Parantapa Bhattacharya

Parantapa Bhattacharya is a Research Scientist at the Network Systems Science and Advanced Computing Division of the Biocomplexity Institute and Initiative at the University of Virginia. His work focuses on developing computational tools and systems for extracting meaningful insights from complex datasets, with applications spanning public health, social networks, and artificial intelligence.

Dr. Bhattacharya's research interests center around High Performance Computing, Agent Based Modeling, Analysis of Large Datasets, and Explainable AI with special emphasis on Natural Language Processing models. His work bridges the gap between theoretical computer science and practical applications in epidemiology and social systems, creating scalable frameworks that can handle real-world complexity and data volume.

His publication record shows a clear trajectory from social media analysis (2013-2016) toward pandemic modeling and response (2019-2022), reflecting the growing importance of computational epidemiology. His research spans multiple disciplines including computer science, epidemiology, social network analysis, and public health, with a consistent focus on developing scalable computational frameworks. The most recent publications demonstrate significant contributions to pandemic response infrastructure, with several papers related to the PanSim framework for agent-based pandemic modeling.

Notable recognitions include being a Finalist for the 2021 ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research and having work published in Nature Computational Science, highlighting the impact and relevance of his research during the global pandemic.

Dr. Bhattacharya has developed significant computational infrastructure, most notably PanSim - a distributed pandemic simulator that enables large-scale agent-based modeling of disease transmission. His technical expertise spans distributed systems, high-performance computing, and machine learning, allowing him to tackle complex problems at the intersection of computer science and public health.