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 infectious disease forecasting and response. My work spans network science, stochastic processes, and — increasingly — agentic AI systems, applied to domains including epidemiology, human mobility, healthy buildings, and neuroscience. I have contributed to national collaborative efforts such as CDC FluSight and the Scenario Modeling Hubs, and have led or co-led several CSTE-, NIH-, and ARPA-H-funded projects on infectious disease epidemiology. I was a recipient of the Collaborative Excellence in Public Service Award in 2022 for my work supporting the Virginia Department of Health during its COVID-19 response. I have managed multiple academia-industry partnerships with Google, Metaculus, Kinsa Health, and Signature Science.

Research Interests

Areas

• Computational modeling & simulation
• Agentic AI systems
• Network science
• Stochastic processes

Domains

• AI for science
• Healthy buildings
• Human mobility
• Epidemiology
• Forecasting
• Neuroscience

A word cloud built from the abstracts of my publications, sized by how distinctive each term is to my work and colored by research theme. Click any word to see the papers behind it.

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
• 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”, accepted at 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

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