Postdocs and Researchers

Aditya began his journey in academic science studying agricultural microbiology, receiving his BSc at the University of Mumbai, India followed by his MSc at the MS University of Baroda, India. Transitioning to studying infectious disease, he then received his PhD in microbial physiology at the University of Massachusetts Medical School studying cell cycle progression in Mycobacterium tuberculosis with Christopher Sassetti. Currently, as a postdoctoral fellow in Yonatan Grad’s lab, where he is co-advised by Ethan Garner, Aditya studies fundamental aspects of Neisseria gonorrhoeae cell biology and mechanisms of antimicrobial resistance. 

Sofia Blomqvist joined the Grad Lab in June 2024. She graduated from UMass Amherst, where she majored in Biology and conducted research on the evolution of antimicrobial proteins. She’s very excited to join the Grad Lab and pursue her interests in infectious disease and antimicrobial resistance. In her free time, Sofia enjoys crosswords, reading, going to the beach, playing the trombone, and curling up on the couch with her dog.

Melike H. Can is a postdoctoral research fellow in the Department of Global Health and Population at Harvard T.H. Chan School of Public Health. Her current work focuses on modeling of tuberculosis (TB) care cascade and diagnostic algorithms.

She holds a BA in Economics, as well as a BS and an MS in Industrial Engineering. Prior to joining Menzies Lab, she completed her PhD in Industrial Engineering at Northeastern University, where she received another MS in Operations Research. During her graduate studies, she has utilized mathematical modeling, statistics, and simulation analysis to optimize health care operations in collaboration with health facilities and universities. Her dissertation focused on the applications of machine learning methods in breast cancer treatment. Her research interests include machine learning, mathematical modeling and optimization, applied statistics, and simulation modeling.

Hening Cui is a research data analyst at Harvard T.H. Chan School of Public Health in the Department of Global Health and Population. Her work focuses on enhancing individual-level decision-making regarding Latent Tuberculosis Infection (LTBI) screening and subsequent treatment in the United States. Hening holds a Master of Science degree in Biostatistics from Columbia University. Her research at Columbia focused on investigating gene expression patterns during cardiogenesis to facilitate treatment for congenital heart defects, employing innovative approaches such as the use of single RNA sequencing data in the Linear Mixed Model (LMM) and Cox Joint Model. Her academic interests lie in cost effective analysis and disease modeling. Beyond her academic pursuits, Hening enjoys cooking, reading mystery novels, and script-killing.

Julia is a postdoctoral research fellow in the lab of Marc Lipsitch. Her research focuses on modeling antibody kinetics following SARS-CoV-2 vaccination and infection, and estimating the associated level of protection against further infection. She recently completed her PhD in Computational Biology at ETH Zurich in Switzerland, where she established models for blood glucose prediction in type 1 diabetes. In addition, Julia developed insulin treatment strategies to improve blood glucose control in the context of exercise. Prior to her PhD studies, she received a BSc and MSc in Physics from RWTH Aachen University and Lund University.

Dr. Dewey is an incoming postdoc with the MIGHTE group. He completed his PhD in Epidemiology at UCLA, using network methods to assess questions from behavioral science, public health, and scientometrics. He hopes to use his background in network science and epidemiology to complement the ongoing outbreak forecasting efforts using non-traditional data. He’s also interested in the collection of high-quality data that could be used as a platform for future network research. 

Juan Gago joined Marc Lipsitch’s Lab in June 2024. Juan, originally from Buenos Aires, Argentina, completed his MD at the University of Buenos Aires. After graduating, he decided to focus his career on population health and epidemiology. He earned his MPH from the CUNY School of Public Health and his PhD from the NYU School of Medicine. He is particularly interested in employing causal inference methods to improve antibiotic use and prevent the surge of antimicrobial resistance. Outside of work, he enjoys swimming, traveling to new cities, and discovering where to find the best cup of coffee.

At Harvard Chan School, Masa conducts research on wastewater-based surveillance for COVID-19 and to measure the social value of vaccination.

I specialize in genomic epidemiology, and my research revolves around the examination of genomic data from both human and bacterial sources in tuberculosis patients. My research focuses on investigating the association between genotype and phenotype in tuberculosis. Furthermore, I have a keen interest in understanding how immunity changes and in unraveling the transmission patterns of tuberculosis.

Salome is a new postdoctoral fellow who joined Jeff Imai-Eaton’s lab in September 2024. She is interested in using modern methods in causal inference to understand gender-based drivers of HIV acquisition and engagement in HIV treatment. Salome completed her PhD in epidemiology from McGill University where her research focused on the implications of intimate partner violence for HIV control in women, girls, and infants. Prior to her PhD, Salome worked on implementation research studies in India, Tanzania, South Africa and Malawi. Outside of work she enjoys reading, hiking, and attending live music shows.

Yunfei Li is a postdoctoral research fellow in the Department of Global Health and Population at Harvard Chan School. Her research focuses on the development and application of advanced quantitative methods to inform decision-making and resource prioritization in public health, within three substantive areas: (I) Modeling patterns and trends in disease burden and disparities; (II) assessing the health effects, economic impacts, and cost-effectiveness of current and future preventive strategies; (III) epidemiology of diabetes, diagnosis, and risk prediction. Yunfei received a Doctor of Science from Harvard University and a Bachelor of Medicine, Bachelor of Surgery from Peking University in China.

Tse Yang (T.Y.) Lim is a complex systems modeler who develops simulation models of public health problems to inform policy decision processes. His recent work has focused on substance use and the opioid crisis, as well as the COVID-19 pandemic. He received his PhD from the system dynamics group at the MIT Sloan School of Management, and was previously an ORISE Fellow at the U.S. Food and Drug Administration. He also holds a BS and Master’s in Environmental Management from Yale.

Jason completed a PhD in physics at Boston University. He then worked as a postdoc in mathematical biology at Harvard. Jason is developing models for understanding how to optimize surveillance for pathogens. He is also exploring digitally connected medical diagnostics data as a potential tool for informing public health strategies. 

In the pursuit of innovative solutions to some of the world’s most pressing health challenges, my research revolves around constructing advanced statistical models for predicting infection outbreaks. My PhD and post-doctoral research focused primarily on understanding the evolution of epidemic and pandemic viruses. Currently, these models are tailored to forecast Dengue outbreaks in tropical countries and Influenza outbreaks in the U.S. My work is not just theoretical; the Dengue forecasts I produce along with the team in the Santillana Lab play a crucial role in supporting clinical trial programs conducted by Johnson and Johnson. Similarly, the Influenza models I’ve helped to develop are instrumental in contributing to the CDC’s Flusight project. The complexities of this research demand a multifaceted approach. I employ a diverse range of methods, bridging the domains of machine learning, infectious disease epidemiology, and time series forecasting. I use classic time series and multivariate methods as well as both custom-produced supervised and unsupervised learning techniques. Subsequent stages involve error analysis and the visualization of our findings. Collaboration is central to the success of these projects. I routinely interact with both governmental and non-governmental stakeholders. In addition, I am a physician and routinely communicate with clinical teams regarding the application of public health data to clinical problems.

Dr. Binod Pant completed his PhD in Applied Mathematics at the University of Maryland, College Park (UMD), where his research focused on the intersection of mathematics and biology. His work involved utilizing mathematical theory, data analytics, and computational methods to gain insight into the transmission dynamics and control of emerging and re-emerging infectious diseases of public health importance. During the COVID-19 pandemic, Dr. Pant’s research addressed several critical areas, including the impact of heterogeneity on herd immunity threshold, the impact of human behavior on disease transmission, and predicting hospitalization using wastewater surveillance data.

Mui is a postdoctoral research fellow in the Lipsitch Lab and Grad Lab, interested in understanding driving factors in explaining infection and antimicrobial resistance trends. Her research focuses on defining these trends for health care-associated pathogens using data from the U.S. Veterans Health Administration and the Alberta Health Services in Canada, and developing mathematical models to study the relationship between antibiotic use and resistance. Mui studied mathematics at RWTH Aachen in Germany. She completed her PhD at the University Medical Center Utrecht in the Netherlands where she developed mathematical models to study the transmission dynamics of P. aeruginosa in intensive-care units. In addition, she evaluated the impact of various interventions on the transmission of SARS-CoV-2 in the community and in hospitals. Besides work, she enjoys rock climbing, hiking, and Lindy Hop dancing.

Domonique Reed received a PhD in Epidemiology from Columbia University. Domonique’s research primarily focuses on understanding the impact of interpersonal relationships on risk behaviors and engagement in HIV prevention services for adolescent girls and young women. Her dissertation research, titled “Moving Beyond the Individual: A Data-driven Approach to Assessing the Multi-level Determinants of HIV among Adolescent Girls and Young Women in Sub-Saharan Africa” was a multi-pronged study that applied novel data science methods to better understand the multi-level drivers of HIV risk in this vulnerable population.

As a Yerby Fellow, she will be mentored by Dr. Jeffrey Imai-Eaton and will expand on her work in data integration to characterize gaps in sub-Saharan African populations that are missing from HIV programming that go beyond standard demographic stratification, such as age and sex.

In her free time, she enjoys long distance running, traveling, and spending time with family.

Minttu Rönn is an infectious disease epidemiologist, and in her research she employs mathematical modeling to inform decision-making in the fields of reproductive health, sexually transmitted infections (STIs), and HIV. Minttu’s interests include transmission dynamics of infectious diseases, HIV-STI co-infections, and social determinants of health. Originally from Finland, Minttu earned her BSc in General Microbiology from the University of Helsinki. She then relocated to London, UK, to pursue studies in public health, obtaining MPH and PhD degrees from Imperial College London.

Kirstin is a postdoctoral fellow in the lab of Yonatan Grad, where she develops mathematical models to compare genomic surveillance strategies. She recently completed her PhD in Computational Mathematics at the University of São Paulo, where she applied machine learning and causal inference to study the spread of vector-borne diseases in Brazil. Prior to her PhD studies, Kirstin was a data scientist at the World Bank Group and has worked on international development projects in Latin America, Sub-Saharan Africa, and Asia.

Ilan Rubin is a postdoctoral research fellow in the Center for Communicable Disease Dynamics at Harvard Chan School, interested in how evolution affects epidemiological dynamics. After graduating with a BA in Computational Biology from Cornell University, he worked in epidemiological and disaster response modeling at Gryphon Scientific. He then worked at Georgia Tech modeling Ebola transmission dynamics and mitigation efforts. He received his PhD in Zoology from the University of British Columbia, using mathematical models to study how diversity evolves and is maintained in ecological communities. At the CCDD, he focuses on modeling the evolution of infectious diseases, including the epidemiology of successive disease variants and the evolution of viruses in response to vaccination efforts.

Nicole Anne Swartwood is a senior research analyst in the lab of Nicolas Menzies.

At Harvard Chan School, Nicole’s work focuses on mathematical modeling of tuberculosis and COVID-19 in the United States. Her academic interests include disease modeling, respiratory health, atmospheric chemistry. She is also passionate about the use of the R programming language in infectious disease epidemiology. She cofounded the R User Group at HDSI and works to improve reproducibility in science through the development of webtools and R packages.

Nicole received several degrees from the University of Tennessee, including a BS in Mathematics and a BS in Microbiology. She also attended Emory University where she received her MSPH in Environmental Health and Epidemiology. While at Emory, Nicole developed quantitative microbial risk assessments of norovirus on produce, air pollution estimates using remote sensing techniques, and survey-based measures of community understanding of tuberculosis and air pollution in Dhaka, Bangladesh. Her thesis estimated the impact of brick kiln emissions on tuberculosis incidence in Bangladesh.

Dr. Qi Tan is a research fellow working on TB study with Prof. Megan Murray at Harvard Medical School since 2019. She was previously a pulmonary physician caring for patients with lung disease and TB in a provincial medical center in China and worked on a long-term clinical cohort study on cytokine therapy for MDR-TB patients between 2008 and 2018. After that she got postdoc training at UMass Medical School studying host immune mechanisms against MTB from 2018 to 2019. Currently, she is focused on developing tools for screening and prevention in TB-exposed people in Peru.

QinQin is a postdoctoral fellow interested in understanding how and why pathogens evolve, what effect this has for disease spread, and what interventions can slow the spread of disease. Her current work is on the evolutionary dynamics of N. gonorrhoeae in response to pressures from the host adaptive immune system and equity in wastewater monitoring. QinQin completed her PhD in biophysics at UC Berkeley where she studied the role of spatial structure and stochasticity in microbial evolution. She completed her undergraduate degree in experimental physics at MIT. Outside of research, QinQin has interests in policy, education, and science communication. Her previous policy work includes assessing the progress of countries’ national action plans on antimicrobial resistance in sub-Saharan Africa with the Center for Disease Dynamics Economics and Policy (now the One Health Trust). Before starting her PhD, she spent one year developing an affordable science teaching lab model in Rwanda with the university program Kepler. Outside of work she enjoys hiking, traveling, food, and music.