Members #_votes Question
– JR, TG, JW, LI, SL 5 —
– GG, JZ, JL, PDM 4 What book are you currently reading? – XL, JW 2 Predict when the new Sci bldg will officially open – TXL 1 1) Predict when your next publication will be released; 2) ???
– PDM: 1 1) Suggested blog/twitter channel; 2) Favorite computer you’ve owned
– JX 1 What’s your favorite research project (on which you’ve previously worked), OR what’s your favorite research paper (which you’ve read)?
– DL 1 What city/country would you most like to live? – SKL 1 Predict biggest breakthrough in biomed in 2019 – YAN 1 1) What do you like most about Farnam?; 2) What do you least like about Farnam?
– BL 1 What programming language do you hate the most? – ZT 1 What journal do you read the most?
– CY 1 How many questions have we posed as part of introductions this year?
– DC 1 What’s your (2nd-favorite) textbook? (your favorite textbook was already posed as a question at a P1 mtg on Sept 7 2018) – LS 1 What’s your favorite ethnic food?
– SK 1 What’s the next textbook you’d like to read? – XS 1 Predict how many new members will join the lab this year? – YY 1 In total, how many emails did you receive last year? – MTG 1 What’s your favorite Science magazine cover from 2018? See vote is on Twitter and Facebook — Search for the tag #SciMagMarchMadness:
– FN 1 How many times to MG re-tweet or tweet in a period of time? – HM 1 1) Who’s your favorite filmmaker; 2) What film would you like to see again?
– MG 1 1) PLoS CB best paper nominations (see Mar 23 email from MG); 2) How many important questions are there to answer – John 1 If you were to learn a natural human language, what would it be?
link for slides:
photos in today’s dropbox
“Distributionally Robust Learning with Applications to Health Analytics”
Speaker: Yannis Paschalidis
http://sites.bu.edu/paschalidis/ (link is external)
Wednesday, April 17, 2019 – 12:00pm
Yale Institute for Network Science, 17 Hillhouse Avenue, 3rd floor Abstract: We 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 assume that training data are contaminated with (unknown) outliers. The learning problem is formulated as the problem of minimizing the worst case expected loss over a family of distributions within a certain Wasserstein ball centered at 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. Speaker Bio: 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 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. 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.
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