Abstract: 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 describe 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/or sample distributions 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.
Bio: Yinyu Ye is currently the K.T. Li Chair Professor of Engineering at Department of Management Science and Engineering and Institute of Computational and Mathematical Engineering, Stanford University. He is also the Director of the MS&E Industrial Affiliates Program. He received the B.S. degree in System Engineering from the Huazhong University of Science and Technology, China, and the M.S. and Ph.D. degrees in Engineering-Economic Systems and Operations Research from Stanford University. He is an INFORMS (The Institute for Operations Research and The Management Science) Fellow since 2012, and has received several academic awards including the winner of the 2014 SIAM Optimization Prize, the inaugural 2012 ISMP Tseng Lectureship Prize, the 2009 John von Neumann Theory Prize, inaugural 2006 Farkas Prize, the 2009 IBM Faculty Award, etc.. He is the Chief Scientist of one of the major commercial international optimization software companies.
Abstract: The explosive growth of Big Data and the emergence of data science promise to revolutionize industries from business to healthcare to government, and to change how we work, live and communicate. In this non-technical talk, I will discuss a few interesting problems to illustrate the potential benefits of Big Data as well as some challenging problems in the analysis of Big Data. The high demand for data scientists in a wide range of fields require substantially expansion of data science/business analytics programs in leading universities/business schools.
Bio: Tony Cai is Vice Dean of the Wharton School, Daniel H. Silberberg Professor of Statistics at Wharton School, professor of Applied Math & Computational Science Graduate Group, and senior scholar at the Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania. In 2006, Professor Cai was elected as a fellow of the Institute of Mathematical Statistics. In 2008, he won the COPSS Presidents’ Award — an award regarded as “the Nobel Prize” of Statistics — by the Committee of Presidents of Statistical Societies. In 2017, he was elected to the presidency of International Chinese Statistical Association (ICSA). He served as the editor of Annals of Statistics and has served on the editorial boards of many academic journals.
Professor Cai earned his PhD in Statistics at Cornell University, where he studied under Lawrence D. Brown, a member of United States National Academy of Sciences. Professor Cai has focused his research on Big Data Analytics, including the areas of statistical inference on high-dimensional data, statistical machine learning, large-scale multiple testing, functional data analysis, statistical decision theory, nonparametric function estimation, as well as applications to financial engineering and genomics.