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SUMMARY:Symposium on Foundations of Responsible Computing (FORC)
DESCRIPTION:On June 6-8\, 2022\, the CMSA hosted the 3rd annual Symposium on Foundations of Responsible Computing (FORC). \nThe Symposium on Foundations of Responsible Computing (FORC) is a forum for mathematical research in computation and society writ large.  The Symposium aims to catalyze the formation of a community supportive of the application of theoretical computer science\, statistics\, economics and other relevant analytical fields to problems of pressing and anticipated societal concern. \nOrganizers: Cynthia Dwork\, Harvard SEAS | Omer Reingold\, Stanford | Elisa Celis\, Yale \nSchedule\nJune 6\, 2022 \n\n\n\n\n9:15 am–10:15 am\nOpening Remarks \nKeynote Speaker: Caroline Nobo\, Yale University\nTitle: From Theory to Impact: Why Better Data Systems are Necessary for Criminal Legal Reform \nAbstract: This talk will dive into the messy\, archaic\, and siloed world of local criminal justice data in America. We will start with a 30\,000 foot discussion about the current state of criminal legal data systems\, then transition to the challenges of this broken paradigm\, and conclude with a call to measure new things – and to measure them better! This talk will leave you with an understanding of criminal justice data infrastructure and transparency in the US\, and will discuss how expensive case management software and other technology are built on outdated normative values which impede efforts to reform the system. The result is an infuriating paradox: an abundance of tech products built without theoretical grounding\, in a space rich with research and evidence.\n\n\n10:15 am–10:45 am\nCoffee Break\n\n\n\n10:45 am–12:15 pm\nPaper Session 1\nSession Chair: Ruth Urner\n\n\n\nGeorgy Noarov\, University of Pennsylvania\nTitle: Online Minimax Multiobjective Optimization \nAbstract: We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round. The learner’s objective is to minimize the maximum cumulative loss over all coordinates. We give a simple algorithm that lets the learner do almost as well as if she knew the adversary’s actions in advance. We demonstrate the power of our framework by using it to (re)derive optimal bounds and efficient algorithms across a variety of domains\, ranging from multicalibration to a large set of no-regret algorithms\, to a variant of Blackwell’s approachability theorem for polytopes with fast convergence rates. As a new application\, we show how to “(multi)calibeat” an arbitrary collection of forecasters — achieving an exponentially improved dependence on the number of models we are competing against\, compared to prior work.\n\n\n\nMatthew Eichhorn\, Cornell University\nTitle: Mind your Ps and Qs: Allocation with Priorities and Quotas \nAbstract: In many settings\, such as university admissions\, the rationing of medical supplies\, and the assignment of public housing\, decision-makers use normative criteria (ethical\, financial\, legal\, etc.) to justify who gets an allocation. These criteria can often be translated into quotas for the number of units available to particular demographics and priorities over agents who qualify in each demographic. Each agent may qualify in multiple categories at different priority levels\, so many allocations may conform to a given set of quotas and priorities. Which of these allocations should be chosen? In this talk\, I’ll formalize this reserve allocation problem and motivate Pareto efficiency as a natural desideratum. I’ll present an algorithm to locate efficient allocations that conform to the quota and priority constraints. This algorithm relies on beautiful techniques from integer and linear programming\, and it is both faster and more straightforward than existing techniques in this space. Moreover\, its clean formulation allows for further refinement\, such as the secondary optimization of some heuristics for fairness.\n\n\n\nHaewon Jeong\, Harvard University\nTitle: Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values \nAbstract: We investigate the fairness concerns of training a machine learning model using data with missing values. Even though there are a number of fairness intervention methods in the literature\, most of them require a complete training set as input. In practice\, data can have missing values\, and data missing patterns can depend on group attributes (e.g. gender or race). Simply applying off-the-shelf fair learning algorithms to an imputed dataset may lead to an unfair model. In this paper\, we first theoretically analyze different sources of discrimination risks when training with an imputed dataset. Then\, we propose an integrated approach based on decision trees that does not require a separate process of imputation and learning. Instead\, we train a tree with missing incorporated as attribute (MIA)\, which does not require explicit imputation\, and we optimize a fairness-regularized objective function. We demonstrate that our approach outperforms existing fairness intervention methods applied to an imputed dataset\, through several experiments on real-world datasets.\n\n\n\nEmily Diana\, University of Pennsylvania\nTitle: Multiaccurate Proxies for Downstream Fairness \nAbstract: We study the problem of training a model that must obey demographic fairness conditions when the sensitive features are not available at training time — in other words\, how can we train a model to be fair by race when we don’t have data about race? We adopt a fairness pipeline perspective\, in which an “upstream” learner that does have access to the sensitive features will learn a proxy model for these features from the other attributes. The goal of the proxy is to allow a general “downstream” learner — with minimal assumptions on their prediction task — to be able to use the proxy to train a model that is fair with respect to the true sensitive features. We show that obeying multiaccuracy constraints with respect to the downstream model class suffices for this purpose\, provide sample- and oracle efficient-algorithms and generalization bounds for learning such proxies\, and conduct an experimental evaluation. In general\, multiaccuracy is much easier to satisfy than classification accuracy\, and can be satisfied even when the sensitive features are hard to predict.\n\n\n12:15 pm–1:45 pm\nLunch Break\n\n\n\n1:45–3:15 pm\nPaper Session 2\nSession Chair: Guy Rothblum\n\n\n\nElbert Du\, Harvard University\nTitle: Improved Generalization Guarantees in Restricted Data Models \nAbstract: Differential privacy is known to protect against threats to validity incurred due to adaptive\, or exploratory\, data analysis — even when the analyst adversarially searches for a statistical estimate that diverges from the true value of the quantity of interest on the underlying population. The cost of this protection is the accuracy loss incurred by differential privacy. In this work\, inspired by standard models in the genomics literature\, we consider data models in which individuals are represented by a sequence of attributes with the property that where distant attributes are only weakly correlated. We show that\, under this assumption\, it is possible to “re-use” privacy budget on different portions of the data\, significantly improving accuracy without increasing the risk of overfitting.\n\n\n\nRuth Urner\, York University\nTitle: Robustness Should not be at Odds with Accuracy \nAbstract: The phenomenon of adversarial examples in deep learning models has caused substantial concern over their reliability and trustworthiness: in many instances an imperceptible perturbation can falsely flip a neural network’s prediction. Applied research in this area has mostly focused on developing novel adversarial attack strategies or building better defenses against such. It has repeatedly been pointed out that adversarial robustness may be in conflict with requirements for high accuracy. In this work\, we take a more principled look at modeling the phenomenon of adversarial examples. We argue that deciding whether a model’s label change under a small perturbation is justified\, should be done in compliance with the underlying data-generating process. Through a series of formal constructions\, systematically analyzing the the relation between standard Bayes classifiers and robust-Bayes classifiers\, we make the case for adversarial robustness as a locally adaptive measure. We propose a novel way defining such a locally adaptive robust loss\, show that it has a natural empirical counterpart\, and develop resulting algorithmic guidance in form of data-informed adaptive robustness radius. We prove that our adaptive robust data-augmentation maintains consistency of 1-nearest neighbor classification under deterministic labels and thereby argue that robustness should not be at odds with accuracy.\n\n\n\nSushant Agarwal\, University of Waterloo\nTitle: Towards the Unification and Robustness of Perturbation and Gradient Based Explanations \nAbstract: As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice\, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work\, we analyze two popular post hoc interpretation techniques: SmoothGrad which is a gradient based method\, and a variant of LIME which is a perturbation based method. More specifically\, we derive explicit closed form expressions for the explanations output by these two methods and show that they both converge to the same explanation in expectation\, i.e.\, when the number of perturbed samples used by these methods is large. We then leverage this connection to establish other desirable properties\, such as robustness and linearity\, for these techniques. We also derive finite sample complexity bounds for the number of perturbations required for these methods to converge to their expected explanation. Finally\, we empirically validate our theory using extensive experimentation on both synthetic and real world datasets.\n\n\n\nTijana Zrnic\, University of California\, Berkeley\nTitle: Regret Minimization with Performative Feedback \nAbstract: In performative prediction\, the deployment of a predictive model triggers a shift in the data distribution. As these shifts are typically unknown ahead of time\, the learner needs to deploy a model to get feedback about the distribution it induces. We study the problem of finding near-optimal models under performativity while maintaining low regret. On the surface\, this problem might seem equivalent to a bandit problem. However\, it exhibits a fundamentally richer feedback structure that we refer to as performative feedback: after every deployment\, the learner receives samples from the shifted distribution rather than only bandit feedback about the reward. Our main contribution is regret bounds that scale only with the complexity of the distribution shifts and not that of the reward function. The key algorithmic idea is careful exploration of the distribution shifts that informs a novel construction of confidence bounds on the risk of unexplored models. The construction only relies on smoothness of the shifts and does not assume convexity. More broadly\, our work establishes a conceptual approach for leveraging tools from the bandits literature for the purpose of regret minimization with performative feedback.\n\n\n3:15 pm–3:45 pm\nCoffee Break\n\n\n\n3:45 pm–5:00 pm\nPanel Discussion\nTitle: What is Responsible Computing? \nPanelists: Jiahao Chen\, Cynthia Dwork\, Kobbi Nissim\, Ruth Urner \nModerator: Elisa Celis\n\n\n\n\n  \nJune 7\, 2022 \n\n\n\n\n9:15 am–10:15 am\nKeynote Speaker: Isaac Kohane\, Harvard Medical School\nTitle: What’s in a label? The case for and against monolithic group/ethnic/race labeling for machine learning \nAbstract: Populations and group labels have been used and abused for thousands of years. The scale at which AI can incorporate such labels into its models and the ways in which such models can be misused are cause for significant concern. I will describe\, with examples drawn from experiments in precision medicine\, the task dependence of how underserved and oppressed populations can be both harmed and helped by the use of group labels. The source of the labels and the utility models underlying their use will be particularly emphasized.\n\n\n10:15 am–10:45 am\nCoffee Break\n\n\n\n10:45 am–12:15 pm\nPaper Session 3\nSession Chair: Ruth Urner\n\n\n\nRojin Rezvan\, University of Texas at Austin\nTitle: Individually-Fair Auctions for Multi-Slot Sponsored Search \nAbstract: We design fair-sponsored search auctions that achieve a near-optimal tradeoff between fairness and quality. Our work builds upon the model and auction design of Chawla and Jagadeesan\, who considered the special case of a single slot. We consider sponsored search settings with multiple slots and the standard model of click-through rates that are multiplicatively separable into an advertiser-specific component and a slot-specific component. When similar users have similar advertiser-specific click-through rates\, our auctions achieve the same near-optimal tradeoff between fairness and quality. When similar users can have different advertiser-specific preferences\, we show that a preference-based fairness guarantee holds. Finally\, we provide a computationally efficient algorithm for computing payments for our auctions as well as those in previous work\, resolving another open direction from Chawla and Jagadeesan.\n\n\n\nJudy Hanwen Shen\, Stanford\nTitle: Leximax Approximations and Representative Cohort Selection \nAbstract: Finding a representative cohort from a broad pool of candidates is a goal that arises in many contexts such as choosing governing committees and consumer panels. While there are many ways to define the degree to which a cohort represents a population\, a very appealing solution concept is lexicographic maximality (leximax) which offers a natural (pareto-optimal like) interpretation that the utility of no population can be increased without decreasing the utility of a population that is already worse off. However\, finding a leximax solution can be highly dependent on small variations in the utility of certain groups. In this work\, we explore new notions of approximate leximax solutions with three distinct motivations: better algorithmic efficiency\, exploiting significant utility improvements\, and robustness to noise. Among other definitional contributions\, we give a new notion of an approximate leximax that satisfies a similarly appealing semantic interpretation and relate it to algorithmically-feasible approximate leximax notions. When group utilities are linear over cohort candidates\, we give an efficient polynomial-time algorithm for finding a leximax distribution over cohort candidates in the exact as well as in the approximate setting. Furthermore\, we show that finding an integer solution to leximax cohort selection with linear utilities is NP-Hard.\n\n\n\nJiayuan Ye\,\nNational University of Singapore\nTitle: Differentially Private Learning Needs Hidden State (or Much Faster Convergence) \nAbstract: Differential privacy analysis of randomized learning algorithms typically relies on composition theorems\, where the implicit assumption is that the internal state of the iterative algorithm is revealed to the adversary. However\, by assuming hidden states for DP algorithms (when only the last-iterate is observable)\, recent works prove a converging privacy bound for noisy gradient descent (on strongly convex smooth loss function) that is significantly smaller than composition bounds after a few epochs. In this talk\, we extend this hidden-state analysis to various stochastic minibatch gradient descent schemes (such as under “shuffle and partition” and “sample without replacement”)\, by deriving novel bounds for the privacy amplification by random post-processing and subsampling. We prove that\, in these settings\, our privacy bound is much smaller than composition for training with a large number of iterations (which is the case for learning from high-dimensional data). Our converging privacy analysis\, thus\, shows that differentially private learning\, with a tight bound\, needs hidden state privacy analysis or a fast convergence. To complement our theoretical results\, we present experiments for training classification models on MNIST\, FMNIST and CIFAR-10 datasets\, and observe a better accuracy given fixed privacy budgets\, under the hidden-state analysis.\n\n\n\nMahbod Majid\, University of Waterloo\nTitle: Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism \nAbstract: We give the first polynomial-time algorithm to estimate the mean of a d-variate probability distribution from O(d) independent samples (up to logarithmic factors) subject to pure differential privacy. \nOur main technique is a new approach to use the powerful Sum of Squares method (SoS) to design differentially private algorithms. SoS proofs to algorithms is a key theme in numerous recent works in high-dimensional algorithmic statistics – estimators which apparently require exponential running time but whose analysis can be captured by low-degree Sum of Squares proofs can be automatically turned into polynomial-time algorithms with the same provable guarantees. We demonstrate a similar proofs to private algorithms phenomenon: instances of the workhorse exponential mechanism which apparently require exponential time but which can be analyzed with low-degree SoS proofs can be automatically turned into polynomial-time differentially private algorithms. We prove a meta-theorem capturing this phenomenon\, which we expect to be of broad use in private algorithm design.\n\n\n12:15 pm–1:45 pm\nLunch Break\n\n\n\n1:45–3:15 pm\nPaper Session 4\nSession Chair: Kunal Talwar\n\n\n\nKunal Talwar\,\nApple\nTitle: Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation \nAbstract: Computing the noisy sum of real-valued vectors is an important primitive in differentially private learning and statistics. In private federated learning applications\, these vectors are held by client devices\, leading to a distributed summation problem. Standard Secure Multiparty Computation (SMC) protocols for this problem are susceptible to poisoning attacks\, where a client may have a large influence on the sum\, without being detected.\nIn this work\, we propose a poisoning-robust private summation protocol in the multiple-server setting\, recently studied in PRIO. We present a protocol for vector summation that verifies that the Euclidean norm of each contribution is approximately bounded. We show that by relaxing the security constraint in SMC to a differential privacy like guarantee\, one can improve over PRIO in terms of communication requirements as well as the client-side computation. Unlike SMC algorithms that inevitably cast integers to elements of a large finite field\, our algorithms work over integers/reals\, which may allow for additional efficiencies.\n\n\n\nGiuseppe Vietri\, University of Minnesota\nTitle: Improved Regret for Differentially Private Exploration in Linear MDP \nAbstract: We study privacy-preserving exploration in sequential decision-making for environments that rely on sensitive data such as medical records. In particular\, we focus on solving the problem of reinforcement learning (RL) subject to the constraint of (joint) differential privacy in the linear MDP setting\, where both dynamics and rewards are given by linear functions. Prior work on this problem due to Luyo et al. (2021) achieves a regret rate that has a dependence of O(K^{3/5}) on the number of episodes K. We provide a private algorithm with an improved regret rate with an optimal dependence of O(K^{1/2}) on the number of episodes. The key recipe for our stronger regret guarantee is the adaptivity in the policy update schedule\, in which an update only occurs when sufficient changes in the data are detected. As a result\, our algorithm benefits from low switching cost and only performs O(log(K)) updates\, which greatly reduces the amount of privacy noise. Finally\, in the most prevalent privacy regimes where the privacy parameter ? is a constant\, our algorithm incurs negligible privacy cost — in comparison with the existing non-private regret bounds\, the additional regret due to privacy appears in lower-order terms.\n\n\n\nMingxun Zhou\,\nCarnegie Mellon University\nTitle: The Power of the Differentially Oblivious Shuffle in Distributed Privacy MechanismsAbstract: The shuffle model has been extensively investigated in the distributed differential privacy (DP) literature. For a class of useful computational tasks\, the shuffle model allows us to achieve privacy-utility tradeoff similar to those in the central model\, while shifting the trust from a central data curator to a “trusted shuffle” which can be implemented through either trusted hardware or cryptography. Very recently\, several works explored cryptographic instantiations of a new type of shuffle with relaxed security\, called differentially oblivious (DO) shuffles. These works demonstrate that by relaxing the shuffler’s security from simulation-style secrecy to differential privacy\, we can achieve asymptotical efficiency improvements. A natural question arises\, can we replace the shuffler in distributed DP mechanisms with a DO-shuffle while retaining a similar privacy-utility tradeoff?\nIn this paper\, we prove an optimal privacy amplification theorem by composing any locally differentially private (LDP) mechanism with a DO-shuffler\, achieving parameters that tightly match the shuffle model. Moreover\, we explore multi-message protocols in the DO-shuffle model\, and construct mechanisms for the real summation and histograph problems. Our error bounds approximate the best known results in the multi-message shuffle-model up to sub-logarithmic factors. Our results also suggest that just like in the shuffle model\, allowing each client to send multiple messages is fundamentally more powerful than restricting to a single message.\n\n\n\nBadih Ghazi\,\nGoogle Research\nTitle: Differentially Private Ad Conversion Measurement \nAbstract: In this work\, we study conversion measurement\, a central functionality in the digital advertising space\, where an advertiser seeks to estimate advertiser site conversions attributed to ad impressions that users have interacted with on various publisher sites. We consider differential privacy (DP)\, a notion that has gained in popularity due to its strong and rigorous guarantees\, and suggest a formal framework for DP conversion measurement\, uncovering a subtle interplay between attribution and privacy. We define the notion of an operationally valid configuration of the attribution logic\, DP adjacency relation\, privacy\nbudget scope and enforcement point\, and provide\, for a natural space of configurations\, a complete characterization.\n\n\n3:15 pm–3:45 pm\nCoffee Break\n\n\n\n3:45 pm–5:00 pm\nOpen Poster Session\n\n\n\n\n\n  \nJune 8\, 2022 \n\n\n\n\n9:15 am–10:15 am\nKeynote Speaker: Nuria Oliver\, Data-Pop Alliance\nTitle: Data Science against COVID-19 \nAbstract: In my talk\, I will describe the work that I have been doing since March 2020\, leading a multi-disciplinary team of 20+ volunteer scientists working very closely with the Presidency of the Valencian Government in Spain on 4 large areas: (1) human mobility modeling; (2) computational epidemiological models (both metapopulation\, individual and LSTM-based models); (3) predictive models; and (4) citizen surveys via the COVID19impactsurvey with over 600\,000 answers worldwide. \nI will describe the results that we have produced in each of these areas\, including winning the 500K XPRIZE Pandemic Response Challenge and best paper award at ECML-PKDD 2021. I will share the lessons learned in this very special initiative of collaboration between the civil society at large (through the survey)\, the scientific community (through the Expert Group) and a public administration (through the Commissioner at the Presidency level). WIRED magazine just published an article describing our story.\n\n\n10:15 am–10:45 am\nCoffee Break\n\n\n\n10:45 am–12:15 pm\nPaper Session 5\nSession Chair: Kunal Talwar\n\n\n\nShengyuan Hu\, Carnegie Mellon University\nTitle: Private Multi-Task Learning: Formulation and Applications to Federated Learning \nAbstract: Many problems in machine learning rely on multi-task learning (MTL)\, in which the goal is to solve multiple related machine learning tasks simultaneously. MTL is particularly relevant for privacy-sensitive applications in areas such as healthcare\, finance\, and IoT computing\, where sensitive data from multiple\, varied sources are shared for the purpose of learning. In this work\, we formalize notions of task-level privacy for MTL via joint differential privacy (JDP)\, a relaxation of differential privacy for mechanism design and distributed optimization. We then propose an algorithm for mean-regularized MTL\, an objective commonly used for applications in personalized federated learning\, subject to JDP. We analyze our objective and solver\, providing certifiable guarantees on both privacy and utility. Empirically\, our method allows for improved privacy/utility trade-offs relative to global baselines across common federated learning benchmarks\n\n\n\nChristina Yu\,\nCornell University\nTitle: Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve \nAbstract: We consider the problem of dividing limited resources to individuals arriving over T rounds with a goal of achieving fairness across individuals. In general there may be multiple resources and multiple types of individuals with different utilities. A standard definition of `fairness’ requires an allocation to simultaneously satisfy envy-freeness and Pareto efficiency. However\, in the online sequential setting\, the social planner must decide on a current allocation before the downstream demand is realized\, such that no policy can guarantee these desiderata simultaneously with probability 1\, requiring a modified metric of measuring fairness for online policies. We show that in the online setting\, the two desired properties (envy-freeness and efficiency) are in direct contention\, in that any algorithm achieving additive counterfactual envy-freeness up to L_T necessarily suffers an efficiency loss of at least 1 / L_T. We complement this uncertainty principle with a simple algorithm\, HopeGuardrail\, which allocates resources based on an adaptive threshold policy and is able to achieve any fairness-efficiency point on this frontier. Our result is the first to provide guarantees for fair online resource allocation with high probability for multiple resource and multiple type settings. In simulation results\, our algorithm provides allocations close to the optimal fair solution in hindsight\, motivating its use in practical applications as the algorithm is able to adapt to any desired fairness efficiency trade-off.\n\n\n\nHedyeh Beyhaghi\, Carnegie Mellon University\nTitle: On classification of strategic agents who can both game and improve \nAbstract: In this work\, we consider classification of agents who can both game and improve. For example\, people wishing to get a loan may be able to take some actions that increase their perceived credit-worthiness and others that also increase their true credit-worthiness. A decision-maker would like to define a classification rule with few false-positives (does not give out many bad loans) while yielding many true positives (giving out many good loans)\, which includes encouraging agents to improve to become true positives if possible. We consider two models for this problem\, a general discrete model and a linear model\, and prove algorithmic\, learning\, and hardness results for each. For the general discrete model\, we give an efficient algorithm for the problem of maximizing the number of true positives subject to no false positives\, and show how to extend this to a partial-information learning setting. We also show hardness for the problem of maximizing the number of true positives subject to a nonzero bound on the number of false positives\, and that this hardness holds even for a finite-point version of our linear model. We also show that maximizing the number of true positives subject to no false positive is NP-hard in our full linear model. We additionally provide an algorithm that determines whether there exists a linear classifier that classifies all agents accurately and causes all improvable agents to become qualified\, and give additional results for low-dimensional data.\n\n\n\nKeegan Harris\, Carnegie Mellon University\nTitle: Bayesian Persuasion for Algorithmic Recourse \nAbstract: When subjected to automated decision-making\, decision subjects may strategically modify their observable features in ways they believe will maximize their chances of receiving a favorable decision. In many practical situations\, the underlying assessment rule is deliberately kept secret to avoid gaming and maintain competitive advantage. The resulting opacity forces the decision subjects to rely on incomplete information when making strategic feature modifications. We capture such settings as a game of Bayesian persuasion\, in which the decision maker offers a form of recourse to the decision subject by providing them with an action recommendation (or signal) to incentivize them to modify their features in desirable ways. We show that when using persuasion\, both the decision maker and decision subject are never worse off in expectation\, while the decision maker can be significantly better off. While the decision maker’s problem of finding the optimal Bayesian incentive-compatible (BIC) signaling policy takes the form of optimization over infinitely-many variables\, we show that this optimization can be cast as a linear program over finitely-many regions of the space of possible assessment rules. While this reformulation simplifies the problem dramatically\, solving the linear program requires reasoning about exponentially-many variables\, even under relatively simple settings. Motivated by this observation\, we provide a polynomial-time approximation scheme that recovers a near-optimal signaling policy. Finally\, our numerical simulations on semi-synthetic data empirically illustrate the benefits of using persuasion in the algorithmic recourse setting.\n\n\n12:15 pm–1:45 pm\nLunch Break\n\n\n\n1:45–3:15 pm\nPaper Session 6\nSession Chair: Elisa Celis\n\n\n\nMark Bun\, Boston University\nTitle: Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling \nAbstract: Sampling schemes are fundamental tools in statistics\, survey design\, and algorithm design. A fundamental result in differential privacy is that a differentially private mechanism run on a simple random sample of a population provides stronger privacy guarantees than the same algorithm run on the entire population. However\, in practice\, sampling designs are often more complex than the simple\, data-independent sampling schemes that are addressed in prior work. In this work\, we extend the study of privacy amplification results to more complex\, data-dependent sampling schemes. We find that not only do these sampling schemes often fail to amplify privacy\, they can actually result in privacy degradation. We analyze the privacy implications of the pervasive cluster sampling and stratified sampling paradigms\, as well as provide some insight into the study of more general sampling designs.\n\n\n\nSamson Zhou\, Carnegie Mellon University\nTitle: Private Data Stream Analysis for Universal Symmetric Norm Estimation \nAbstract: We study how to release summary statistics on a data stream subject to the constraint of differential privacy. In particular\, we focus on releasing the family of symmetric norms\, which are invariant under sign-flips and coordinate-wise permutations on an input data stream and include L_p norms\, k-support norms\, top-k norms\, and the box norm as special cases. Although it may be possible to design and analyze a separate mechanism for each symmetric norm\, we propose a general parametrizable framework that differentially privately releases a number of sufficient statistics from which the approximation of all symmetric norms can be simultaneously computed. Our framework partitions the coordinates of the underlying frequency vector into different levels based on their magnitude and releases approximate frequencies for the “heavy” coordinates in important levels and releases approximate level sizes for the “light” coordinates in important levels. Surprisingly\, our mechanism allows for the release of an arbitrary number of symmetric norm approximations without any overhead or additional loss in privacy. Moreover\, our mechanism permits (1+\alpha)-approximation to each of the symmetric norms and can be implemented using sublinear space in the streaming model for many regimes of the accuracy and privacy parameters.\n\n\n\nAloni Cohen\, University of Chicago\nTitle: Attacks on Deidentification’s Defenses \nAbstract: Quasi-identifier-based deidentification techniques (QI-deidentification) are widely used in practice\, including k-anonymity\, ?-diversity\, and t-closeness. We present three new attacks on QI-deidentification: two theoretical attacks and one practical attack on a real dataset. In contrast to prior work\, our theoretical attacks work even if every attribute is a quasi-identifier. Hence\, they apply to k-anonymity\, ?-diversity\, t-closeness\, and most other QI-deidentification techniques.\nFirst\, we introduce a new class of privacy attacks called downcoding attacks\, and prove that every QI-deidentification scheme is vulnerable to downcoding attacks if it is minimal and hierarchical. Second\, we convert the downcoding attacks into powerful predicate singling-out (PSO) attacks\, which were recently proposed as a way to demonstrate that a privacy mechanism fails to legally anonymize under Europe’s General Data Protection Regulation. Third\, we use LinkedIn.com to reidentify 3 students in a k-anonymized dataset published by EdX (and show thousands are potentially vulnerable)\, undermining EdX’s claimed compliance with the Family Educational Rights and Privacy Act. \nThe significance of this work is both scientific and political. Our theoretical attacks demonstrate that QI-deidentification may offer no protection even if every attribute is treated as a quasi-identifier. Our practical attack demonstrates that even deidentification experts acting in accordance with strict privacy regulations fail to prevent real-world reidentification. Together\, they rebut a foundational tenet of QI-deidentification and challenge the actual arguments made to justify the continued use of k-anonymity and other QI-deidentification techniques.\n\n\n\nSteven Wu\,\nCarnegie Mellon University\nTitle: Fully Adaptive Composition in Differential Privacy \nAbstract: Composition is a key feature of differential privacy. Well-known advanced composition theorems allow one to query a private database quadratically more times than basic privacy composition would permit. However\, these results require that the privacy parameters of all algorithms be fixed before interacting with the data. To address this\, Rogers et al. introduced fully adaptive composition\, wherein both algorithms and their privacy parameters can be selected adaptively. The authors introduce two probabilistic objects to measure privacy in adaptive composition: privacy filters\, which provide differential privacy guarantees for composed interactions\, and privacy odometers\, time-uniform bounds on privacy loss. There are substantial gaps between advanced composition and existing filters and odometers. First\, existing filters place stronger assumptions on the algorithms being composed. Second\, these odometers and filters suffer from large constants\, making them impractical. We construct filters that match the tightness of advanced composition\, including constants\, despite allowing for adaptively chosen privacy parameters. We also construct several general families of odometers. These odometers can match the tightness of advanced composition at an arbitrary\, preselected point in time\, or at all points in time simultaneously\, up to a doubly-logarithmic factor. We obtain our results by leveraging recent advances in time-uniform martingale concentration. In sum\, we show that fully adaptive privacy is obtainable at almost no loss\, and conjecture that our results are essentially not improvable (even in constants) in general.\n\n\n3:15 pm–3:45 pm\nFORC Reception\n\n\n\n3:45 pm–5:00 pm\nSocial Hour
URL:https://live-hu-cmsa-222.pantheonsite.io/event/symposium-on-foundations-of-responsible-computing-forc/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/FORC22_poster.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220415T090000
DTEND;TZID=America/New_York:20220415T130000
DTSTAMP:20240229T102446Z
CREATED:20230705T083343Z
LAST-MODIFIED:20240229T102446Z
UID:10000088-1650013200-1650027600@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Workshop on Machine Learning and Mathematical Conjecture
DESCRIPTION:On April 15\, 2022\, the CMSA will hold a one-day workshop\, Machine Learning and Mathematical Conjecture\, related to the New Technologies in Mathematics Seminar Series. \nLocation: Room G10\, 20 Garden Street\, Cambridge\, MA 02138. \nOrganizers: Michael R. Douglas (CMSA/Stony Brook/IAIFI) and Peter Chin (CMSA/BU). \nMachine learning has driven many exciting recent scientific advances. It has enabled progress on long-standing challenges such as protein folding\, and it has helped mathematicians and mathematical physicists create new conjectures and theorems in knot theory\, algebraic geometry\, and representation theory. \nAt this workshop\, we will bring together mathematicians\, theoretical physicists\, and machine learning researchers to review the state of the art in machine learning\, discuss how ML results can be used to inspire\, test and refine precise conjectures\, and identify mathematical questions which may be suitable for this approach. \nSpeakers: \n\nJames Halverson\, Northeastern University Dept. of Physics and IAIFI\nFabian Ruehle\, Northeastern University Dept. of Physics and Mathematics and IAIFI\nAndrew Sutherland\, MIT Department of Mathematics\n\n  \n \n  \n  \n \n 
URL:https://live-hu-cmsa-222.pantheonsite.io/event/workshop-on-machine-learning-and-mathematical-conjecture/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Machine-Learning.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220406T133000
DTEND;TZID=America/New_York:20220406T143000
DTSTAMP:20240228T114223Z
CREATED:20230801T182005Z
LAST-MODIFIED:20240228T114223Z
UID:10001163-1649251800-1649255400@live-hu-cmsa-222.pantheonsite.io
SUMMARY:On the wave turbulence theory for a stochastic KdV type equation
DESCRIPTION:Random Matrix & Probability Theory Seminar\n\nSpeaker: Minh-Binh TRAN (SMU & MIT)\n\n\n\n\n\nLocation: CMSA\, Room G02 \nTitle: On the wave turbulence theory for a stochastic KdV type equation \nAbstract: We report recent progress\, in collaboration with Gigliola Staffilani (MIT)\, on the problem of deriving kinetic equations from dispersive equations. To be more precise\, starting from the stochastic  Zakharov-Kuznetsov equation\, a multidimensional KdV type equation on a hypercubic lattice\, we provide a derivation of the 3-wave kinetic equation. We show that the two point correlation function can be asymptotically expressed as the solution of the 3-wave  kinetic equation at the kinetic limit under very general assumptions: the initial condition is out of equilibrium\, the dimension is  $d\ge 2$\, the smallness of the nonlinearity $\lambda$ is allowed to be independent of the size of the lattice\, the weak noise is chosen not to compete with the weak nonlinearity and not to inject energy into the equation.  Unlike the cubic nonlinear Schrodinger equation\, for which such a general result is commonly expected without the noise\, the kinetic description of the deterministic lattice ZK equation is unlikely to happen. One of the key reasons is that the dispersion relation of the lattice ZK equation leads to a singular manifold\, on which not only 3-wave interactions but also all m-wave interactions are allowed to happen. This phenomenon has been first observed by Lukkarinen  as a counterexample for which one of the main tools to derive kinetic equations from wave equations (the suppression of crossings) fails to hold true.
URL:https://live-hu-cmsa-222.pantheonsite.io/event/random_4622/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Special Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220301T100000
DTEND;TZID=America/New_York:20220517T130000
DTSTAMP:20250328T144509Z
CREATED:20240215T103842Z
LAST-MODIFIED:20250328T144509Z
UID:10002743-1646128800-1652792400@live-hu-cmsa-222.pantheonsite.io
SUMMARY:General Relativity Program Minicourses
DESCRIPTION:Minicourses\nGeneral Relativity Program Minicourses \n\nDuring the Spring 2022 semester\, the CMSA hosted a program on General Relativity. \nThis semester-long program included four minicourses running in March\, April\, and May;  a conference April 4–8\, 2022;  and a workshop from May 2–5\, 2022. \n\n  \n\n\n\n\nSchedule\nSpeaker\nTitle\nAbstract\n\n\nMarch 1 – 3\, 2022\n10:00 am – 12:00 pm ET\, each dayLocation: Hybrid. CMSA main seminar room\, G-10.\nDr. Stefan Czimek\nCharacteristic Gluing for the Einstein Equations\nAbstract: This course serves as an introduction to characteristic gluing for the Einstein equations (developed by the lecturer in collaboration with S. Aretakis and I. Rodnianski). First we set up and analyze the characteristic gluing problem along one outgoing null hypersurface.  Then we turn to bifurcate characteristic gluing (i.e.  gluing along two null hypersurfaces bifurcating from a spacelike 2-sphere) and show how to localize characteristic initial data. Subsequently we turn to applications for spacelike initial data. Specifically\, we discuss in detail our alternative proofs of the celebrated Corvino-Schoen gluing to Kerr and the Carlotto-Schoen localization of spacelike initial data (with improved decay).\n\n\nMarch 22 – 25\, 2022\n22nd & 23rd\, 10:00 am – 11:30am ET\n24th & 25th\, 11:00 am – 12:30pm ETLocation: Hybrid. CMSA main seminar room\, G-10.\nProf. Lan-Hsuan Huang\nExistence of Static Metrics with Prescribed Bartnik Boundary Data\nAbstract: The study of static Riemannian metrics arises naturally in general relativity and differential geometry. A static metric produces a special Einstein manifold\, and it interconnects with scalar curvature deformation and gluing. The well-known Uniqueness Theorem of Static Black Holes says that an asymptotically flat\, static metric with black hole boundary must belong to the Schwarzschild family. In the same vein\, most efforts have been made to classify static metrics as known exact solutions. In contrast to the rigidity phenomena and classification efforts\, Robert Bartnik proposed the Static Vacuum Extension Conjecture (originating from his other conjectures about quasi-local masses in the 80’s) that there is always a unique\, asymptotically flat\, static vacuum metric with quite arbitrarily prescribed Bartnik boundary data. In this course\, I will discuss some recent progress confirming this conjecture for large classes of boundary data. The course is based on joint work with Zhongshan An\, and the tentative plan is \n1. The conjecture and an overview of the results\n2. Static regular: a sufficient condition for existence and local uniqueness\n3. Convex boundary\, isometric embedding\, and static regular\n4. Perturbations of any hypersurface are static regular \nVideo on Youtube: March 22\, 2022\n\n\nMarch 29 – April 1\, 2022 10:00am – 12:00pm ET\, each day \nLocation: Hybrid. CMSA main seminar room\, G-10.\nProf. Martin Taylor\nThe nonlinear stability of the Schwarzschild family of black holes\nAbstract: I will present aspects of a theorem\, joint with Mihalis Dafermos\, Gustav Holzegel and Igor Rodnianski\, on the full finite codimension nonlinear asymptotic stability of the Schwarzschild family of black holes.\n\n\nApril 19 & 21\, 2022\n10 am – 12 pm ET\, each dayZoom only\nProf. Håkan Andréasson\nTwo topics for the Einstein-Vlasov system: Gravitational collapse and properties of static and stationary solutions.\nAbstract: In these lectures I will discuss the Einstein-Vlasov system in the asymptotically flat case. I will focus on two topics; gravitational collapse and properties of static and stationary solutions. In the former case I will present results in the spherically symmetric case that give criteria on initial data which guarantee the formation of black holes in the evolution. I will also discuss the relation between gravitational collapse for the Einstein-Vlasov system and the Einstein-dust system. I will then discuss properties of static and stationary solutions in the spherically symmetric case and the axisymmetric case. In particular I will present a recent result on the existence of massless steady states surrounding a Schwarzschild black hole. \nVideo 4/19/2022 \nVideo 4/22/2022\n\n\nMay 16 – 17\, 2022\n10:00 am – 1:00 pm ET\, each dayLocation: Hybrid. CMSA main seminar room\, G-10.\nProf. Marcelo Disconzi\nA brief overview of recent developments in relativistic fluids\nAbstract: In this series of lectures\, we will discuss some recent developments in the field of relativistic fluids\, considering both the motion of relativistic fluids in a fixed background or coupled to Einstein’s equations. The topics to be discussed will include: the relativistic free-boundary Euler equations with a physical vacuum boundary\, a new formulation of the relativistic Euler equations tailored to applications to shock formation\, and formulations of relativistic fluids with viscosity. \n1. Set-up\, review of standard results\, physical motivation.\n2. The relativistic Euler equations: null structures and the problem of shocks.\n3. The free-boundary relativistic Euler equations with a physical vacuum boundary.\n4. Relativistic viscous fluids. \nVideo 5/16/2022 \nVideo 5/17/2022
URL:https://live-hu-cmsa-222.pantheonsite.io/event/grminicourses/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220124T090000
DTEND;TZID=America/New_York:20220521T170000
DTSTAMP:20240215T103430Z
CREATED:20230904T083438Z
LAST-MODIFIED:20240215T103430Z
UID:10000055-1643014800-1653152400@live-hu-cmsa-222.pantheonsite.io
SUMMARY:General Relativity Program
DESCRIPTION:During the Spring 2022 semester\, the CMSA hosted a program on General Relativity. \nThis semester-long program included four minicourses\,  a conference\, and a workshop. \nGeneral Relativity Mincourses: March–May\, 2022 \nGeneral Relativity Conference: April 4–8\, 2022 \nGeneral Relativity Workshop: May 2–5\, 2022 \n  \nProgram Visitors \n\nDan Lee\, CMSA/CUNY\, 1/24/22 – 5/20/22\nStefan Czimek\, Brown\, 2/27/22 – 3/3/22\nLan-Hsuan Huang\, University of Connecticut\, 3/13/22 – 3/19/222\, 3/21/22 – 3/25/22\, 4/17 /22– 4/23/22\nMu-Tao Wang\, Columbia\, 3/21/22 – 3/25/22\, 5/7/22 – 5/9/22\nPo-Ning Chen\, University of California\, Riverside\, 3/21/22 – 3/25/22\,  5/7/22–5/9/22\nMarnie Smith\, Imperial College London\, 3/27/22 – 4/11/22\nChristopher Stith\, University of Michigan\, 3/27/22 – 4/23/22\nMartin Taylor\, Imperial College London\,  3/27/22 – 4/11/22\nMarcelo Disconzi\, Vanderbilt\, 5/9/22 – 5/21/22\nLydia Bieri\, University of Michigan\, 5/5/22 – 5/9/22\n\n 
URL:https://live-hu-cmsa-222.pantheonsite.io/event/general-relativity-program/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Programs
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/GR-Program-Banner_800x450-2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200304T090000
DTEND;TZID=America/New_York:20200306T163000
DTSTAMP:20250328T144548Z
CREATED:20230715T073919Z
LAST-MODIFIED:20250328T144548Z
UID:10000126-1583312400-1583512200@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Mirror symmetry\, gauged linear sigma models\, matrix factorizations\, and related topics
DESCRIPTION:On March 4-6\, 2020 the CMSA will be hosting a three-day workshop on Mirror symmetry\, Gauged linear sigma models\, Matrix factorizations\, and related topics as part of the Simons Collaboration on Homological Mirror Symmetry. The workshop will be held in room G10 of the CMSA\, located at 20 Garden Street\, Cambridge\, MA.  \nSpeakers:  \n\nAndrei Căldăraru\, University of Wisconsin\nDavid Favero\, University of Alberta\nElana Kalashnikov\, Harvard University\nTsung-Ju Lee\, CMSA\nConan Leung\, CUHK\nDavid Morrison\, University of California\, Santa Barbara\nMauricio Romo\, YMSC\nYun Shi\, CMSA\nMark Shoemaker\, Colorado State University\nRachel Webb\, University of Michigan\nChris Woodward\, Rutgers University\nGuangbo Xu\, Texas A&M University\nChenglong Yu\, University of Pennsylvania\n\nSchedule \nVideos from the workshop are available in the Youtube playlist.
URL:https://live-hu-cmsa-222.pantheonsite.io/event/mirror-symmetry-gauged-linear-sigma-models-matrix-factorizations-and-related-topics/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Mirror-Symmetry-poster-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191202T090000
DTEND;TZID=America/New_York:20191204T170000
DTSTAMP:20250305T191848Z
CREATED:20230715T073716Z
LAST-MODIFIED:20250305T191848Z
UID:10000125-1575277200-1575478800@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Quantum Matter Workshop
DESCRIPTION:On December 2-4\, 2019 the CMSA will be hosting a workshop on Quantum Matter as part of our program on Quantum Matter in Mathematics and Physics. The workshop will be held in room G10 of the CMSA\, located at 20 Garden Street\, Cambridge\, MA. \nPictures can be found here.\n \nOrganizers: Juven Wang (CMSA)\, Xiao-Gang Wen (MIT)\, and Shing-Tung Yau (Harvard) \nConfirmed Speakers:  \n\nZhen Bi\, MIT | Video\nClaudio Chamon\, BU | Video\nTrithep Devakul\, Princeton | Video\nAnushya Chandran\, BU\nLiang Fu\, MIT\nAndrey Gromov\, Brown | Video\nDaniel Louis Jafferis\, Harvard | Video\nEslam Khalaf\, Harvard | Video\nHong Liu\, MIT\nShang Liu\, Harvard | Video\nEmil Prodan\, Yeshiva | Video\nSubir Sachdev\, Harvard | Video\nDries Sels\, Harvard | Video\nYuya Tanizaki\, NCSU | Video\nSenthil Todadri\, MIT | Video\nJuven Wang\, CMSA | Video\nYifan Wang\, CMSA | Video\nXiao-Gang Wen\, MIT\nXueda Wen\, MIT | Video\nXi Yin\, Harvard | Video\nYizhi You\, Princeton | Video\nYunqin Zheng\, Princeton | Video\n\n 
URL:https://live-hu-cmsa-222.pantheonsite.io/event/quantum-matter-workshop/
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/Quantum-12x18-1-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180305T152700
DTEND;TZID=America/New_York:20180305T152700
DTSTAMP:20240213T100816Z
CREATED:20240213T100816Z
LAST-MODIFIED:20240213T100816Z
UID:10002395-1520263620-1520263620@live-hu-cmsa-222.pantheonsite.io
SUMMARY:3-5-2018 Mathematical Physics Seminar
DESCRIPTION:
URL:https://live-hu-cmsa-222.pantheonsite.io/event/3-5-2018-mathematical-physics-seminar/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Mathematical Physics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180226T154200
DTEND;TZID=America/New_York:20180226T154200
DTSTAMP:20240213T100602Z
CREATED:20240213T100602Z
LAST-MODIFIED:20240213T100602Z
UID:10002390-1519659720-1519659720@live-hu-cmsa-222.pantheonsite.io
SUMMARY:2-26-2018 Mathematical Physics Seminar
DESCRIPTION:
URL:https://live-hu-cmsa-222.pantheonsite.io/event/2-26-2018-mathematical-physics-seminar/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Mathematical Physics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180223T153000
DTEND;TZID=America/New_York:20180223T153000
DTSTAMP:20240213T101226Z
CREATED:20240213T101226Z
LAST-MODIFIED:20240213T101226Z
UID:10002401-1519399800-1519399800@live-hu-cmsa-222.pantheonsite.io
SUMMARY:2-23-2018 RM & PT Seminar
DESCRIPTION:
URL:https://live-hu-cmsa-222.pantheonsite.io/event/2-23-2018-rm-pt-seminar/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Random Matrix & Probability Theory Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180216T150900
DTEND;TZID=America/New_York:20180216T150900
DTSTAMP:20240213T101421Z
CREATED:20240213T101421Z
LAST-MODIFIED:20240213T101421Z
UID:10002404-1518793740-1518793740@live-hu-cmsa-222.pantheonsite.io
SUMMARY:2-16-2018 RM & PT Seminar
DESCRIPTION:
URL:https://live-hu-cmsa-222.pantheonsite.io/event/2-16-2018-rm-pt-seminar/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Random Matrix & Probability Theory Seminar
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:20171129T141100
DTEND;TZID=America/New_York:20171129T141100
DTSTAMP:20240213T094010Z
CREATED:20240213T093633Z
LAST-MODIFIED:20240213T094010Z
UID:10002346-1511964660-1511964660@live-hu-cmsa-222.pantheonsite.io
SUMMARY:11-29-2017 Mathematical Physics Seminar
DESCRIPTION:
URL:https://live-hu-cmsa-222.pantheonsite.io/event/11-29-2017-mathematical-physics-seminar/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Mathematical Physics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171129T140300
DTEND;TZID=America/New_York:20171129T140300
DTSTAMP:20240213T093912Z
CREATED:20240213T093912Z
LAST-MODIFIED:20240213T093912Z
UID:10002353-1511964180-1511964180@live-hu-cmsa-222.pantheonsite.io
SUMMARY:11-29-17 RM & PT Seminar
DESCRIPTION:
URL:https://live-hu-cmsa-222.pantheonsite.io/event/11-29-17-rm-pt-seminar/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Random Matrix & Probability Theory Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170523T133200
DTEND;TZID=America/New_York:20170523T133200
DTSTAMP:20240209T152416Z
CREATED:20230801T174602Z
LAST-MODIFIED:20240209T152416Z
UID:10000033-1495546320-1495546320@live-hu-cmsa-222.pantheonsite.io
SUMMARY:5/23/2017 CMSA Special Seminar
DESCRIPTION:
URL:https://live-hu-cmsa-222.pantheonsite.io/event/5-23-2017-cmsa-special-seminar/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Special Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170327T153400
DTEND;TZID=America/New_York:20170330T153400
DTSTAMP:20240307T105730Z
CREATED:20240209T021031Z
LAST-MODIFIED:20240307T105730Z
UID:10001796-1490628840-1490888040@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Working Conference on Materials and Data Analysis\, March 27-30\, 2017
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a 5-day working Conference on Materials and Data Analysis and related areas\, March 27-30\, 2017.  The conference will be hosted in Room G10 of the CMSA Building located at 20 Garden Street\, Cambridge\, MA 02138. \nPhotos of the event can be found on CMSA’s Blog. \n Participants:\n\nRyan P. Adams\, Harvard University\nJörg Behler\, University of Göttingen\nKieron Burke\, University of California\, Irvine\nLucy Colwell\, University of Cambridge\nGábor Csányi\, University of Cambridge\nEkin Doğuş Çubuk\, Stanford University\nLeslie Greengard\, Courant Institute of Mathematical Sciences\, New York University\nPetros Koumoutsakos\, Radcliffe Institute for Advanced Study\, Harvard University\nGovind Menon\, Brown University\nEvan Reed\, Stanford University\nPatrick Riley\, Google\nMatthias Rupp\, Fitz Haber Institute of the Max Planck Society\nSadasivan Shankar\, Harvard University\nDennis Sheberla\, Harvard University\n\n\n\nOrganizers: \n\n\n\nMichael Brenner\, Efthimios Kaxiras \n\n\n\n* This event is sponsored by CMSA Harvard University. \n\nSchedule:\n\nMonday\, March 27 \n\n\n\nTime\nSpeaker\nTitle\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nKieron Burke\, University of California\, Irvine\nBackground in DFT and electronic structure calculations\n\n\n10:00am – 11:00am\nKieron Burke\, University of California\, Irvine\n\nThe density functionals machines can learn \n\n\n\n11:00am – 12:00pm\nSadasivan Shankar\, Harvard University\nA few key principles for applying Machine Learning to Materials (or Complex Systems) — Scientific and Engineering Perspectives\n\n\n\nTuesday\, March 28 \n\n\n\nTime\nSpeaker\nTitle\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nRyan Adams\, Harvard\nTBA\n\n\n10:00am – 11:00am\nGábor Csányi\, University of Cambridge\n\nInteratomic potentials using machine learning: accuracy\, transferability and chemical diversity \n\n\n\n11:00am – 1:00pm\nLunch Break\n\n\n1:00pm – 2:00pm\nEvan Reed\, Stanford University\nTBA\n\n\n\n Wednesday\, March 29  \n\n\n\nTime\nSpeaker\nTitle\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nPatrick Riley\, Google\nThe Message Passing Neural Network framework and its application to molecular property prediction\n\n\n10:00am – 11:00am\nJörg Behler\, University of Göttingen\nTBA\n\n\n11:00am – 12:00pm\nEkin Doğuş Çubuk\, Stanford Univers\nTBA\n\n\n4:00pm\nLeslie Greengard\, Courant Institute\nInverse problems in acoustic scattering and cryo-electron microscopy \nCMSA Colloquium\n\n\n\nThursday\, March 30 \n\n\n\nTime\nSpeaker\nTitle\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nMatthias Rupp\, Fitz Haber Institute of the Max Planck Society\nTBA\n\n\n10:00am – 11:00am\nPetros Koumoutsakos\, Radcliffe Institute for Advanced Study\, Harvard\nTBA\n\n\n11:00am – 1:00pm\nLunch Break\n\n\n1:00pm – 2:00pm\nDennis Sheberla\, Harvard University\nRapid discovery of functional molecules by a high-throughput virtual screening\n\n\n\n\n\n\n\nEvents\, Past Events
URL:https://live-hu-cmsa-222.pantheonsite.io/event/working-conference-on-materials-and-data-analysis-march-27-30-2017/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170215T163000
DTEND;TZID=America/New_York:20170215T173000
DTSTAMP:20240214T155238Z
CREATED:20240213T062641Z
LAST-MODIFIED:20240214T155238Z
UID:10002109-1487176200-1487179800@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Geometry of 3-manifolds and Complex Chern-Simons Theory
DESCRIPTION:Speaker: Masahito Yamazaki (IMPU) \nTitle: Geometry of 3-manifolds and Complex Chern-Simons Theory \nAbstract: The geometry of 3-manifolds has been a fascinating subject in mathematics. In this talk I discuss a “quantization” of 3-manifold geometry\, in the language of complex Chern-Simons theory. This Chern-Simons theory in turn is related to the physics of 3-dimensional supersymmetric field theories through the so-called 3d/3d correspondence\, whose origin can be traced back to a mysterious theory on the M5-branes. Along the way I will also comment on the connection with a number of related topics\, such as knot theory\, hyperbolic geometry\, quantum dilogarithm and cluster algebras.
URL:https://live-hu-cmsa-222.pantheonsite.io/event/02-15-2017-colloquium/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Colloquium
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/2017_02_07_11_38_45.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170215T150000
DTEND;TZID=America/New_York:20171214T160000
DTSTAMP:20240124T065049Z
CREATED:20240124T064532Z
LAST-MODIFIED:20240124T065049Z
UID:10001332-1487170800-1513267200@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Random Matrix & Probability Theory Seminar (2016-2017)
DESCRIPTION:The random matrix and probability theory will be every Wednesday from 3pm-4pm in CMSA Building\, 20 Garden Street\, Room G10.
URL:https://live-hu-cmsa-222.pantheonsite.io/event/random-matrix-probability-theory-seminar-2016-2017/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Random Matrix & Probability Theory Seminar
ATTACH;FMTTYPE=image/jpeg:https://live-hu-cmsa-222.pantheonsite.io/media/DSC_0025-768x512-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170109T090000
DTEND;TZID=America/New_York:20170113T170000
DTSTAMP:20240209T152014Z
CREATED:20230717T173216Z
LAST-MODIFIED:20240209T152014Z
UID:10000020-1483952400-1484326800@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Workshop on Discrete and Topological Models for Effective Field Theories\, January 9-13\, 2017
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a Workshop on “Discrete and Topological Models for Effective Field Theories\,” January 9-13\, 2017.  The workshop will be hosted in G02 of the CMSA Building located at 20 Garden Street\, Cambridge\, MA 02138. \nTitles\, abstracts and schedule will be provided nearer to the event. \nParticipants:\nDan Freed\, UT Austin \nAnton Kapustin\, California Institute of Technology \nAlexei Y. Kitaev\, California Institute of Technology \nGreg Moore\, Rutgers University \n\n\n\nConstantin Teleman\, University of Oxford \n\n\n\nOrganizers: \n\n\n\nMike Hopkins\, Shing-Tung Yau \n* This event is sponsored by CMSA Harvard University.
URL:https://live-hu-cmsa-222.pantheonsite.io/event/workshop-on-discrete-and-topological-models-for-effective-field-theories-january-9-13-2017/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20160627T090000
DTEND;TZID=America/New_York:20160630T123000
DTSTAMP:20240209T151628Z
CREATED:20230717T181127Z
LAST-MODIFIED:20240209T151628Z
UID:10001123-1467018000-1467289800@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Workshop on Optimization in Image Processing
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a workshop on Optimization in Image Processing on June 27 – 30\, 2016. This 4-day workshop aims to bring together researchers to exchange and stimulate ideas in imaging sciences\, with a special focus on new approaches based on optimization methods. This is a cutting-edge topic with crucial impact in various areas of imaging science including inverse problems\, image processing and computer vision. 16 speakers will participate in this event\, which we think will be a very stimulating and exciting workshop. The workshop will be hosted in Room G10 of the CMSA Building located at 20 Garden Street\, Cambridge\, MA 02138. \nTitles\, abstracts and schedule will be provided nearer to the event. \nSpeakers:\n\nAntonin Chambolle\, CMAP\, Ecole Polytechnique\nRaymond Chan\, The Chinese University of Hong Kong\nKe Chen\, University of Liverpool\nPatrick Louis Combettes\, Université Pierre et Marie Curie\nMario Figueiredo\, Instituto Superior Técnico\nAlfred Hero\, University of Michigan\nRonald Lok Ming Lui\, The Chinese University of Hong Kong\nMila Nikolova\, Ecole Normale Superieure Cachan\nShoham Sabach\, Israel Institute of Technology\nMartin Benning\, University of Cambridge\nJin Keun Seo\, Yonsei University\nFiorella Sgallari\, University of Bologna\nGabriele Steidl\, Kaiserslautern University of Technology\nJoachim Weickert\, Saarland University\nIsao Yamada\, Tokyo Institute of Technology\nWotao Yin\, UCLA\n\nPlease click Workshop Program for a downloadable schedule with talk abstracts.\nPlease note that lunch will not be provided during the conference\, but a map of Harvard Square with a list of local restaurants can be found by clicking Map & Resturants.\nPlease click here for registration – Registration Deadline: June 7\, 2016; Registration is capped at 70 participants.\n\nSchedule:\n\n\n\nJune 27 – Day 1\n\n\n9:00am\nBreakfast\n\n\n9:20am\nOpening remarks\n\n\n9:30am – 10:20am\nJoachim Weickert\, “FSI Schemes: Fast Semi-Iterative Methods for Diffusive or Variational Image Analysis Problems”\n\n\n10:20am – 10:50am\nBreak\n\n\n10:50am – 11:40pm\nPatrick Louis Combettes\, “Block-Iterative Asynchronous Variational Image Recovery”\n\n\n11:40am – 12:30pm\nIsao Yamada\, “Spicing up Convex Optimization for Certain Inverse Problems”\n\n\n12:30pm – 2:00pm\nLunch\n\n\n2:30pm – 3:20pm\nFiorella Sgallari\, “Majorization-Minimization for Nonconvex Optimization”\n\n\n3:20pm – 3:50pm\nBreak\n\n\n3:50pm – 4:40pm\nShoham Sabach\, “A Framework for Globally Convergent Methods in Nonsmooth and Nonconvex Problems”\n\n\nJune 28 – Day 2\n\n\n9:00am\nBreakfast\n\n\n9:30am – 10:20am\nAntonin Chambolle\, “Acceleration of alternating minimisations”\n\n\n10:20am – 10:50am\nBreak\n\n\n10:50am – 11:40am\nMario Figueiredo\, “ADMM in Image Restoration and Related Problems: Some History and Recent Advances”\n\n\n11:40am – 12:30pm\nKe Chen\, “Image Restoration and Registration Based on Total Fractional-Order Variation Regularization”\n\n\n12:30pm – 2:30pm\nLunch\n\n\n2:30pm – 4:40pm\nDiscussions\n\n\nJune 29 – Day 3\n\n\n9:00am\nBreakfast\n\n\n9:30am – 10:20am\nAlfred Hero\, “Continuum relaxations for discrete optimization”\n\n\n10:20am – 10:50am\nBreak\n\n\n10:50am – 11:40am\nWotao Yin\, “Coordinate Update Algorithms for Computational Imaging and Machine Learning”\n\n\n11:40am – 12:30pm\nMila Nikolova\, “Limits on noise removal using log-likelihood and regularization”\n\n\n12:30pm – 2:30pm\nLunch\n\n\n2:30pm – 3:20pm\nMartin Benning\, “Nonlinear spectral decompositions and the inverse scale space method”\n\n\n3:20pm – 3:50pm\nBreak\n\n\n3:50pm – 4:40pm\nRonald Ming Lui\, “TEMPO: Feature-endowed Teichmuller extremal mappings of point cloud for shape classification”\n\n\nJune 30 – Day 4\n\n\n9:00am\nBreakfast\n\n\n9:30am – 10:20am\nJin Keun Seo\, “Mathematical methods for biomedical impedance imaging”\n\n\n10:20am – 10:50am\nBreak\n\n\n10:50am – 11:40am\nGabriele Steidl\, “Iterative Multiplicative Filters for Data Labeling”\n\n\n11:40am – 12:30pm\nRaymond Chan\, “Point-spread function reconstruction in ground-based astronomy”\n\n\n\n* This event is sponsored by CMSA Harvard University.\n \nOrganizers: Raymond Chan and Shing-Tung Yau
URL:https://live-hu-cmsa-222.pantheonsite.io/event/workshop-on-optimization-in-image-processing-3/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20160408T083000
DTEND;TZID=America/New_York:20160410T180000
DTSTAMP:20240209T151732Z
CREATED:20230717T180554Z
LAST-MODIFIED:20240209T151732Z
UID:10000016-1460104200-1460311200@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Concluding Conference of the Special Program on Nonlinear Equations\, April 8 – 10\, 2016
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a concluding conference on April 8-10\, 2016 to accompany the year-long program on nonlinear equations. The conference will have 15 speakers and will be hosted at Harvard CMSA Building: Room G10 20 Garden Street\, Cambridge\, MA 02138 \nSpeakers:\n\nLydia Bieri (University of Michigan)\nLuis Caffarelli (University of Texas at Austin)\nMihalis Dafermos (Princeton University)\nCamillo De Lellis (Universität Zürich)\nPengfei Guan (McGill University)\nSlawomir Kolodziej (Jagiellonian University)\nMelissa Liu (Columbia University)\nDuong H. Phong (Columbia University)\nRichard Schoen (UC Irvine)\nCliff Taubes (Harvard University)\nBlake Temple (UC Davis)\nValentino Tosatti (Northwestern University)\nTai-Peng Tsai (University of British Columbia)\nMu-Tao Wang (Columbia University)\nXu-jia Wang (Australian National University)\n\nPlease click NLE Conference Schedule with Abstracts for a downloadable schedule with talk abstracts.\nPlease note that lunch will not be provided during the conference\, but a map of Harvard Square with a list of local restaurants can be found by clicking Map & Resturants.\nSchedule:\n\n\n\nApril 8 – Day 1\n\n\n8:30am\nBreakfast\n\n\n8:45am\nOpening remarks\n\n\n9:00am – 10:00am\nCamillo De Lellis\, “A Nash Kuiper theorem for $C^{1\,1:5}$ isometric immersions of disks“\n\n\n10:00am – 10:15am\nBreak\n\n\n10:15am – 11:15am\nXu-Jia Wang\, “Monge’s mass transport problem“\n\n\n11:15am – 11:30am\nBreak\n\n\n11:30am – 12:30pm\nPeng-Fei Guan\, “The Weyl isometric embedding problem in general $3$ d Riemannian manifolds“\n\n\n12:30pm – 2:00pm\nLunch\n\n\n2:00pm – 3:00pm\nBlake Temple\, “An instability in the Standard Model of Cosmology“\n\n\n3:00pm – 3:15pm\nBreak\n\n\n3:15pm – 4:15pm\nLydia Bieri\, “The Einstein Equations and Gravitational Radiation“\n\n\n4:15pm – 4:30pm\nBreak\n\n\n4:30pm – 5:30pm\nValentino Tosatti\, “Adiabatic limits of Ricci flat Kahler metrics“\n\n\n\n\n\nApril 9 – Day 2\n\n\n8:45am\nBreakfast\n\n\n9:00am – 10:00am\nD.H. Phong\, “On Strominger systems and Fu-Yau equations”\n\n\n10:00am – 10:15am\nBreak\n\n\n10:15am – 11:15am\nSlawomir Kolodziej\, “Stability of weak solutions of the complex Monge-Ampère equation on compact Hermitian manifolds”\n\n\n11:15am – 11:30am\nBreak\n\n\n11:30am – 12:30pm\nLuis Caffarelli\, “Non local minimal surfaces and their interactions”\n\n\n12:30pm – 2:00pm\nLunch\n\n\n2:00pm – 3:00pm\nMihalis Dafermos\, “The interior of dynamical vacuum black holes and the strong cosmic censorship conjecture in general relativity”\n\n\n3:00pm – 3:15pm\nBreak\n\n\n3:15pm – 4:15pm\nMu-Tao Wang\, “The stability of Lagrangian curvature flows”\n\n\n4:15pm – 4:30pm\nBreak\n\n\n4:30pm – 5:30pm\nMelissa Liu\, “Counting curves in a quintic threefold”\n\n\n\n\n\nApril 10 – Day 3\n\n\n8:45am\nBreakfast\n\n\n9:00am – 10:00am\nRick Schoen\, “Metrics of fixed area on high genus surfaces with largest first eigenvalue”\n\n\n10:00am – 10:15am\nBreak\n\n\n10:15am – 11:15am\nCliff Taubes\, “The zero loci of Z/2 harmonic spinors in dimensions 2\, 3 and 4”\n\n\n11:15am – 11:30am\nBreak\n\n\n11:30am – 12:30pm\nTai-Peng Tsai\, “Forward Self-Similar and Discretely Self-Similar Solutions of the 3D incompressible Navier-Stokes Equations”\n\n\n\n* This event is sponsored by National Science Foundation (NSF) and CMSA Harvard University.
URL:https://live-hu-cmsa-222.pantheonsite.io/event/concluding-conference-of-the-special-program-on-nonlinear-equations-april-8-10-2016-2/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event
END:VEVENT
END:VCALENDAR