My research interests include Machine Learning, Modeling, and Graph Algorithms applied to epidemics, social good, and social networks.

His research interests include network science, modeling, and machine learning applied to epidemics, social good, and social networks. He collaborates with teams around the world and the CDC for infectious disease forecasting and scenario projections. He has been a PI/co-PI of many NSF, CDC, and DARPA-funded awards. He is a DARPA Grand Challenge Winner (2014). He is also an Indian National Math Olympiad Awardee (awarded to 30 students in India in 2008).

Dr. Ajitesh Srivastava's research is centered around the application of network science, modeling, and machine learning to real-world applications. Current work spans applications in epidemics and homelessness. In epidemics applications, he uses a combination of time-series, epidemic modeling, and graph neural network methods improve forecasting, scenario modeling, evaluation, and ensemble development. He also works on policy optimization problems over diffusion models that combine epidemics and human behavior. In homelessness, he is works on a combination of graph neural networks, and network diffusion processes to understand the driving factors of behavior and to optimize peer-based interventions. Beyond these applications, he is interested in machine learning theory to improve graph neural networks and knowledge discovery.