Srinivasan (Srini) Venkatramanan

About

I am a Research Assistant Professor at the Biocomplexity Institute, University of Virginia, where I work on computational modeling and simulation for biological and socio-technical systems. My work spans network science, stochastic processes, and agentic AI systems, applied to domains including epidemiology, forced migration, healthy buildings, and neuroscience. I have published 100+ technical articles (3600+ citations on Google Scholar, h-index 32) across diverse venues such as PNAS, Nature Communications, ICML, and KDD, and received a best paper award at IEEE BigData 2022. I have contributed to national collaborative efforts such as CDC FluSight, the COVID-19 Forecast Hub, and the Scenario Modeling Hubs, and have led or co-led multiple CDC-, CSTE-, NSF-, NIH-, and ARPA-H-funded projects on computational modeling for infectious disease epidemiology. I was a recipient of UVA's Collaborative Excellence in Public Service Award in 2022 for my work supporting the Virginia Department of Health during its COVID-19 response. I have also managed several academia-industry partnerships with Google, Metaculus, Kinsa Health, and Signature Science.

I mentor graduate and undergraduate students on these problems most years. If you are looking to join, see working with me.

News

• Sep 2026 New preprint: multi-season evaluation of CDC FluSight categorical trend forecasts.
• Aug 2026 Two abstracts accepted at iCSD 2026, hosted at UVA in September.
• Aug 2026 New preprint: EpiFlow, improving the utility of wastewater signals for disease forecasting.
• Jul 2026 New preprint: EpiNarrate, agentic generation of grounded narratives from epidemiological data.
• Jul 2026 Invited talk on PatchSim for pandemic response support at the 2026 SIAM Conference on the Life Sciences.

Professional Experience

• 09/2021 – nowResearch Assistant Professor · Biocomplexity Institute, University of Virginia
• 10/2018 – 09/2021Research Scientist · Biocomplexity Institute & Initiative, University of Virginia
• 12/2017 – 10/2018Computational Health Data Scientist · Biocomplexity Institute, Virginia Tech
• 02/2015 – 12/2017Postdoctoral Associate · Biocomplexity Institute, Virginia Tech
• 05/2014 – 11/2014Research Assistant · Dept. of Information Engineering, Chinese University of Hong Kong
• 05/2007 – 08/2007Student Intern · Bell Research Labs India

Education

• 08/2008 – 08/2014Ph.D., Electrical & Communication Engineering · Indian Institute of Science
  Thesis: Influence Dynamics on Social Networks · Advisor: Anurag KumarDefense slides
• 08/2004 – 06/2008B.E., Electronics & Communication Engineering · College of Engineering Guindy, Anna University

• R. Datta, Z. Guan, ..., SV, N. Ramakrishnan, and A. Vullikanti, “Agentic Framework for Epidemiological Modeling”, International Conference on Machine Learning (ICML), 2026
• B. Espinoza, SV, A. S. Warren, B. L. Lewis, H. V. Poor, S. A. Levin, and M. V. Marathe, “Integrated framework to study genomic surveillance of selective sweeps in multivariants dynamics”, Proceedings of the National Academy of Sciences, 123(11), e2521031123, 2025
• S. Mathis, A. Webber, ..., SV, ..., M. Biggerstaff, R. Borchering, “Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations”, Nature Communications 15.1, 6289, 2024
• E. Howerton, L. Contamin, ..., SV, ..., K. Shea, C. Viboud, and J. Lessler, “Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty”, Nature Communications 14.1, 7260, 2023
• B. Espinoza, A. Adiga, SV, A. S. Warren, ..., S. Levin, and M. Marathe, “Coupled models of genomic surveillance and evolving pandemics with applications for timely public health interventions”, Proceedings of the National Academy of Sciences 120.48, e2305227120, 2023
• P. V. Prasad, M. K. Steele, C. Reed, ..., SV, ..., and M. Biggerstaff, “Multimodeling approach to evaluating the efficacy of layering pharmaceutical and nonpharmaceutical interventions for influenza pandemics”, Proceedings of the National Academy of Sciences 120.28, e2300590120, 2023
• P. Bhattacharya, J. Chen, S. Hoops, D. Machi, B. Lewis, SV, ..., C. Barrett, and M. Marathe, “Data-Driven Scalable Pipeline using National Agent-Based Models for Real-time Pandemic Response and Decision Support”, The International Journal of High Performance Computing Applications (IJHPCA), 2022 Gordon Bell finalist
• A. Adiga, G. Kaur, L. Wang, B. Hurt, P. Porebski, SV, B. Lewis, and M. Marathe, “Enhancing COVID-19 Ensemble Forecasting Model Performance Using Auxiliary Data Sources”, IEEE International Conference on Big Data (IEEE BigData), 2022 Best Paper Award
• SV, A. Sadilek, A. Fadikar, ..., L. Wang, and M. Marathe, “Forecasting influenza activity using machine-learned mobility map”, Nature Communications 12.1, 1–12, 2021
• SV, J. Chen, A. Fadikar, S. Gupta, D. Higdon, B. Lewis, M. Marathe, H. Mortveit, and A. Vullikanti, “Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints”, PLOS Computational Biology 15.9, e1007111, 2019

Open Data & Software

License, language and last activity are stated for each, so you can tell what you are free to reuse before opening the repository. Dates are the last commit, not a maintenance promise.