StatConnect@AI | 2026
StatConnect is a one-day conference collaboration presented by multiple DMV-area universities: Georgetown University, George Mason University, George Washington University, the University of Maryland, and American University.
It is anchored at a different partner university each year. This year’s conference will be hosted by the MS Data Science & Analytics program at Georgetown University on March 27, 2026.
This conference is generously funded by the Initiative on Pedagogical Uses of Artificial Intelligence (IPAI) grant, secured by Dr. Purna Gamage, Program Director, MS Data Science & Analytics at Georgetown University.
Conference Schedule
(Subject to minor changes)
Time
- 9:00 -10:45 AM
- 11 – 11:15 AM
- 11:15 AM-12:15 PM
- 12:15 -1:30 PM
- 1:30-2:30 PM
- 2:30-3:30 PM
- 3:30-4:15 PM
- 4:15-5:45 PM
Event
- Professional Development Session
- Opening remarks
- Industry Panel
- Lunch + Poster Session
- Keynote speaker
- Student speakers
- Coffee break + Networking
- Faculty speakers
Faculty Speakers (more to be added soon)
Mahlet Tadesse, Professor | Chair, Department of Mathematics & Statistics- Georgetown University
Mahlet Tadesse is Professor and Chair in the Department of Mathematics and Statistics at Georgetown University. Her research focuses on the development of statistical and computational tools for the analysis of large-scale genomic data. She is particularly interested in stochastic search methods and Bayesian inferential strategies to identify structures and relationships in high-dimensional data sets.
She is an elected member of the International Statistical Institute (since 2006) and an elected fellow of the American Statistical Association (since 2013).
Student Speakers (more to be added soon)
Farshid Abadizaman | Ph.D. candidate in Applied Mathematics- Georgetown University
Farshid Abadizaman is a Ph.D. candidate in Applied Mathematics, specializing in Bayesian learning for high-dimensional biological data. His research develops scalable and interpretable statistical models to uncover latent structures in -omics datasets, using stochastic search variable selection, sparse graphical modeling techniques, mixture models, and Markov chain Monte Carlo methods. His work supports applications in biomarker discovery, therapeutic target identification, and precision medicine.
Selected photos from StatConnect 2025:
