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Past Events › LIDS Seminar Series

Distinguished speakers provide an overview and discuss recent progress in research areas spanning communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing. Intended for a broad audience.

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May 2019

Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery

May 14, 2019 @ 4:00 pm - 5:00 pm

Mihaela van der Schaar (University of California, Los Angeles)


The overarching goal of my research is to develop cutting-edge machine learning, AI and operations research theory, methods, algorithms, and systems to understand the basis of health and disease; develop methodology to catalyze clinical research; support clinical decisions through individualized medicine; inform clinical pathways, better utilize resources & reduce costs; and inform public health. To do this, Prof. van der Schaar is creating what she calls Learning Engines for Healthcare (LEH’s). An LEH is an integrated ecosystem that uses machine learning, AI…

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April 2019

On Coupling Methods for Nonlinear Filtering and Smoothing

April 30, 2019 @ 4:00 pm - 5:00 pm

Youssef Marzouk (MIT)


Bayesian inference for non-Gaussian state-space models is a ubiquitous problem with applications ranging from geophysical data assimilation to mathematical finance. We will discuss how deterministic couplings between probability distributions enable new solutions to this problem. We first consider filtering in high-dimensional models with nonlinear (potentially chaotic) dynamics and sparse observations in space and time. While the ensemble Kalman filter (EnKF) yields robust ensemble approximations of the filtering distribution in this setting, it is limited by linear forecast-to-analysis transformations. To generalize…

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Memory-Efficient Adaptive Optimization for Humungous-Scale Learning

April 23, 2019 @ 4:00 pm - 5:00 pm

Yoram Singer (Google)

32-G449 (KIva/Patel)

Adaptive gradient-based optimizers such as AdaGrad and Adam are among the methods of choice in modern machine learning. These methods maintain second-order statistics of each model parameter, thus doubling the memory footprint of the optimizer. In behemoth-size applications, this memory overhead restricts the size of the model being used as well as the number of examples in a mini-batch. We describe a novel, simple, and flexible adaptive optimization method with sublinear memory cost that retains the benefits of per-parameter adaptivity…

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Personalized Dynamic Pricing with Machine Learning: High Dimensional Covariates and Heterogeneous Elasticity

April 9, 2019 @ 4:00 pm - 5:00 pm

Gah-Yi Ban (London Business School)


We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers’ characteristics encoded as a $d$-dimensional feature vector. We assume a personalized demand model, parameters of which depend on $s$ out of the $d$ features. The seller initially does not know the relationship between the customer features and the product demand, but learns this through sales observations over a selling horizon of $T$ periods. We prove that the…

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March 2019

Automatic Computation of Exact Worst-Case Performance for First-Order Methods

March 12, 2019 @ 4:00 pm - 5:00 pm

Julien Hendrickx (UCLouvain)


Joint work with Adrien Taylor (INRIA) and Francois Glineur (UCLouvain). We show that the exact worst-case performances of a wide class of first-order convex optimization algorithms can be obtained as solutions to semi-definite programs, which provide both the performance bounds and functions on which these are reached.  Our formulation is based on a necessary and sufficient condition for smooth (strongly) convex interpolation, allowing for a finite representation for smooth (strongly) convex functions in this context. These results allow improving the…

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February 2019

Coded Computing: A Transformative Framework for Resilient, Secure, and Private Distributed Learning

February 26, 2019 @ 4:00 pm - 5:00 pm

Salman Avestimehr (University of Southern California)


This talk introduces "Coded Computing”, a new framework that brings concepts and tools from information theory and coding into distributed computing to mitigate several performance bottlenecks that arise in large-scale distributed computing and machine learning, such as resiliency to stragglers and bandwidth bottleneck. Furthermore, coded computing can enable (information-theoretically) secure and private learning over untrusted workers that is gaining increasing importance in various application domains. In particular, we present CodedPrivateML for distributed learning, which keeps both the data and the…

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Safeguarding Privacy in Dynamic Decision-Making Problems

February 19, 2019 @ 4:00 pm - 5:00 pm

Kuang Xu (Stanford University)


The increasing ubiquity of large-scale infrastructures for surveillance and data analysis has made understanding the impact of privacy a pressing priority in many domains. We propose a framework for studying a fundamental cost vs. privacy tradeoff in dynamic decision-making problems. The central question is: how can a decision maker take actions that are efficient for her goal, while simultaneously ensuring these actions do not inadvertently reveal her private information, even when observed and analyzed by a powerful adversary? We will…

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December 2018

Symmetry, Bifurcation, and Multi-Agent Decision-Making

December 10, 2018 @ 4:00 pm - 5:00 pm

Naomi E. Leonard (Princeton University)


Prof. Leonard will present nonlinear dynamics for distributed decision-making that derive from principles of symmetry and bifurcation. Inspired by studies of animal groups, including house-hunting honeybees and schooling fish, the nonlinear dynamics describe a group of interacting agents that can manage flexibility as well as stability in response to a changing environment. Bio: Prof. Naomi Ehrich Leonard is Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering and associated faculty in Applied and Computational Mathematics at Princeton University. She is…

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November 2018

Transportation Systems Resilience: Capacity-Aware Control and Value of Information

November 26, 2018 @ 4:00 pm - 5:00 pm

Saurabh Amin (MIT)


Resilience of a transportation system is its ability to operate under adverse events like incidents and storms. Availability of real-time traffic data provides new opportunities for predicting travelers’ routing behavior and implementing network control operations during adverse events. In this talk, we will discuss two problems: controlling highway corridors in response to disruptions and modeling strategic route choices of travelers with heterogeneous access to incident information. Firstly, we present an approach to designing control strategies for highway corridors facing stochastic…

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Modeling Electricity Markets with Complementarity: Why It’s Important (and Fun)

November 19, 2018 @ 4:00 pm - 5:00 pm

Benjamin Hobbs (Johns Hopkins University)


Electric power: done wrong, it drags the economy and environment down; done right, it could help to create a more efficient, brighter, and cleaner future. Better policy, planning, and operations models--both simple analytical, and complex computational ones--are essential if we're going to do it right. Better modeling is also fun, as the math of electricity models is inherently interesting and revealing --models often show flaws in our intuition. Used intelligently, models can point us towards better regulations, investments, and operating…

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