Conference Schedule

(Subject to minor changes)

Industry Panel

Nate Reynolds,Senior Data Analyst at the Library of Congress

Nathaniel Reynolds is a senior data analyst at the Library of Congress, Federal Research Division. Nathaniel works on applied data analytics projects including survey analysis, financial modeling, interactive dashboards, and increasingly today, ramping up data preparedness for AI use. Nathaniel also teaches part-time as adjunct faculty for the Data Analytics and Computational Social Science (DACSS) program at his alma mater, the University of Massachusetts Amherst.

Amit Arora, Principal Solutions Architect at AWS

Amit Arora is a Principal Solutions Architect – AI/ML at AWS and an Adjunct Lecturer at Georgetown University. Previously, he was a data scientist at Hughes Network Systems.He is an experienced cloud architect and data science professional specializing in applied ML, data pipelines and deploying ML in production. As part of his current role at AWS he helps enterprise customers use cloud-based machine learning platforms and services to rapidly scale their innovations.

Michael Rossetti, Data Analyst at Google

Michael Rossetti is a data scientist, software engineer, and machine learning researcher. He has worked as a polling data analyst for a winning US Presidential campaign, a data analytics director for a Silicon Valley startup, and a technology consultant for the US Government. He teaches courses in data science, computer science, and software development, and conducts research in applied machine learning.

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).

Feifang Hu, Chair, Department of Statistics- George Washington University

Feifang Hu is currently the chair of the Department of Statistics at the George Washington University. He earned his PhD in statistics at the University of British Columbia in Vancouver. His research focuses on statistical issues in personalized medicine, bioinformatics, biostatistics, and financial econometrics. Hu has been awarded the Career Award from the National Science Foundation and served as a fellow at the Institute of Mathematical Statistics and American Statistical Association.

Hu is the author of a groundbreaking textbook “Theory of Response-Adaptive Randomization”, and multiple influential FDA white papers.  He served as associate editor for the Journal of the American Statistical Association (2011-2020), The Annals of Statistics (2007-2012), and Statistics and Its Interface (2007-2016). In 2018, Hu received the CCAS Dean’s Research Chair Award from George Washington University for his outstanding research in the design of clinical trials.

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.

Zixiang Xu | Ph.D. candidate in Statistics- Georgetown University

Zixiang Xu is a Ph.D. candidate in Statistical Science at George Mason University, specializing in selection bias, data shifts, and statistical modeling for observational and clinical data. His research develops semiparametric and shape-constrained methods with applications ranging from astronomical observations to medical and real-world evidence analysis. Zixiang’s work has been recognized with awards including the Washington Statistical Society Outstanding Graduate Student Award.

Selected photos from StatConnect 2025: