Keynote Speakers

Professor Teck Hua Ho

Senior Deputy President and Provost, Tan Chin Tuan Centennial Professor
National University of Singapore
Singapore

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Title: Does Big Data Solve Big Problems?

There is enormous excitement about big data because it promises to solve major societal problems. We assemble big data in transport and healthcare with the objectives of reducing the rate of car accidents, and preventing late-stage breast cancer. We show that big data must have certain properties in order for it to be useful in solving big societal problems.

Professor Yinyu Ye

K. T. Li Chair Professor of Engineering Management Science & Engineering and, by courtesy, Electrical Engineering
Stanford University
USA

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Title: Distributionally Robust Stochastic and Online Optimization

We present decision/optimization models/problems driven by uncertain and online data, and show how analytical models and computational algorithms can be used to achieve solution efficiency and near optimality.

  • First, we develop the so-called Distributionally or Likelihood Robust optimization (DRO) models and their algorithms in dealing stochastic decision problems when the exact uncertainty distribution is unknown but certain statistical moments and samples can be estimated.
  • Secondly, when decisions are made in presence of high dimensional stochastic data, handling joint distribution of correlated random variables can present a formidable task, both in terms of sampling and estimation as well as algorithmic complexity. A common heuristic is to estimate only marginal distributions and substitute joint distribution by independent (product) distribution. Here, we study possible loss incurred on ignoring correlations through the DRO approach, and quantify that loss as Price of Correlations (POC).
  • Thirdly, we describe an online combinatorial auction problem using online linear programming technologies. We discuss near-optimal algorithms for solving this surprisingly general class of online problems under the assumption of random order of arrivals and some conditions on the data and size of the problem.