学术报告第201979期

报告题目：Distributionally Robust Learning with Applications to Health Analytics

报告人： Yannis Paschalidis（波士顿大学）

报告时间：2019年11月27日 10:30--11:30

报告地点：南一楼 中 311

报告摘要：

I will present a distributionally robust optimization approach to learning predictive

models, using general loss functions that can be used either in the context of classification or regression. Motivated by medical applications, we consider a setting where training data may be contaminated with (unknown) outliers. The robust learning problem is formulated as the problem of minimizing the worst case expected

loss over a family of distributions that are close to the empirical distribution obtained from the training data. We will explore the generality of this approach, its robustness properties, its ability to explain a host of "ad-hoc" regularized learning methods, and we will establish rigorous out-of-sample performance guarantees.

Beyond predictions, we will discuss methods that can leverage the robust predictive

models to make decisions and offer specific personalized prescriptions and recommendations to improve future outcomes. We will provide some examples of medical applications of our methods, including predicting hospitalizations for chronic

disease patients, predicting hospital length-of-stay for surgical patients, and making treatment recommendations for diabetes and hypertension.

报告人简介：

Yannis Paschalidis is a Professor and Data Science Fellow in Electrical and Computer

Engineering, Systems Engineering, and Biomedical Engineering at Boston University. He is the Director of the Center for Information and Systems Engineering (CISE). He

obtained a Diploma (1991) from the National Technical University of Athens, Greece,

and an M.S. (1993) and a Ph.D. (1996) from the Massachusetts Institute of Technology (MIT), all in Electrical Engineering and Computer Science. He has been at Boston University since 1996. His current research interests lie in the fields of systems and control, networks, optimization, operations research, computational biology, and medical informatics.

Prof. Paschalidis' work has been recognized with a CAREER award (2000) from the

U.S. National Science Foundation, the second prize in the 1997 George E. Nicholson

paper competition by INFORMS, the best student paper award at the 9th Intl. Symposium of Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt 2011) won by one of his Ph.D. students for a joint paper, an IBM/IEEE Smarter Planet Challenge Award, and a finalist best paper award at the IEEE International Conference on Robotics and Automation (ICRA). His work on protein docking (with his collaborators) has been recognized for best performance in modeling selected protein-protein complexes against 64 other predictor groups (2009 Protein Interaction Evaluation Meeting). His recent work on health informatics won an IEEE Computer Society Crowd Sourcing Prize and a best paper award by the International Medical Informatics Associations (IMIA). He was an invited participant at the 2002 Frontiers of Engineering Symposium organized by the National Academy of Engineering, and at the 2014 National Academies Keck Futures Initiative (NAFKI) Conference. Prof. Paschalidis is a Fellow of the IEEE and the founding Editor-in-Chief of the IEEE Transactions on Control of Network Systems.