Highly accomplished Research Assistant Professor and computational epidemiologist with over 15 years of expertise in developing advanced modeling and simulation frameworks for infectious disease forecasting and public health interventions. Proven leader in securing over $12 million in grant funding as PI/Co-PI, driving impactful research in areas like COVID-19 and influenza, and translating complex data into actionable insights for national health agencies.
Research Assistant Professor
Charlottesville, VA, US
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Summary
Leads advanced computational modeling and data analytics initiatives, driving multi-million dollar research projects in infectious disease forecasting and public health informatics for national agencies.
Highlights
Served as PI or Co-PI on 13 major grants, securing over $12 million in funding from ARPA-H, NIH, CSTE, NSF, and CDC for critical research in bioaerosol risk, influenza forecasting, and COVID-19 response.
Developed and applied novel influenza forecasting frameworks, including NIF-FLERS and PHOENIX, leveraging learned ensembles and network approaches to enhance predictive accuracy for public health stakeholders.
Led the strengthening of public health informatics using next-generation tools (SPHINX) and championed hybrid approaches for forecasting flu and projecting under feasible futures (HAFFLPUFF) with $375,000 in CSTE funding.
Directed multi-scale multi-model approaches for scalable scenario-based projections for the CDC, contributing to effective communication of localized influenza predictions (ECLIPSE) with $300,000 in funding.
Provided critical public health support for CDC FluSight, CDC Scenario Modeling Hub (COVID-19, Influenza, RSV), and the Tridemic modeling and forecasting initiative, informing national pandemic preparedness and response.
Co-authored numerous high-impact journal articles and conference papers, including contributions to PNAS Nexus and Nature Communications, advancing the understanding of epidemic dynamics and forecasting methodologies.
Research Scientist
Charlottesville, VA, US
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Summary
Conducted advanced research in computational epidemiology and data analytics, contributing to key projects in infectious disease modeling and public health interventions.
Highlights
Co-PI on RAPID: COVID-19 Response Support grants, securing $173,640 from NSF for building synthetic multi-scale networks and $25,000 for transfer learning techniques to improve COVID-19 response.
Co-PI for Smart Targeting and Optimization for Influenza Mitigation (STOMP-flu), securing $454,427 from CDC, and Network-based Mobility Modeling for Complex Humanitarian Emergencies ($98,750) from the Global Infectious Diseases Institute.
Contributed to the MIDAS Coordination Center, a large-scale NIH subaward ($1.6M), focusing on national epidemic preparedness and response coordination.
Supported COVID-19 response efforts for Virginia Departments of Health, Education, and Emergency Management, as well as the Defense Threat Reduction Agency, providing critical modeling and forecasting insights.
Co-PI on AccuWeather license ($55,000) for 4-week influenza forecast, and USAID grant ($135,458) for invasive alien species distribution assessment in Nepal, demonstrating diverse application of modeling skills.
Computational Health Data Scientist
Blacksburg, VA, US
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Summary
Applied computational health data science methodologies to analyze and model complex health datasets, contributing to epidemic forecasting and public health research.
Highlights
Awarded Best Poster Award in Biomedical category at UNC Going Viral Symposium (2018), highlighting innovative research in computational health.
Contributed to influenza forecasting challenges for Armed Forces Health Services Branch (2018-19) and Ebola outbreak response in Central Africa (2018-19), leveraging data science expertise.
Conducted pest risk assessments for Fall Army Worm in Egypt (2017-18) and Tomato leafminer in Nepal (2016-17), applying data-driven models to ecological challenges.
Developed and deployed seasonal influenza vaccine allocation planning strategies for DTRA US NORTHCOM (2016-17), optimizing public health interventions.
Postdoctoral Associate
Blacksburg, VA, US
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Summary
Engaged in postdoctoral research focusing on advanced computational methods for complex systems, contributing to early-stage epidemic modeling and data analysis.
Highlights
Recognized among top three teams in NIH/NSF RAPIDD Ebola Forecasting Challenge (2015), demonstrating early expertise in rapid response modeling.
Co-authored publications on spatio-temporal optimization of seasonal influenza vaccine using metapopulation models and frameworks for evaluating epidemic forecasts.
Contributed to the development of data-driven agent-based models for forecasting emerging infectious diseases, enhancing predictive capabilities.
Participated in research on network reliability to understand international food trade dynamics and its role in invasive species spread.
Research Assistant
Hong Kong, New Territories, Hong Kong
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Summary
Conducted research in information engineering, focusing on data analysis and modeling techniques relevant to complex systems and network science.
Highlights
Awarded Best Presentation Award at SCINSE'14 workshop co-held with COMSNETS (2014), recognizing effective communication of research findings.
Contributed to studies on co-evolution of content spread and popularity in mobile opportunistic networks, advancing understanding of information dissemination.
Assisted in developing models for user modeling and usage profiling based on temporal posting behavior in online social networks.
Student Intern
Bangalore, Karnataka, India
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Summary
Gained foundational experience in research and development within a leading technology lab, supporting projects in communication and network systems.
Highlights
Supported research initiatives related to mobile opportunistic networks, contributing to early-stage project development.
Assisted senior researchers in data collection and preliminary analysis for network performance studies.
Developed foundational skills in research methodologies and technical problem-solving in a corporate R&D environment.
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Ph.D.
Electrical and Computer Engineering
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B.E.
Electrical and Computer Engineering
Awarded By
University of Virginia
Recognized for outstanding collaborative contributions to public service initiatives.
Awarded By
IEEE International Conference on Big Data
Awarded for significant contributions to the field of big data research.
Awarded By
Flu Lab and Center for Open Science
Fellowship recognizing promising research in influenza modeling and forecasting.
Awarded By
CDC
Fellowship recognizing expertise in scenario modeling for public health.
Awarded By
Intelligent Health Summit
Acknowledged for innovative contributions to health technology and research.
Awarded By
UNC Going Viral Symposium
Awarded for exceptional poster presentation in biomedical research.
Awarded By
BDMM2017 Hackathon / IEEE BigData
Achieved runner-up status in a competitive hackathon focused on big data and machine learning.
Awarded By
NIH/NSF RAPIDD
Recognized for outstanding performance in a national challenge for Ebola forecasting.
Awarded By
SCINSE'14 workshop / COMSNETS
Awarded for excellence in presenting research findings at a scientific workshop.
Published by
Winter Simulation Conference (WSC)
Summary
Z. Mehrab, S. S. Ravi, H. Mortveit, SV, S. Swarup, B. Lewis, D. Leblang, and M. Marathe, "Agent-based social simulation of spatiotemporal process-triggered graph dynamical systems”, Winter Simulation Conference (WSC 2025)
Published by
JAMA Network Open
Summary
S. L. Loo, S. Jung, L. Contamin ..., SV, ..., C. Viboud, J. Lessler, S. Truelove, “Scenario Projections of COVID-19 Burden in the US, 2024-2025", JAMA Network Open 2025;8;(9):e2532469.
Published by
PNAS Nexus
Summary
J. Chen, S. Hoops, H. Mortveit, B. Lewis, D. Machi, P. Bhattacharya, SV, M. Wilson, C. Barrett, M. Marathe, "EPIHIPER—A high performance computational modeling framework to support epidemic science", PNAS Nexus, Volume 4, Issue 1, January 2025
Published by
Asilomar Conference on Signals, Systems, and Computers (ASILOMAR)
Summary
S. Mutnuri, A. Adiga, SV, M. Marathe, “Causal Analysis of Graph Signals for Brain Effectome Inference", Asilomar Conference on Signals, Systems, and Computers (ASILOMAR 2025)
Published by
Epidemics
Summary
S. Loo, M. Chinazzi, A. Srivastava, SV, S. Truelove, C. Viboud, "Preface: COVID-19 Scenario Modeling Hubs", Volume 48, September 2024, 100788
Published by
Epidemics
Summary
J. Chen, P. Bhattacharya, S. Hoops, D. Machi, A. Adiga, H. Mortveit, SV, B. Lewis, and M. Marathe, "Role of heterogeneity: National scale data-driven agent-based modeling for the US COVID-19 Scenario Modeling Hub", Epidemics 48 (2024): 100779
Published by
arXiv
Summary
T. McAndrew, M. S. Majumder, A. A. Lover, SV, ..., N. I. Bosse, J. Cambeiro, and D. Braun, "Assessing Human Judgment Forecasts in the Rapid Spread of the Mpox Outbreak: Insights and Challenges for Pandemic Preparedness", arXiv:2404.14686, 2024
Published by
Epidemics
Summary
C. Chen, G. Kaur, A. Adiga, B. Espinoza, SV, ..., M. Marathe, “Wastewater-based Epidemiology for COVID-19 Surveillance: A Survey", Epidemics, Volume 49, 2024, 100793
Published by
Nature Communications
Summary
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 (2024): 6289
Published by
Epidemics
Summary
M. C. Runge, K. Shea, E. Howerton, ..., SV, S. Truelove, J. Lessler, C. Viboud, "Scenario Design for Infectious Disease Projections: Integrating Concepts from Decision Analysis and Experimental Design.” Epidemics (2024): 100775
Published by
medrXiv
Summary
SV, J. Cambeiro, T. Liptay, B. Lewis, M. Orr, G. Dempsey, A. Telionis, J. Crow, C. Barrett, and M. Marathe, “Utility of human judgment ensembles during times of pandemic uncertainty: A case study during the COVID-19 Omicron BA.1 wave in the USA", medrXiv, 2022
Published by
Artificial Intelligence in COVID-19, Springer
Summary
A. Adiga, B. Lewis, S. Levin, M. Marathe, H.V. Poor, S.S. Ravi, D.J. Rosenkrantz, R.E. Stearns, SV, A. Vullikanti, L. Wang, "AI Techniques for Forecasting Epidemic Dynamics: Theory and Practice", in Artificial Intelligence in COVID-19, Springer, 2022
Published by
AI for Science, World Scientific Press
Summary
A. Adiga, A. Wilson, SV, ..., M. Marathe, and NSSAC-BII team, "The role of Artificial Intelligence in Epidemiological Modeling", to be published in AI for Science, World Scientific Press, 2022
Published by
Knowledge-Guided Machine Learning, Chapman and Hall/CRC
Summary
L. Wang, A. Adiga, J. Chen, B. Lewis, A. Sadilek, SV, and M. Marathe, Title: "Combining theory and data driven approaches for epidemic forecasts", in Knowledge-Guided Machine Learning, Chapman and Hall/CRC 55-82, 2022
Agent-Based Modeling, Network Science, Stochastic Processes, Epidemic Forecasting, Human Mobility Modeling, Invasive Species Modeling, Multi-scale Modeling, Scenario-Based Projections, High-Performance Computing (HPC).
Data Analytics, Deep Learning, Predictive Analytics, Time Series Analysis, Geospatial Analysis, Statistical Ensemble Modeling, Machine Learning, Data Management, Pattern Recognition.
Infectious Disease Forecasting, Epidemiology, Pandemic Preparedness, Public Health Interventions, Risk Assessment, Vaccine Allocation Optimization, Disease Surveillance, Public Health Informatics, COVID-19 Response, Influenza Forecasting.
Grant Writing, Research Leadership, Project Management, Scientific Communication, Collaborative Research, Peer Review, Mentorship, Technical Writing, Academic Publishing.
Python, R, Simulation Software (e.g., PatchSim), PEpiTA, PatchFlow, GitHub, Data Visualization.
Computational modeling & simulation, Network science, Data analytics, Stochastic processes, Optimization, Epidemiology, Infectious disease forecasting, Human mobility modeling, Migration, Invasive species.