Aditya Prakash

Aditya Prakash is a Professor and Associate Chair for Academic Affairs at the School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology. He is also core-faculty at the Center for Machine Learning (ML@GT) and the Institute for Data Engineering and Science (IDEaS) at Georgia Tech. His research has been supported by major organizations including NSF, CDC, DoE, NSA, and NEH, with tools developed by his group being used at ORNL, CDC, Walmart, and Facebook.

Dr. Prakash received his Ph.D. in Computer Science from Carnegie Mellon University in 2012 and his B.Tech in Computer Science from IIT Bombay in 2007. His academic journey includes previous faculty positions at Virginia Tech before joining Georgia Tech.

His research focuses on Data Science, Machine Learning, and AI with emphasis on big-data problems in networks and time-series, with applications spanning epidemiology, health, security, urban computing, and the web. His work combines theoretical analysis, algorithm development, and empirical studies on large-scale data to address challenges in understanding and managing dynamical mechanisms across natural, social, and technological systems.

His recent publications demonstrate a strong trend toward integrating AI and machine learning techniques with epidemiological modeling, time-series forecasting, and network analysis. There's a clear focus on real-world applications, particularly in public health (including pandemic response), healthcare systems, and critical infrastructure. His work increasingly incorporates large language models, graph neural networks, and advanced uncertainty quantification methods.

• Facebook Faculty Award (2015)
• 'AI Ten to Watch' 2017 by IEEE
• NSF CAREER award (2018)
• Best Paper Award at AI4ABM workshop at ICML 2022
• Best Poster Award at SDM 2024
• 1st place in the COVID-19 Symptom Data Challenge
• 2nd place in the C3AI COVID-19 Grand Challenge

Dr. Prakash has advised numerous PhD students who have gone on to faculty positions at institutions like Virginia Tech, University of Michigan, and University of Iowa, as well as positions at leading tech companies including Google, Pinterest, and LinkedIn. His research has been supported by multiple NSF grants including a CAREER award, CDC funding, and industry partnerships. He leads the BEHIVE project, a multi-institution NSF initiative for developing the science of pandemic prevention and prediction.

He is actively involved in the infectious diseases modeling MIDAS network and has developed tools that have been implemented in real-world settings including ORNL, CDC, Walmart, and Facebook. His group is currently working on impactful projects related to ML and data science for networks and time-series, including applications to COVID, hospital infections, campus mobility, and energy grids.