MSSISS 2017 extended and enhanced the success of previous symposia. In 2017, an inaugural Thursday night event showcased a junior faculty keynote (Assistant Professor Eric Schwartz), speed oral and poster combo presentations, and undergraduate posters. We were also honored to have Professor Dimitris Bertsimas, the Boeing Professor of Operations Research at MIT challenge traditional thinking in our field and propose the use of advanced computing and optimization as practable (practically tractable) solutions to modern challenges.
The field of Statistics has historically been linked with Probability Theory. However, some of the central problems of classification, regression and estimation can naturally be written as optimization problems. While continuous optimization approaches has had a significant impact in Statistics, mixed integer optimization (MIO) has played a very limited role, primarily based on the belief that MIO models are computationally intractable.
In this talk, we demonstrate that modern convex, robust and especially mixed integer optimization methods, when applied to a variety of classical Machine Learning (ML) /Statistics (S) problems can lead to certifiable optimal solutions for large scale instances that have often significantly improved out of sample accuracy compared to heuristic methods used in ML/S.Specifically, we report results on
- The classical variable selection problem in regression currently solved by Lasso heuristically.
- We show that robustness and not sparsity is the major reason of the success of Lasso in contrast to widely held beliefs in ML/S.
- A systematic approach to design linear and logistic regression models based on MIO.
- Optimal trees for classification solved by CART heuristically.
- Robust classification including robust Logistic regression, robust optimal trees and robust support vector machines.
- Sparse matrix estimation problems: Principal Component Analysis, Factor Analysis and Covariance matrix estimation.
In all cases we demonstrate that optimal solutions to large scale instances (a) can be found in seconds, (b) can be certified to be optimal in minutes and (c) outperform classical approaches. Most importantly, this body of work suggests that linking ML/S to modern optimization leads to significant advances.
Dimitris Bertsimas is currently the Boeing Professor of Operations Research and the co-director of the Operations Research Center at the Massachusetts Institute of Technology. His research interests include analytics, optimization and their applications in a variety of industries. He has co-authored more than 200 scientific papers and recently published the book “The Analytics Edge’’. He is a member of the US National Academy of Engineering, and an INFORMS fellow. He has received several research awards including the Philip Morse lectureship award 2013), the William Pierskalla award for best paper in health care (2013), the best paper award in Transportation Science (2013), the Farkas prize (2008), the Erlang prize (1996), the SIAM prize in optimization (1996), the Bodossaki prize (1998) and the Presidential Young Investigator award (1991-1996).
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MSSISS 2017 Presentation Awards:
Best Oral Presentation:
Selin Merdan & Christine Barnett (IOE) – Data Analytics for Optimal Staging Decisions for Newly-Diagnosed Prostate Cancer Patients
Oral Presentation Honorable Mention:
Morteza Noshad (EECS) – Direct Estimation of Information Divergence Using Nearest Neighbor Ratios
Best Speed Session:
Arya Fahari (EECS) – On the Search for Lead Pipes in Flint
ASA Prize for Best Poster Presentation:
Wesley Marrero (IOE) – Projections of Non-Alcoholic Steatohepatitis Related Liver Transplantation Waitlist Additions
Departmental Poster Presentation Winners:
- Brian Segal (Biostatistics) – Tests of Matrix Structure for Construct Validation
- David Hong (EECS) – Asymptotic Performance of PCA for High-Dimensional Heteroscedastic Data
- Nicholas Seewald (Statistics) – Sample Size Considerations for the Analysis of Continuous Repeated-Measures Outcomes in Sequential Multiple-Assignment Randomized Trials
- Iago Santos Muraro (Survey Methodology) – Optimal Timing for Incentive Changes in a Long-Standing Panel Survey with High Calling Volume
Best Undergraduate Poster Presentation:
Katherine Li (Statistics) – ReVibe: Recalling everyday moments with context
MSSISS 2017 Student Organizing Committee
|Colleen McClain||Survey Methodologyemail@example.com|
MSSISS 2017 Faculty Advisory Committee
|Brady West||Survey Methodologyfirstname.lastname@example.org|