could be viewed as a single-channel blind source separation problem. The mathematical and statistical problems are made challenging by the structure of the signal which consists of non-sinusoidal oscillations with time varying amplitude/frequency\, and by the heteroscedastic nature of the noise. In this talk\, I will discuss recent progress in solving this kind of problem by combining the cepstrum-based nonlinear time-frequency analysis and manifold learning technique. A particular solution will be given along with its theoretical properties. I will also discuss the application of this method to two medical problems – (1) the extraction of a fetal ECG signal from a single lead maternal abdominal ECG signal; (2) the simultaneous extraction of the instantaneous heart/respiratory rate from a PPG signal during exercise; (3) (optional depending on time) an application to atrial fibrillation signals. If time permits\, the clinical trial results will be discussed.\n\n\n4:00 pm – 4:40 pm\nSifan Zhou \nVideo\nTitle: Citing People Like Me: Homophily\, Knowledge Spillovers\, and Continuing a Career in Science \nAbstract: Forward citation is widely used to measure the scientific merits of articles. This research studies millions of journal article citation records in life sciences from MEDLINE and finds that authors of the same gender\, the same ethnicity\, sharing common collaborators\, working in the same institution\, or being geographically close are more likely (and quickly) to cite each other than predicted by their proportion among authors working on the same research topics. This phenomenon reveals how social and geographic distances influence the quantity and speed of knowledge spillovers. Given the importance of forward citations in academic evaluation system\, citation homophily potentially put authors from minority group at a disadvantage. I then show how it influences scientists’ chances to survive in the academia and continue publishing. Based on joint work with Richard Freeman.\n\n\n\n  \nTo view photos and video interviews from the conference\, please visit the CMSA blog. \n\n \n\n  \n\n\n\nBig Data\,CMSA\,Harvard\,Math\nEvents\,Past Events
URL:https://live-hu-cmsa-222.pantheonsite.io/event/2017-big-data-conference-aug-18-19/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Big Data Conference,Conference,Event
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Big-Data-2017_2.png
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