HDSI Faculty Affiliates

About Faculty Affiliates

The Harvard Data Science Initiative (HDSI) Affiliates Program supports Harvard faculty who are actively engaged in advancing data science methodologies and applications or data science teaching. The Program seeks to foster collaboration between methodologically- and domain-focused researchers, strengthen the Harvard data science community, and amplify the impact of data science beyond the University.

The Program supports Affiliates by convening networking sessions, seminars, workshops, and tutorials; providing opportunities for research funding; facilitating engagement with industry; and promoting data science research. The Program also offers Affiliates an opportunity to shape the future of data science at Harvard through participation in strategic planning activities and service on governance committees. As the Initiative grows, so too will the benefits to Affiliates.

Affiliate Benefits

  • Networking opportunities with other HDSI Affiliates
  • Can state HDSI affiliation in publications (“and Affiliate, Harvard Data Science Initiative”)
  • Inclusion on HDSI website and other communications
  • Inclusion in the HDSI faculty profiles portal (under development)
  • Opportunities for engagement and collaboration with HDSI Corporate Members
  • Complimentary print subscription to the Harvard Data Science Review

Becoming a Faculty Affiliate

Requirements for Membership

All Harvard full-time faculty who hold PI rights, with a demonstrated commitment to data science research – including both methodologically-oriented and applications-oriented research – or teaching are encouraged to apply and can do so by filling out this short application form.

We will also invite any faculty member who hosts an HDSI Postdoctoral Fellow, receives funding from the HDSI, or serves on an HDSI committee to become an HDSI Affiliate.

Affiliates are appointed to a renewable two‐year term. Renewing members are encouraged to demonstrate some engagement with activities of the Initiative in the year prior to renewal. Some examples are:

  • Attended HDSI seminars, workshops, or networking events
  • Hosted an HDSI postdoctoral fellow
  • Received funding from the HDSI
  • Served on an HDSI committee
  • Participated in, or attended, an event convened by the HDSI, for example a panel, a workshop, roundtable discussion, or networking event
  • Provided Service to the Harvard Data Science Review

Eva Ascarza

Jakurski Family Associate Professor of Business Administration
Harvard Business School
Marketing
As a marketing modeler, Professor Ascarza uses tools from statistics, economics, and machine learning to answer relevant marketing questions. Her main research areas are customer analytics and customer management, with special attention to the problem of customer retention. She uses field experimentation (e.g., A/B testing) as well as econometric modeling and machine learning tools not only to understand and predict patterns of behavior, but also to optimize the impact of firms’ interventions. 

Demba Ba

Departments of Electrical Engineering and Bioengineering
Harvard John A. Paulson School of Engineering and Applied Sciences

Demba Ba is currently an Associate Professor of Electrical Engineering and Bioengineering with Harvard University, where he directs the CRISP group. His research interests lie at the intersection of high-dimensional statistics, optimization, and time-series analysis, with applications to neuroscience and multimedia signal processing. Recently, he has taken a keen interest in the connection between artificial neural networks and sparse signal processing, as a means to understand the principles of hierarchical representations of sensory signals in the brain, and to develop explainable AI. The aim of this research is to explain neural networks as inference algorithms in biologically-plausible mechanistic, generative, statistical models.... Read more about Demba Ba

Michael Baym

Assistant Professor of Statistics
Biomedical Informatics
Harvard Medical School
Michael Baym studies microbial evolution and antibiotic resistance. His data science interests primarily revolve around analysis of microbial genomes, both from clinical samples and laboratory evolution.

Edo Berger

Professor of Astronomy
Department of Astronomy
Harvard Faculty of Arts and Sciences
Edo Berger, along with his students and postdoctoral fellows, researches a wide range of explosive and eruptive astrophysical phenomena, including gamma-ray bursts, tidal disruption events, super-luminous supernovae, and other optical transients (from the Pan-STARRS project and elsewhere), as well as magnetic activity in sub-stellar objects.  He uses observations across the electromagnetic spectrum - from radio to γ-rays - utilizing observatories around the world and in space.

Marcia Castro

Andelot Professor of Demography
Global Health and Population
Harvard TH Chan School of Public Health
Marcia Castro is Andelot Professor of Demography and Chair of the Department of Global Health and Population at the Harvard T.H. Chan School of Public Health. Her research focuses on the identification of social, biological, and environmental risks associated with infectious diseases (particularly mosquito-borne), with the ultimate goal of informing the planning, implementation, and evaluation of control interventions. She uses multidisciplinary approaches and applies varied statistical models combining data from different sources: administrative records, population census, household surveys, satellite imagery, and geocoded data.

Elena Glassman

Asst. Professor of Computer Science
Department of Computer Science
Harvard John A. Paulson School of Engineering and Applied Sciences

Elena Glassman is a the Stanley A. Marks and William H. Marks Assistant Professor at the Radcliffe Institute and a professor of computer science at the Harvard Paulson School of Engineering and Applied Sciences specializing in human-computer interaction. She designs, builds and evaluates systems for comprehending and interacting with population-level structure and trends in large code and data corpora.... Read more about Elena Glassman

Mark Glickman

Mark Glickman

Senior Lecturer in Statistics
Department of Statistics
Harvard Faculty of Arts and Sciences
Dr. Mark Glickman, a Fellow of the American Statistical Association, is Senior Lecturer on Statistics at the Harvard University Department of Statistics, and Senior Statistician at the Center for Healthcare Organization and Implementation Research, a Veterans Administration Center of Innovation.  He serves as an elected member of the American Statistical Association's Board of Directors as representative of the Council of Sections Governing Board with a term expiring in 2021. In that role, he co-chairs the Ad Hoc Advisory Committee on Data Science.  Dr. Glickman's long-standing interest in methods for rating competitors in games and sports arose from his involvement in playing tournament chess, where he attained the title of U.S. national master in 1988. Dr. Glickman is known for having invented the Glicko and Glicko-2 rating systems, both of which have been adopted by many gaming organizations internationally, particularly online gaming. These models stemmed from his Harvard docotoral dissertation on foundational probability models for rating competitors with time-varying abilities, a topic on which he has since published a number of scholarly papers, including methods for pairing competitors in tournaments. 

Christopher Golden

Assistant Professor of Nutrition and Planetary Health
Department of Nutrition
Harvard T.H. Chan School of Public Health
Chris Golden is an Assistant Professor of Planetary Health and Nutrition at the Harvard TH Chan School of Public Health. With a background in ecology and epidemiology, he investigates the nexus of trends in global environmental change and human health. He received his BA from Harvard College and two graduate degrees from UC Berkeley: an MPH in Epidemiology, and a PhD in Environmental Science. For the past 20 years, Golden has been conducting environmental and public health research in Madagascar where he created a non-profit organization called Madagascar Health and Environmental Research. He is currently leading various data science efforts: 1) harnessing existing databases to examine the human health impacts of natural disasters in Sierra Leone, Fiji, and other countries; 2) developing a climate-smart health monitoring system to examine links between climate and environmental change and human health; and 3) using data science approaches to model nutrition-sensitive, sustainable seafood systems.

Alyssa Goodman

Robert Wheeler Wilson Professor of Applied Astronomy
Department of Astronomy
Harvard Faculty of Arts and Sciences
Alyssa Goodman is the Robert Wheeler Willson Professor of Applied Astronomy at Harvard University, co-Director for Science at the Radcliffe Institute for Advanced Study, and a Research Associate of the Smithsonian Institution. Goodman's research and teaching interests span astronomy, data visualization, and online systems for research and education. Goodman's personal research presently focuses primarily on new ways to visualize and analyze the tremendous data volumes created by large and/or diverse astronomical surveys, and on improving our understanding of the structure of the Milky Way Galaxy. She is working closely with colleagues at the American Astronomical Society, helping to expand the use of the WorldWide Telescope program, in both research and in education. Goodman also leads the Prediction project at Harvard University, focused on tracing back the roots of modern computer simulation, as prediction, through history, all the way back to the sheep entrail divination practiced in Mesopotamia.... Read more about Alyssa Goodman

Peter Huybers

Professor
Earth and Planetary Sciences
Harvard Faculty of Arts and Sciences
Peter’s research interests lie in developing a better understanding of the climate system. His current research involves evaluating historical climate events; predicting temperature, hydrological, and sea level variations; and assessing the implication of climate change for food production. Peter generally pursues these questions through combining disparate and peculiar data with theory. Peter received his B.S. in physics from the United States Military Academy at West Point. Subsequent to serving in the military, Peter received a Ph.D. in climate physics and chemistry from MIT, worked at Woods Hole Oceanographic Institute, and joined the faculty at Harvard in 2007. In 2012, Peter worked as a senior climate advisor at the Office of Science Technology Policy in the Executive Office of the President. He has been awarded a MacArthur 'genius' grant, a Packard Fellowship, and the American Geophysical Union's Macelwane Medal.

Stratos Idreos

Associate Professor
Department of Computer Science
Harvard John A. Paulson School of Engineering and Applied Sciences
Stratos works on data structure synthesis and self-designing data system architectures. Data science applications include fast statistical computations, fast visualizations and exploratory queries, as well as neural network training, inference, and accuracy optimization through storage/architecture co-design.

Kosuke Imai

Professor of Government and Statistics
Departments of Government and Statistics
Harvard Faculty of Arts and Sciences
Kosuke Imai is Professor in the Department of Government and the Department of Statistics at Harvard University. He specializes in the development and... Read more about Kosuke Imai

Scott Kominers

MBA Class of 1960 Associate Professor
Harvard Business School
Kominers is the MBA Class of 1960 Associate Professor of Business Administration in the Entrepreneurial Management Unit at Harvard Business School, and a Faculty Affiliate of the Harvard Department of Economics and the Harvard Center of Mathematical Sciences and Applications. From 2013-2017, he was a Junior Fellow at the Harvard Society of Fellows. Prior to that, he was the inaugural Saieh Family Fellow in Economics at the Becker Friedman Institute at the University of Chicago. Kominers graduated from Harvard summa cum laude and Phi Beta Kappa in Mathematics (with a minor in Ethnomusicology) in 2009, and completed his PhD in Business Economics at Harvard in 2011.

Luke Miratrix

Assistant Professor
Harvard Graduate School of Education
Affiliate Faculty of the Department of Statistics
Luke Miratrix works in the areas of causal inference and text analysis (both separately and overlapping). For causal inference he has a specific focus on assessing and characterizing treatment variation in randomized experiments. For text he primarily focuses on how to capture human-interpretable quantities using automated methods. He has particularly worked on ways of evaluating the success of machine learning approaches using human validation. He is also working on how data science interacts with causal inference by developing tools built from more classical statistical ideas such as survey sampling that use data science approaches in a manner that protects the validity of subsequent causal estimates.

Rachel Nethery

Assistant Professor of Biostatistics
Department of Biostatistics
Harvard T.H. Chan School of Public Health

The primary aim of my research is to develop statistical methods that enable maximally rigorous and impactful uses of data to answer environmental health questions. In particular, my recent work centers on the following topics:

(1) Methods for estimation of the health impacts of complex, nationwide environmental regulations

(2) Integration of causal inference principles and methods into epidemiological cancer cluster analyses

(3) New causal inference approaches for studying the effects of environmental exposures on childhood cancer

(4) Methods for studying the impacts of climate, heat, and natural disasters on health and predicting the health impacts of future extreme climate events

Beyond causal inference, my methodological research interests include machine learning, Bayesian methods, latent variable models, spatial statistics, and time series analysis. I have applied these methods to investigate scientific questions not only in environmental health contexts but also in reproductive epidemiology, neuroimaging, social science, and cell biology.

Giovanni Parmigiani

Professor
Department of Biostatistics
Harvard T.H. Chan School of Public Health
Giovanni Parmigiani's research investigates statistical principles and tools, often with a focus on understanding cancer data. For example, he is currently interested in addressing the challenges of cross-study replication of predictions, by constructing predictors that learn replicability from being trained on multiple studies at once. He also has a long term interest in helping families who are particularly susceptible to inherited cancer understand their risk and make informed decisions. He uses Bayesian modeling and machine learning concepts to predict who is at risk of carrying genetic variants, and to integrate literature-based and other information about the effects of mutations. Throughout his research activities, his broad goals are to find innovative ways to use data science and data technologies to fuel cancer prevention and early detection and, methodologically, to increase the rigor end efficiency with which we leverage the vast and complex information generated in today’s cancer research. He strives to foster the use of data sciences as a common thread to facilitate interactions between fields and academic cultures, and has a passion for mentoring and training young(er) scientists in interdisciplinary settings.

Mauricio Santillana

Assistant Professor
Harvard Medical School

Mauricio Santillana is an Assistant Professor at Harvard Medical School, a faculty member in the Computational Health Informatics Program at Boston Children’s Hospital, and an associate at the Harvard Institute for Applied and Computational Sciences. Mauricio enjoys working with clinicians in the design of decision-making support tools.... Read more about Mauricio Santillana

Dustin Tingley

Professor of Government
Department of Government
Harvard Faculty of Arts and Sciences
Dustin Tingley is Professor of Government in the Government Department at Harvard University. Dustin is Deputy Vice Provost for Advances in Learning, Faculty director for the Vice Provost for Advances in Learning Research Group (Harvard higher education data science group), and Faculty director for the Harvard Initiative on Learning and Teaching. He received a PhD in Politics from Princeton in 2010 and BA from the University of Rochester in 2001. His research interests include international relations, international political economy, statistical methodology, and experimental approaches to political science. His book on American foreign policy, Sailing the Water's Edge, was published in fall 2015, and was awarded the Gladys M. Kammerer Award for the best book published in the field of U.S. national policy. Recent projects include attitudes towards global climate technologies and policies, and the intersection of causal inference and machine learning methods for the social sciences.

David Yang

Assistant Professor
Department of Economics
Harvard Faculty of Arts and Sciences
David Yang’s research focuses on political economy, behavioral and experimental economics, economic history, and cultural economics. In particular, David studies the forces of stability and forces of changes in authoritarian regimes, drawing lessons from historical and contemporary China. David received a B.A. in Statistics and B.S. in Business Administration from University of California at Berkeley, and PhD in Economics from Stanford.

Xiang Zhou

Assistant Professor
Department of Sociology
Harvard Faculty of Arts and Sciences
Xiang Zhou is an assistant professor in the Department of Sociology at Harvard University. His research broadly concerns social and economic inequality, causal inference, and statistical and computational methods. His work in quantitative methodology has appeared (or will soon appear) in Sociological Methodology, Sociological Methods & Research, Political Analysis, Journal of the Royal Statistical Society (Series A), Epidemiology, and Journal of Political Economy. He has authored five software packages in R. His most recent work focuses on the use of machine learning methods to analyze causal mediation and causal effect heterogeneity in observational studies.

Marinka Zitnik

Assistant Professor
Department of Biomedical Informatics
Harvard Medical School

Marinka Zitnik is a computer scientist who studies applied machine learning with a focus on challenges brought forward by data in science, medicine, and health. Before joining Harvard, Dr. Zitnik was a postdoctoral scholar in Computer Science at Stanford and a member of the Chan Zuckerberg Biohub. She was named a Rising Star in EECS by MIT and a Next Generation in Biomedicine by The Broad Institute, being the only young scientist who received such recognition in both EECS and Biomedicine.... Read more about Marinka Zitnik

Jose Zubizarreta

Associate Professor
Department of Health Care Policy
Harvard Medical School
Jose Zubizarreta, PhD, is an Associate Professor in the Department of Health Care Policy (HCP) at Harvard Medical School and a Faculty Affiliate in the Department of Statistics at the Faculty of Arts and Sciences at Harvard University. His work centers on the development of statistical methods for causal inference and impact evaluation to advance research in health care and public policy. In his methodological work, Dr. Zubizarreta develops new methods for the design and analysis of experimental and observational studies. In his health care work, he is interested in assessing the quality of care provided by hospitals and physicians using health outcomes and operations measures. His research interests also encompass comparative effectiveness research and health program impact evaluation.