The Data Mining Section of INFORMS is organizing the 2019 INFORMS Workshop on Data Mining and Decision Analytics on October 19, 2019 in Seattle in conjunction with the 2019 INFORMS Annual Meeting. You are invited to join us and submit and present you research papers. The workshop committee also announce the best paper competition in both theoretical and applied research tracks; all papers are automatically considered for the best paper competition in the chosen track.

Topics of interest include, but are not limited to:

• Data Science
• Large-Scale Data Analytics and Big Data – Data-Driven Decision-Making
• Visual Analytics
• Web Analytics/Web Mining
• Social Media Analytics
• Classification, Clustering, and Feature Selection – Text Mining
• Reliability & Maintenance
• Bayesian Data Analytics
• Healthcare Analytics
• Simulation/Optimization in Data Analytics
• Interpretable Data Mining
• Longitudinal Data Analysis
• Causal Mining (Inference)
• Analytics in Social Media & Finance
• Anomaly Detection
• Other Industrial Applications of Data Science
• Deep Learning
• Natural Language Processing

Papers should follow the below listed guidelines

• Maximum of 10 pages (including abstract, tables, figures, and references)
• Single-spacing and 11-point font with one-inch margins on four sides
• Papers must be submitted via the link or to the following email: dmdaworkshop@gmail.com before the deadline on 8/19.
  • Late submission will not be considered for further review.
• Copyright: The DMDA workshop will not retain the copyrights on the papers; so, the authors are free to submit their papers to other outlets. The full papers will not be posted online.

Program Schedule

The full workshop program of all accepted papers can be downloaded at the link.

07:00 AM - 08:00 AM Breakfast (Grand Ballroom D)

08:00 AM - 09:00 AM DS Keynote Speech (Grand Ballroom D) by Dr. Rajiv J. Krishnamurthy, Facebook

09:00 AM - 10:20 AM Session A

10:20 AM - 10:40 AM Break (Grand Ballroom D)

10:40 AM - 12:00 PM Session B

12:00 PM - 14:00 PM Lunch (Grand Ballroom D)

14:00 PM - 15:00 PM DMDA Keynote Speech (Grand Ballroom D) by Prof. Mohsen Bayati, Stanford University

15:00 PM - 15:20 PM Break (Grand Ballroom D)

15:20 PM - 17:00 PM Session C

Keynote Speakers

Rajiv Krishnamurthy

Rajiv Krishnamurthy is currently a Director of Data Science at Facebook Inc, Menlo Park, CA. He joined Facebook in 2011, since then he has led data science teams to optimize Facebook and its backend infrastructure while its userbase grew rapidly to over a billion plus users worldwide. Prior to Facebook, he worked in industry roles that focused on matching-algorithms and predictive analytics in the digital consumer space. Overall, Rajiv has been at the intersection of data, technology and decision making for the last fifteen years - first as an academic and then as a practitioner. As industry datasets evolve, tools mature and new business models emerge, he is passionate about bringing industry and academia closer to solve hard, practical problems. He has a PhD in Management Science, Masters in Computer Science, Masters in Telecom Engineering all from the University of Texas at Dallas, his undergraduate degree is in Electrical Engineering from NIT Warangal, India.

Title: Data Science: 2019 and Beyond – A Practitioner’s Perspective

Mohsen Bayati

Mohsen received a BS in Mathematics from Sharif University of Technology and a PhD in Electrical Engineering from Stanford University in 2007. His dissertation was on algorithms and models for large-scale networks. During the summers of 2005 and 2006 he interned at IBM Research and Microsoft Research respectively. He was a Postdoctoral Researcher with Microsoft Research from 2007 to 2009 working mainly on applications of machine learning and optimization methods in healthcare and online advertising. In particular, he helped develop a system for predicting hospital patient readmissions and obtained a decision support mechanism for allocating scarce hospital resources to post-discharge care. Their system is currently used in several hospitals across US and Europe. He was a Postdoctoral Scholar at Stanford University from 2009 to 2011 with a research focus in high-dimensional statistical learning. In 2011 he joined Stanford University as a faculty, and since 2015 he is an associate professor of Operations, Information, and Technology at Stanford University Graduate School of Business. He was awarded the INFORMS Healthcare Applications Society best paper (Pierskalla) award in 2014 and in 2016, INFORMS Applied Probability Society best paper award in 2015, and National Science Foundation CAREER award.

Title: Online Personalization of Many Decisions with High-Dimensional Covariates

Abstract: A central problem in personalized decision-making is to learn decision outcomes as functions of individual-specific covariates (contexts). Current algorithms on this topic (specifically, k-armed contextual bandits with d dimensional covariates) have regret bounds that scale as polynomials of degree at least two in k and d. In this talk, we present a new algorithm (REAL-Bandit) that targets the learning towards shared similarities among decisions. We also prove that the regret of our algorithm scales by r(k+d) when r is rank of the k by d matrix of unknown parameters. REAL-Bandit relies on ideas from low-rank matrix estimation literature and a new row-enhancement subroutine that yields sharper bounds for estimating each row of the parameter matrix that may be of independent interest. Moreover, we show empirically that REAL-Bandit algorithm outperforms existing benchmarks.

Workshop Committee

The DMDA Workshop Co-Chairs

• Tong Wang, University of Iowa
• Roy Jafari, California Polytechnic State University
• Minh Pham, Rochester Institute of Technology
• Chun-An (Joe) Chou, Northeastern University
• Ozden Gur Ali, Koc University

The DMDA Workshop Management Committee

• George Runger (Arizona State University)
• Cynthia Rudin (Duke University)
• Paul Brooks (Virginia Commonwealth University)
• Onur Seref (Virginia Tech)
• Asil Oztekin (University of Massachusetts Lowell)
• Matthew Lanham (Purdue University)
• Ramin Moghaddass (University of Miami)

Sponsors

Please contact us at dmdaworkshop@gmail.com if you have any questions.