Event Category: Operations Research Seminar Series

  • Cancer Etiology, Evolution, and Earlier Detection

    Cancer Etiology, Evolution, and Earlier Detection

    2/20/20 | 4:15pm | E51-325 Reception to follow.         Cristian Tomasetti Associate Professor Johns Hopkins University Abstract: A fundamental question in cancer research and cancer prevention is what causes cancer. In this talk, recent findings that have challenged the core of our current understanding of cancer etiology will be presented, together with mathematical models of tumor evolution. In the final…

  • Solving Tall Dense Linear Programs in Nearly Linear Time

    Solving Tall Dense Linear Programs in Nearly Linear Time

    2/13/20 | 4:15pm | E51-325 Reception to follow.         Aaron Sidford Assistant Professor Stanford Abstract: Linear programming is one of the most well-studied problems in continuous optimization and a prominent proving ground for new algorithms. In this talk, I will survey recent advances in improving the time complexity of solving linear programs…

  • Combinatorial Optimization Augmented with Machine Learning

    Combinatorial Optimization Augmented with Machine Learning

    2/6/20 | 4:15pm | E51-325 Reception to follow.         Ben Moseley Assistant Professor Carnegie Mellon Abstract: Combinatorial optimization often focuses on optimizing for the worst-case. However, the best algorithm to use depends on the “relative inputs”, which is application specific and often does not have a formal definition.   The talk gives…

  • Robust Mission Network Analysis

    Robust Mission Network Analysis

    11/14/19 | 4:15pm | E51-335 Reception to follow.         Les Servi Chief Scientist The MITRE Corporation Abstract: Cyber security and asset protection efforts are becoming more difficult as technological advances broaden the scope and scale of integrated systems. Mission networks organize and quantify the dependencies between cyber assets in the system. The…

  • Warning Against Recurring Risks: An Information Design Approach

    Warning Against Recurring Risks: An Information Design Approach

    11/21/19 | 4:15pm | E51-335 Reception to follow.         Francis de Vericourt Professor European School of Management and Technology Abstract: The World Health Organization seeks effective ways to alert its member states about global pandemics. Motivated by this challenge, we study a public agency’s problem of designing warning policies to mitigate potential…

  • What Should We Do About the Opioid Epidemic? Models to Support Good Decisions

    What Should We Do About the Opioid Epidemic? Models to Support Good Decisions

    11/7/19 | 4:15pm | E51-335 Reception to follow.         Margaret Brandeau Professor Stanford University Abstract: The US is currently experiencing an epidemic of drug abuse caused by prescription opioids and illegal opioid use, including heroin. In addition to crime and social problems, rising levels of drug abuse have led to a sharp…

  • Towards an Average-case Complexity of High-dimensional Statistics

    Towards an Average-case Complexity of High-dimensional Statistics

    10/31/19 | 4:15pm | E51-335 Reception to follow.         Guy Bresler Associate Professor MIT Abstract: The prototypical high-dimensional statistical estimation problem entails finding a structured signal in noise. These problems have traditionally been studied in isolation, with researchers aiming to develop statistically and computationally efficient algorithms, as well as to try to understand the fundamental limits governing…

  • Crowdsourcing Information in Informal Supply Chains

    Crowdsourcing Information in Informal Supply Chains

    10/17/19 | 4:15pm | E51-335 Reception to follow.         Joann de Zegher Assistant Professor MIT Abstract: Access to timely and accurate market information is critical for sound decision-making, but it is currently missing for many decision-makers in the developing world. This is notably the case for decision-makers that operate in informal work…

  • Multinomial Logit Contextual Bandits

    Multinomial Logit Contextual Bandits

    10/10/19 | 4:15pm | E51-335 Reception to follow.         Garud Iyengar Professor Columbia University Abstract: We consider a dynamic assortment selection problem where the goal is to offer a sequence of assortments that maximizes the expected cumulative revenue. The feedback here is in the form of the item that the user picks…

  • A Data-Driven Approach to Multi-Stage Linear Optimization

    A Data-Driven Approach to Multi-Stage Linear Optimization

    9/26/19 | 4:15pm | E51-335 Reception to follow.         Brad Sturt PhD Student MIT Abstract: We present a data-driven approach for solving multi-stage stochastic linear optimization problems in which uncertainty is correlated across stages. The proposed approach chooses decision rules which perform best when averaging over sample paths of a stochastic process; however,…