Anil Kumar S. Vullikanti is a professor in the Department of Computer Science and at the Biocomplexity Institute. Vullikanti received his undergraduate degree from the Indian Institute of Technology, Kanpur, and his Ph.D. from the Indian Institute of Science, Bangalore. He was a post-doctoral researcher at the Max-Planck Institute for Informatics, and a technical staff member at the Los Alamos National Laboratory.
Vullikanti‘s research interests are in the broad areas of approximation and randomized algorithms, dynamical systems, wireless networks, social networks, computation epidemiology and the modeling, simulation and analysis of socio-technical systems. His work has been published in journals and conferences in different areas, such as Nature, Journal of the ACM, IEEE/ACM Transactions on Networking, and the SIAM Journal on Computing and Transactions on Parallel and Distributed Computing. Vullikanti’s name appears as V.S. Anil Kumar on most publications. He is the recipient of the NSF CAREER award and the DOE Early Career award.
Modeling and simulation of social and infrastructure systems, epidemiology, distributed and mobile computing, combinatorial optimization, and combinatorial algorithms
Indian Institute of Science, Computer Science, Ph.D., 1999
Indian Institute of Technology Kanpur, India, Computer Science and Engineering, Bachelor, 1993
Postdoctoral Associate, Max-Planck Inst for Computer Science, Saarbrucken, Germany, 2001
Postdoctoral Associate, Los Alamos National Laboratory, 2003
Contagions, severe weather, natural disasters, civil unrest – whatever data scientists are forecasting using network models, simulation-based methods are often the most effective, according to researchers at the University of Virginia Biocomplexity Institute and Princeton University, whose findings were published in the paper, “Fundamental Limitations on Efficiently Forecasting Certain Epidemic Measures in Network Models,” by PNAS (Proceedings of the National Academy of Sciences).
Researchers from UVA’s Biocomplexity Institute and School of Engineering and Applied Science, working with a team of multi-disciplinary scientists from around the world, have spent the last two years developing highly advanced computational models designed to inform policy makers, save lives and prepare for future global epidemics.
Researchers at the University of Virginia Biocomplexity Institute are founding partners of a national research institute that will develop artificial intelligence-driven solutions for some of agriculture’s biggest problems: labor, water, weather, and climate change.
In this paper, researchers focus on a machine-learned anonymized mobility map aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics, specifically the flu.
Longstanding members like professor Anil Vullikanti are cited for significant achievements across the computing field.
Google.org announced that Madhav Marathe and Anil Vullikanti, researchers at the University of Virginia’s Biocomplexity Institute, were selected to receive funding as part of the company’s efforts to support projects using innovative Al and data analytics to help understand COVID-19 and address its impacts.
The UVA Biocomplexity Institute has received a $1.44M award from the National Science Foundation for a Virtual Organization (VO) that will facilitate communication and collaboration among CISE scientists currently involved in pandemic research through the NSF RAPID program.
As the COVID-19 pandemic continues to escalate in the United States and in many locations around the world, countless questions remain about next steps for mitigation and response. How will various mitigation methods affect the spread? How will those change as the pandemic progresses or regresses?
As the COVID-19 global health crisis continues to unfurl worldwide, the questions around global pandemics are no longer “if” they will occur, but how frequent, widespread, and severe those that come next will become.
According to the Centers for Disease Control and Prevention (CDC), more than 63,600 people died from opioid overdoses in the United States in 2016, and more than 70,000 people died from the same cause in 2017. The opioid epidemic in the United States is showing no signs of abating, but researchers from the Biocomplexity Institute have identified potential geographic opioid misuse and abuse hotspots in Virginia, West Virginia, and North Carolina using network scan statistics – a methodology that may ultimately be used to save lives through targeted interventions in high-risk areas.