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Subhadeep Paul
Associate Professor, Department of Statistics
Co-Director, CoP on Foundations of Data Science and AI, Translational Data Analytics Institute
Faculty member, Interdisciplinary Ph.D. program in Biostatistics
The Ohio State University
Cockins Hall 231
1958 Neil Ave, Columbus, OH 43210
Email: paul.963@osu.edu

 

What’s new?

  • [Jan 2025] We received the OSU Presidential Research Excellence Accelerator Grant on “Heterogeneous Transfer And Federated Learning For Digital Twin In Unmanned Aerial Vehicles.” (with Debdipta Goswami, Dept of Mechanical and Aerospace Engg). Department news feature here. TDAI news feature here.
  • [Dec 2024] Posted a new preprint to Arxiv on heterogeneous transfer learning with feature mismatch with Massimiliano Russo, Statistics, and Jae Ho Chang, Statistics graduate student.
  • [Dec 2024] We received a College of Arts and Sciences at OSU exploration grant for “Transfer, Federated, and Private Statistical Learning for Trustworthy AI.” (with Arnab Auddy, Statistics).
  • [Nov 2024] Our paper on non-negative matrix factorization for network data is accepted for publication at Statistica Sinica. (with Yuguo Chen, Statistics).
  • [Oct 2024] Our paper on identifying peer effects adjusting for latent homophily is accepted for publication at the Annals of Applied Statistics (With Keith Warren, Social Work, and Shanjukta Nath, Agricultural and Applied Economics).

Research Interests

  • Statistical inference in networks, multi-layer, and temporal networks
  • Transfer learning, Federated learning
  • Graphical models, Neuroimaging data analysis
  • Network peer effects, Network time series

Education

  • Ph.D. in Statistics, University of Illinois at Urbana-Champaign, 2012-2017.
  • Master of Science & Bachelor of Science (Hons.) in Statistics and Informatics, Indian Institute of Technology, Kharagpur, 2007-2012.

Editorial Services

About Me

I am an Associate Professor of Statistics at The Ohio State University (OSU). I am also the co-director of the community of practice on foundations of data science and AI at the Translational Data Analytics Institute (TDAI). My current research has two focus areas: (1) network analysis, including multilayer and temporal networks, network time series, and peer effect estimation, problems, (2) heterogeneous transfer learning and privacy-preserving federated learning under the general theme of statistical foundations of AI. I am interested in interdisciplinary collaborations, and in the past, I have collaborated with researchers in Computer Science, Neuroscience, Microbiology, Psychology, and Economics. My research has been funded by a joint NSF-NGIA grant from the Algorithms for Threat Detection program.