The Data Mining Section of INFORMS is organizing the 15th INFORMS Virtual Workshop on Data Mining and Decision Analytics in conjunction with the 2020 INFORMS Virtual Annual Meeting. You are cordially invited to join us and share your recent research work with peers from data mining, decision, analytics, and artificial intelligence.

Topics of interest include, but are not limited to:

• • Large-Scale Data Analytics and Big Data

  • Data-Driven Decision-Making

  • Interpretable Data Mining

  • Simulation/Optimization in Data Analytics

  • Network Analysis and Graph Mining

  • Privacy & Fairness in Data Science

  • Bayesian Data Analytics

  • Healthcare Analytics

  • Longitudinal Data Analysis

  • Causal Mining (Inference)

  • Anomaly Detection

  • Deep Learning

  • Emerging Data Analytics in Industrial Applications

  • Analytics in Social Media & Finance

  • Reliability & Maintenance

  • Visual Analytics

  • Web Analytics/Web Mining

  • Text Mining & Natural Language Processing

  • Ethics and Security in Data Mining

  • Fairness in Machine Learning

Timeline

• • May 1: Paper submission begin

  • August 1 Extended to September 1: Paper submission close

  • September 1 October 15: Final review decision

  • September 14 October 20: Workshop on Data Mining and Decision Analytics registration deadline

  • November 1: Pre-recorded presentation upload (the instruction is sent from INFORMS).

Guidelines for Paper Submission

• • MS Words and Latex templates are available.

    • 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 before the deadline.

    • Late submission will not be considered for further review

  • Copyright: The DMDA workshop will not retain the copyright on the papers. Authors are free to submit their papers to other outlets. The full papers will not be posted online.

Program Schedule

Note that the workshop is scheduled for EST.

9:00-10:00 Joint Editor Panel Discussion (Live)

10:10-11:10 Industrial Keynote (Brett Wujek, SAS) (Live)

11:10-11:30 Break and Network

11:30-13:00 session A (5 breakouts including best applied paper competition)

13:00-13:30 Lunch Break

13:30-14:30 Academic Keynote (Ram Ramesh, University of Buffalo) (Live)

14:30-14:50 Break and Network

14:50-16:20 session B (5 breakouts including best theoretical paper competition)

16:20-16:30 Adjournment and Network

The full workshop program can be downloaded now.

Keynotes

Green Data Analytics of Supercomputing from Massive Sensor Networks: Does Workload Distribution Matter?

Dr. Ram Ramesh is a Professor at the Department of Management Science & Systems in the School of Management, State University of New York at Buffalo. His research spans the analytics of cloud and high performance computing, database optimization and decision analysis. In particular, his contemporary works deal with predictive and prescriptive analytics of availability-aware cloud computing and energy-efficient high performance computing systems, optimal design of service-level contracts in cloud computing markets, and predictive analytics of health information exchanges. Methodologically, his research is established in predictive modeling, mathematical programming and stochastic optimization. His research has been funded by the National Science Foundation, Google Research, Samsung, Raytheon and Westinghouse, besides various U.S. military research programs including U.S. Army Research Institute, U.S. Air Force Office of Scientific Research, U.S. Air Force Research Laboratory and the U.S. Naval Training Systems Center. He serves as an area editor for the machine learning & knowledge management area of INFORMS Journal on Computing and is a founding co-editor-in-chief of Information Systems Frontiers (published by Springer).

Ramesh has published extensively on the research topics above. His publications appear in such journals as INFORMS Journal on Computing, Information Systems Research, IEEE Transactions on Computers, IEEE Transactions on Knowledge and Data Engineering, ACM Transactions On Database Systems, ACM Transactions on the Web, ACM Transactions on MIS, IEEE Transactions on Systems, Man and Cybernetics, Naval Research Logistics, Management Science, Communications of the ACM, Journal of the American Society for Information Science and Technology (JASIST), Journal of American Medical Informatics Association (JAMIA), and Applied Artificial Intelligence. He has served in numerous leadership positions such as the Chair of department of Management Science & Systems and Associate Dean for Research at the School of Management at SUNY at Buffalo, and board member of Infotech Niagara (an association of CIOs in Western New York). He designed, developed and implemented a dual-degree MS/MBA program at Bangalore, India, in collaboration with Amrita University and Hewlett-Packard. He serves as its program director since 2007.

Oh Grow Up: Raising your Analytics from Infancy to Maturity”

Dr. Brett Wujek is a Principal Product Manager for Machine Learning at SAS. He helps evangelize and guide the direction of artificial intelligence development at SAS, particularly in the areas data mining, machine learning, and reinforcement learning. Brett previously worked as a Principal Data Scientist at SAS in the Data Mining and Machine Learning division of R&D supporting technical projects and customer engagements, with a focus on the development and application of automation in machine learning. Prior to joining SAS, Brett led the development of process integration and design exploration technologies at Dassault Systèmes, helping architect and implement industry-leading computer-aided optimization software for product design applications. His formal education is in design optimization methodologies and surrogate modeling. He received his PhD from the University of Notre Dame in 1997 for his work in developing efficient algorithms and automation strategies for multidisciplinary design optimization.

Workshop Committee

The DMDA Workshop Co-Chairs

• Chun-An (Joe) Chou, Northeastern University

• Eyyub Kibis, Montclair State University

• Chen Kan, University of Texas at Arlington

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)

• Durai Sundaramoorthi (Washington University)

Registration

$0 for students (student must be first author)

$30 for non-students

Sponsor

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

© 2020 INFORMS Workshop on Data Mining and Data Analytics. All rights reserved.