Alison Lynn Hill, Ph.D., M.P.H.

Headshot of Alison Lynn Hill
  • Assistant Professor of Biomedical Engineering

Research Interests

Infectious Disease Modeling; Evolutionary Dynamics; Viral Dynamics; HIV/AIDS; Drug Resistance more


Dr. Alison Hill is an Assistant Professor in the Department of Biomedical Engineering, and a core faculty member at the Institute for Computational Medicine and Infectious Disease Dynamics Group.

She received her undergraduate degree in physics from Queen’s University, Canada and a PhD through Harvard’s Biophysics Program where she was a joint graduate student in the Harvard-MIT Division of Health-Sciences and Technology (HST)’s Medical Engineering and Medical Physics program.

After graduating, Dr. Hill won an NIH Director’s Early Independence Award, which allowed her to run her own research group for six years and become a member of the John Harvard Distinguished Science Fellows program. During this time she also completed an MPH and the Global Infectious Diseases Program at Harvard School of Public Health. 

Dr. Hill was a member of the Emerging Leaders in Biosecurity program run by the Johns Hopkins Center for Health Security. She has written popular science pieces for PBS Nova and Physics Today, and is involved with STEM outreach. more


  • Assistant Professor of Biomedical Engineering

Departments / Divisions

Centers & Institutes



  • M.P.H.; Harvard T.H. Chan School of Public Health (Massachusetts) (2020)
  • Ph.D.; Harvard University (Massachusetts) (2013)
  • Ph.D.; Massachusetts Institute of Technology (Massachusetts) (2013)
  • B.S.; Queen's University (Canada) (2007)

Research & Publications

Research Summary

Dr. Hill and her team develop mathematical models and computational tools to help understand, predict, and treat infectious diseases, with a particular focus on human viral infections including HIV/AIDS. They also work on drug resistant infections, bed bug infestations, anti-viral immune responses, and SARS-CoV-2/COVID-19. Their research spans infection dynamics both within single individual and across populations. The lab also works on general evolutionary theory of infectious diseases. In the past, Dr. Hill has developed mathematical models for a broad range of topics in biology and medicine, such as pattern formation in early multicellular lifeforms, iron regulation in the body, the interpersonal spread of health-related behaviors, and more.

The work of Dr. Hill’s team is done in close collaboration with experimental biologists and clinicians around the world. Their research incorporates a range of data sources, including time-series characterization of infections within infected individuals and across populations, in vitro microbial growth, pathogen genetic data, biomarkers of immunity, in vivo lineage tracing, pharmacokinetics and pharmacodynamics, and human behavior. This work is guided by the belief that quantitative models are a powerful tool to test hypotheses about the biological mechanisms responsible for observed trends in data, and to integrate existing biological data in a formal way to predict the outcomes of experiments that have not yet, or could never, be performed.


Lab Website: Alison Hill Lab

Selected Publications

Rader B*, Scarpino SV*, Nande A, Hill AL, Adlam B, Reiner RC, Pigott DM, Gutierrez B, Zarebski A, Shrestha M, Brownstein JS, Castro MC, Dye C, Tian Y, Pybus OG, Kraemer MUG (2020). “Crowding and the epidemic intensity of COVID-19 transmission”. Nature Medicine

Krieger MS, Denison CE*, Anderson TL*, Nowak MA, Hill AL (2020). “Population structure across scales facilitates coexistence and spatial heterogeneity of antibiotic-resistant infections”. PLOS Computational Biology. 16(7):e1008010. doi:10.1371/journal.pcbi.1008010

Leventhal GE*, Hill AL*, Nowak MA, Bonhoeffer S (2015). “Evolution and emergence of infectious diseases in theoretical and real world networks”.  Nature Communications, 6 (6101); doi:10.1038/ncomms7101

Hill AL*, Rosenbloom DIS*, Fu F, Nowak MA, Siliciano RF (2014). “Predicting the outcomes of treatment to eradicate the latent reservoir for HIV-1”. PNAS, 111 (37), 13475-13480

Rosenbloom DIS*, Hill AL*, Rabi SA*, Siliciano RF, Nowak MA (2012). “Antiretroviral dynamics determines HIV evolution and predicts therapy outcome”. Nature Medicine (cover article). 18(9):1378–1385

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