Anil Vullikanti
University of Virginia · Computer Science
Active 2008–2026
Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.
About
Anil Vullikanti is a Professor in the Department of Computer Science and the Biocomplexity Institute at the University of Virginia. His research interests broadly encompass randomized algorithms, combinatorial optimization, distributed computing, dynamical systems, network science, machine learning, and artificial intelligence. He applies these areas to fields such as epidemiology, public health, and the modeling, analysis, and protection of critical infrastructures. Vullikanti has a notable academic background, having completed his B. Tech at the Indian Institute of Technology, Kanpur, in 1993, and his PhD at the Indian Institute of Science, Bangalore, in 1999. His professional experience includes postdoctoral work at the Max Planck Institute for Computer Science and Los Alamos National Laboratory, where he was also a technical staff member from 2003 to 2005. Prior to his current position, he was at Virginia Tech from 2005 to 2018. His contributions to the field have been recognized through nominations for best paper awards at prominent conferences such as Supercomputing 2016 and AAAI 2013. Vullikanti has received several awards, including the College of Engineering Faculty Fellow Award at Virginia Tech, the Excellence in Research Award from the Biocomplexity Institute of Virginia Tech, the DOE Early Career Award, and the NSF CAREER Award.
Research topics
- Computer Science
- Medicine
- Virology
- Political Science
- Machine Learning
- Computer Security
- Data Mining
- Artificial Intelligence
- Economics
- Development economics
Selected publications
Mathematical Models for COVID-19 Pandemic: A Comparative Analysis
Journal of the Indian Institute of Science · 2020 · 211 citations
Senior authorCorrespondingPrivacy-first health research with federated learning
npj Digital Medicine · 2021 · 177 citations
Privacy protection is paramount in conducting health research. However, studies often rely on data stored in a centralized repository, where analysis is done with full access to the sensitive underlying content. Recent advances in federated learning enable building complex machine-learned models that are trained in a distributed fashion. These techniques facilitate the calculation of research study endpoints such that private data never leaves a given device or healthcare system. We show-on a di…
MMWR Morbidity and Mortality Weekly Report · 2021 · 163 citations
After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term proje…
medRxiv (Cold Spring Harbor Laboratory) · 2021 · 70 citations
We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 to 24 months to vaccinate their citizens. These time estimates and the emergence of new viral strains urge us to find quick and effective ways to allocate the vaccines and contain the pandemic. While current approaches use combinations of age-based and occupation-based prioritizations, our s…
Fundamental limitations on efficiently forecasting certain epidemic measures in network models
Proceedings of the National Academy of Sciences · 2022 · 20 citations
The ongoing COVID-19 pandemic underscores the importance of developing reliable forecasts that would allow decision makers to devise appropriate response strategies. Despite much recent research on the topic, epidemic forecasting remains poorly understood. Researchers have attributed the difficulty of forecasting contagion dynamics to a multitude of factors, including complex behavioral responses, uncertainty in data, the stochastic nature of the underlying process, and the high sensitivity of t…
Recent grants
NSF · $295k · 2012–2016
NSF · $736k · 2016–2019
Detection and characterization of critical under-immunized hotspots
NIH · $3.3M · 2014–2024
Frequent coauthors
- 251 shared
Madhav Marathe
- 133 shared
Achla Marathe
- 121 shared
Henning Mortveit
University of Virginia
- 114 shared
Bryan Lewis
Biocom
- 110 shared
Jiangzhuo Chen
University of Virginia
- 92 shared
Srinivasan Venkatramanan
Biocom
- 80 shared
Samarth Swarup
University of Virginia
- 59 shared
Abhijin Adiga
Warwick Hospital
Awards & honors
- College of Engineering Faculty Fellow Award, Virginia Tech 2…
- Excellence in Research Award, Biocomplexity Institute of Vir…
- DOE Early Career Award 2010
- NSF CAREER Award 2009
Similar researchers at University of Virginia
- Resume-aware match score
- Save to shortlist
- AI-drafted outreach
See your match with Anil Vullikanti
PhdFit ranks faculty by your research interests, methods, and publications — grounded in their actual work, not templates.
- Free to start
- No credit card
- 30-second signup