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INFORMS Workshop on Data Mining & Decision Analytics

October 23, 2021, Anaheim, CA

Organized by

INFORMS Data Mining Society logo

The Data Mining Section of INFORMS is organizing the 16th INFORMS Workshop on Data Mining and Decision Analytics in conjunction with 2021 INFORMS 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.

To participate, a full paper must be submitted before the deadline for consideration. The workshop committee also announces the best paper competition in both theoretical and applied research tracks. All accepted papers are automatically considered for the best paper competition in the chosen track. Suitable best papers and runner-ups will be recommended for fast-track submission to INFORMS Journal on Data Science (IJDS).

Topics of Interest

Include, but are not limited to:

  • Data Science and Artificial Intelligence
  • Large-Scale Data Analytics and Big Data
  • Reinforcement Learning
  • 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 12: Paper submission close
September 1: Final review decision
September 14: Workshop on Data Mining and Decision Analytics registration deadline

DM Workshop Co-Chairs

Eyyub Kibis, Montclair State University
Chen Kan, University of Texas at Arlington
Nathan Gaw, Georgia Institute of Technology 

DM 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

Papers submission guideline

  • 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 this link. Late submission will not be considered for further review.
  • Copyright: The DM workshop will not retain the copyrights on the papers; so, the authors are free to submit their papers to other outlets, unless they want the paper to be considered for fast-track submission to INFORMS Journal on Data Science (IJDS).
  • Fast-track submission to IJDS: The IJDS publishes innovative and impactful data science methodologies contributing to decision making in business, management, and industry. To increase chances to be considered for fast-track submission, a paper should include novel methodology or existing methodology applied in a completely new way (“methodological discovery”).

Keynote Speakers

  • Jayashree Kalpathy-Cramer (Associate Professor, Harvard Medical School), Jayashree Kalpathy-Cramer is the Director of the QTIM lab and the Center for Machine Learning at the Athinoula A. Martinos Center for Biomedical Imaging and an Associate Professor of Radiology at MGH/Harvard Medical School. Dr. Kalpathy-Cramer is also Scientific Director at MGB Center for Clinical Data Science, a Senior Scientist at the American College of Radiology Data Science Institute and a member of the RSNA Machine Learning Steering Sub-committee. Her research interests include medical image analysis, machine learning and artificial intelligence for applications in radiology, oncology and ophthalmology. Dr. Kalpathy-Cramer has authored over 150 peer-reviewed publications and has written over a dozen book chapters. She is a Deputy Editor for the Radiology-AI journal, an Associate editor for the BJR and Editorial Board Member for TVST.
  • Ben Amaba (Global CTO, IBM), Dr. Ben Amaba holds a Ph.D. degree in Industrial & Systems Engineering, an M.S. degree in Engineering and Operations, and a B.S. degree in Electrical Engineering. Dr. Amaba is a registered and licensed Professional Engineer in several states with International Registry; certified in Production, Operations, and Inventory Management by APICS®; LEED® Accredited Professional (Leadership in Energy & Environment Design); and certified in Corporate Strategy by Massachusetts Institute of Technology in Cambridge, Massachusetts. He is responsible for industrial manufacturing, infrastructure, engineering, and supply chain solutions. Dr. Amaba is the Global Chief Technology Officer for the Industrial Sector – IBM Cloud and Cognitive. Dr. Amaba’s focus and interest is in artificial intelligence, data analytics, software engineering, Industrial Internet of Things (IIoT), 5G, and cloud technology.

Tutorials

Hands-on Tutorial (Virtual): Machine Learning Made Easy with PyCaret

Speaker: Moez Ali, Data Scientist and Creator of PyCaret (An open-source, low-code machine learning library in Python).

Abstract: PyCaret is an open-source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of environment. This talk is a practical demo on how to use PyCaret in your existing workflows and supercharge your data science team’s productivity.

Previous Workshops

15th Virtual INFORMS Workshop on Data Mining and Decision Analytics

14th INFORMS Workshop on Data Mining and Decision Analytics

13th INFORMS Workshop on Data Mining and Decision Analytics

Registration

Onsite

$50 for students and retirees

$150 for professionals

Virtual

$35 for students and retirees

$100 for professionals

Click here to register for this event.

Thank you to our sponsors!

VCU Master of Decision Analytics
Pamplin College of Business, Center for Business Analytics, Virginia Tech