Trang Bui is a recent Ph.D. graduate in Statistics from University of Waterloo. Her doctoral thesis concerns the design and analysis of experiments on networks. During her Ph.D., Trang also worked as a statistical consultant for various research projects from different departments at the university. Her collaborative work involves the topics of meta-analysis, survey sampling and explainable machine learning.
Dr. Garam Byun is a Postdoctoral Associate at the Yale School of the Environment. She investigates the health effects of environmental risk factors, including but not limited to air pollution, temperature, and greenness, with a particular focus on vulnerable individuals and communities. Her research interests also encompass epidemiological methodologies and the evaluation of environmental policies. She has contributed to multiple government-funded research projects, emphasizing the critical role of epidemiological studies…
Dr. Chen Chen is a postdoctoral scholar at Scripps Institution of Oceanography at the University of California San Diego. Her research lies under three main areas: 1) applying causal inference and advanced methods in air pollution epidemiology, 2) incorporating considerations of social determinants of health into studies of environmental exposures, and 3) quantifying the health risks of emerging climate-related hazards and informing effective adaptation strategies. She is also interested in…
Yongsoo Choi is a Postdoctoral Associate at Yale University, working under the mentorship of Dr. Michelle Bell. His primary research interest is in exploring the varying patterns of health effects caused by air pollution across different times, regions, and populations. Furthermore, he is focused on identifying the causes behind these variations. His goal is to generate valid evidence that can help reduce the overall disease burden attributable to air pollution…
Dr. Choma is a Research Fellow in the Department of Environmental Health at the Harvard T.H. Chan School of Public Health. His research focuses on health risk assessment, with a primary interest in the use of risk assessment to inform policy decisions. Dr. Choma’s research has focused on fine particulate matter air pollution and on the health co-benefits of climate change mitigation, especially on the transportation sector, where he has…
George is the 2022 Wojcicki and Troper Postdoctoral Research Fellow of Harvard Data Science Initiative. He received a Ph.D. in Computer Science from the DaSciM group of LIX, École Polytechnique in Paris, France. He is working on machine learning for structured data with a special focus on graphs and generally non-Euclidean structures. His research is concentrated mainly on building powerful graph learning models, that are able to extract knowledge in…
Pedram Fard, PhD, is a Research Associate at Department of Biomedical Informatics (DBMI) at Harvard Medical School (HMS). With a background in geoinformatics and planning, he has developed expertise in geospatial modeling and large-scale environmental data analysis. At HMS, Pedram has contributed to multiple environmental exposure assessment studies, notably an NIH funded project to investigate the health impacts of extreme heat and cold events (EHE/ECE) exposure on elderly populations with…
Younsoo Jung is a research associate in the Department of Environmental Health at Harvard T.H Chan School of Public Health, mentored by Kari Neadu. Her research interests are quantifying the impact of climate change and understanding the vulnerable groups to improve health disparity and help better shape health policy. She completed her Ph.D. in Economics at the University of Iowa. Her dissertation focused on the impact of Medicaid expansion on…
Heejun Shin is a postdoctoral research fellow at Harvard University, working under the mentorship of Dr. Francesca Dominici. Before joining Harvard, he completed his Ph.D. in Statistics at the University of Florida, where he was mentored by Dr. Joseph Antonelli. His research interests lie in causal inference, flexible Bayesian models, and environmental statistics.
Mauricio Tec is a Ph.D. student in Statistics at the University of Texas at Austin. His main research interest is developing new AI and statistical methods for public policy, healthcare, and scientific applications. He considers major challenges like causality, sequential decisions, uncertainty quantification, and modeling complex structured data. Thus, he focuses mainly on deep and reinforcement learning, Bayesian statistics, and causal inference techniques. He believes that AI can revolutionize scientific…