© 2026. All rights reserved.
© 2026. All rights reserved.
We develop and deploy data-driven models, digital twins, and large-scale simulations to support decision-making in complex, interconnected systems. This site provides a curated entry point to selected tools, datasets, and research efforts, with links to our broader portfolio and application areas across the Biocomplexity Institute.
Artificial Intelligence AI and machine learning methods integrated with domain knowledge to enable prediction, optimization, and decision support.
Digital Twins Dynamic, data-driven representations of real-world systems used to simulate scenarios and evaluate interventions.
Network Science Modeling relational systems to uncover patterns, dependencies, and cascading effects across complex networks.
High-Performance Computing Advanced computing infrastructure that powers large-scale simulations, integrated data pipelines, and rapid analysis.
Energy Systems Designing and analyzing resilient energy systems, including grid modernization, distributed energy integration, and long-term planning under uncertainty.
Bioinformatics Transforming genomic data into actionable insights for public health, biodefense, and biomedical discovery.
Pandemics and Biothreats Modeling infectious disease spread, evaluating intervention strategies, and supporting real-time public health decision-making.
Supply Chains and Critical Materials Understanding vulnerabilities in global supply chains, modeling disruptions, and informing strategies for resilience and national security.
National Security Supporting decision-makers with tools and simulations that assess risk, anticipate cascading impacts, and guide response strategies.
Agricultural and Food Systems Using data-driven models and simulations to analyze agricultural production, food supply chains, and system vulnerabilities, enabling more resilient and adaptive food systems.
We are proud to support the recently completed U.K.-U.S. prize challenges.
In the challenge, participants were tasked with creating personalized risk forecasts of infection in a privacy-preserving manner. The challenge was put on by the U.K.’s Center for Data Ethics and Innovation (CDEI) and Innovate UK, as well as by the U.S. National Institutes of Standards and Technology (NIST), and the National Science Foundation (NSF) in cooperation with the White House Office of Science and Technology Policy (OSTP).
Our team generated the two synthetic population datasets provided in the pandemic response challenge. The first covers the population of the UK, and the second the population of the state of Virginia, USA. We used an outbreak simulation that created 63 days-worth of data, subsequently split into 56 days of training data and 7 days of test data. You can learn more about our contributions here and access the dataset here.
Presented to Jiangzhuo Chen, Bryan Lewis, and Srini Venkatramanan as representatives of the Biocomplexity Institute’s COVID-19 Response Team for their service to the University, the Commonwealth of Virginia, and federal authorities during the pandemic, which continues today. Link
The team was recognized at SC21 for their paper describing an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; (ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; (iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; (iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences. Link
The team was selected as a finalist in the June 2021 Trinity Challenge for better protecting the world against health emergencies using data-driven research and analytics. 16 finalists were chosen out of 340 entries. Link
Presented to A Bhatele, J Yeom, N Jain, C Kuhlman, Y Livnat, K Bisset, L Kale, M Marathe for innovative use of HPC that led to scalable mapping of epidemic simulations on NERSC machines. Link
Presented to the team in the category of Data to Decisions for work on developing high performance computing solutions to support national disaster management. Link
Presented to the team for our work on simulating the entire US population in seconds versus an hour, helping to contain influenza outbreaks and optimizing placement of treatment centers for the Ebola outbreak. Link