Harvard Data Science Initiative Postdoctoral Fellows

The Harvard Data Science Initiative Postdoctoral Fellows are outstanding early-career researchers whose interests lie in a number of different fields. HDSI Fellows work independently over a two to three year fellowship with the guidance and partnership of Harvard University faculty.

NOW CLOSED | 2020 Request For Applications

Current HDSI Postdoctoral Fellows

Isabel Fulcher

Isabel Fulcher

HDSI Postdoctoral Fellow
Department of Global Health and Social Medicine
Harvard Medical School

Isabel Fulcher’s research aims to develop innovative causal inference methodologies to improve the delivery of sexual and reproductive health care to at-risk populations. She is particularly interested in statistical methods that can identify underlying causal mechanisms ofpotential interventions in settings where this proves particularly challenging, such as in the presence of unmeasured confounding and spillover effects on a network. Currently, Isabel is working to evaluate and improve the implementation of digital maternal health interventions in Tanzania and Rwanda. As part of these global partnerships, she is committed to statistical capacity building by supporting in-country researchers to participate in and leadlead research.

... Read more about Isabel Fulcher

Sam Mehr

Dr. Samuel Mehr

HDSI Postdoctoral Fellow
Department of Psychology
Harvard Faculty of Arts and Sciences

Samuel Mehr is a Research Associate in the Department of Psychology at Harvard University, where he directs the Music Lab. Sam studies music: how the design of the human mind leads us to perceive, create, and engage with music, and how this psychology of music may be leveraged to improve health outcomes in infancy and adulthood. These questions are multidisciplinary, drawing insights from the cognitive sciences, evolutionary biology, anthropology, ethnomusicology and music theory, linguistics, and computer science. To learn more about Sam's research and to participate in music research online, please visit http://themusiclab.org.... Read more about Samuel Mehr

Evan Rosenman

Evan Rosenman

HDSI Postdoctoral Fellow
The Institute of Quantitative Social Sciences
Harvard University

From an early age, Evan Rosenman has been interested in problems at the interface of the mathematical sciences and public policy. Evan earned his undergraduate degree at Harvard, where he majored in Applied Mathematics. Evan subsequently did a stint as a software Product Manager in Washington, D.C. Finding himself drawn to the academic study of data science, he earned a part-time Master's Degree in Statistics and Mathematics from Georgetown. In 2015, Evan moved to California to begin a doctorate in Statistics at Stanford, which he completed in 2020. Evan's research interests have been motivated by the real-world challenges faced by statistical practitioners, and center on questions of causal inference. Throughout graduate school, he has also pursued data analytics opportunities with various progressive political organizations, including the Black Voters Matter Fund and the Morris County, NJ Democratic Committee. Outside of research, Evan enjoys stand-up comedy, cooking, and seeing movies.... Read more about Evan Rosenman

Matthew Cooper

Matthew Cooper

HDSI Postdoctoral Fellow
Department of Global Health and Social Medicine
Harvard Medical School

Matthew Coooper is an environmental geographer interested in how global environmental change is affecting human well-being. Before academia, Matthew lived all over the world - from Mali to Alaska to the Philippines - working at the intersection of policy and research. Now, he works with large datasets at global scales using data science methods, but with his research questions and hypotheses deeply informed by his experiences in the field and in the policy sector.

... Read more about Matthew Cooper

Melanie Pradier

Melanie F Pradier

HDSI Postdoctoral Fellow
Department of Computer Science
Harvard John A. Paulson School of Engineering and Applied Sciences

Melanie F. Pradier is currently a DSI Postdoctoral Fellow working on probabilistic models for data exploration. Her current research interests include interpretable machine learning, probabilistic graphical models, approximate inference techniques (MCMC and variational techniques), dependent random measures, clustering and topic modeling, biomedical applications, and information theory.A detailed CV and list of publications or patents can be found at her personal website.... Read more about Melanie F Pradier

Carolina

Carolina Nobre

HDSI Postdoctoral Fellow
Department of Computer Science
Harvard John A. Paulson School of Engineering and Applied Sciences

Carolina's research is focused on visualizing networks that are associated with attributes (multivariate). More recently she has started a project to better understand how users interact with complex visualizations. During her PhD she published on graphs in the context of genealogies and on using spanning trees and associating attribute tables as an approach to visualizing general multivariate networks. She also conducted a large user study to assess how different multivariate network visualization techniques are best suited for different network exploration tasks. Her Master’s thesis addressed the challenge of visualizing multivariate oceanographic data, which she modeled as trees of currents.... Read more about Carolina Nobre

Michelle Ntampaka

Michelle Ntampaka

HDSI Postdoctoral Fellow
Department of Astronomy
Harvard Faculty of Arts and Sciences

Michelle Ntampaka's research focuses on constraining cosmological models with the large scale structure of the Universe. She uses machine learning and statistical tools to tease out complicated patterns in the data that are inaccessible through more traditional means. She received her Ph.D. from Carnegie Mellon University and she is currently a HDSI postdoctoral fellow.  In the fall of 2020, she will step into the role of tenure-track Assistant Astronomer at the Space Telescope Science Institute.... Read more about Michelle Ntampaka

Herren

Cristina Herren

HDSI Postdoctoral Fellow
Department of Biomedical Informatics
Harvard Medical School
Cristina Herren is broadly interested in combining statistics, mathematical models, and experiments to study complex biological systems that are not easily deconstructed into separate parts. Her current research investigates how microbial communities assemble and change, and whether these processes are predictable. While completing her PhD at the University of Wisconsin-Madison, Cristina worked in a number of varied ecosystems (studying biofilms, flies, zooplankton, and aquatic bacteria) with the goal of understanding what drives the abundances of populations over time. She also enjoys the statistical challenges presented by the new kinds of data generated while studying microbial communities. She believes that microbial ecology lies at the intersection of ecological theory, microbial systems, and applied statistics. Her research touches on all these themes – during the course of applying ecological theory to microbial ecosystems, she often ends up developing new statistical methods for these novel datasets. 
Joseph Dexter

Joseph Dexter

HDSI Postdoctoral Fellow
Department of Human Evolutionary Biology
Harvard Faculty of Arts and Sciences

Joseph Dexter is a Neukom Fellow at Dartmouth College and is an incoming HDSI Postdoctoral Fellow. A computational biologist by training, he has broad interests across data science, and he is particularly enthusiastic about research that brings together traditionally quantitative and qualitative disciplines. To that end, most of his research is concentrated in two interdisciplinary areas: the Digital Humanities, including computational text analysis for Latin, ancient Greek, and other premodern traditions and the cultural evolution of literature, and systems biology and mathematical modeling for biomedicine.... Read more about Joseph Dexter

Damian Blasi

Damián Blasi

HDSI Postdoctoral Fellow
Department of Human Evolutionary Biology
Faculty of Arts and Sciences, Harvard University

Damián E. Blasi is a researcher at the intersection of linguistics, anthropology, cognitive sciences, and data sciences. His work aims to understand which aspects of collective and individual human behavior shape the structure of languages by leveraging data on present and past linguistic diversity. His recent projects include studying transmission processes during the emergence of creole languages, regular sound-meaning associations appearing independently around the globe, and the influence of subsistence and behavior on the speech apparatus since the beginning of the Holocene.... Read more about Damián Blasi

Jenny Chen

Jenny Chen

HDSI Postdoctoral Fellow
Department of Molecular & Cellular Biology
Harvard Faculty of Arts and Sciences
Jenny Chen's research focus is on understanding the evolution of gene regulation across mammalian species, and applying these principles to improving the diagnosis and treatment of genetic diseases.  She received my PhD from the Bioinformatics and Integrative Genomics (BIG) program at MIT, where she was advised by Aviv Regev.
Max Kleiman-Weiner

Max Kleiman-Weiner

HDSI Postdoctoral Fellow
Department of Computer Science
Harvard John A. Paulson School of Engineering and Applied Sciences
Max's interests span the study of intelligence (natural), intelligence (artificial), and intelligence (moral). His goal is to understand how the human mind works with enough precision that it can be implemented on a computer. He also draw on insights from how people learn and think to engineer smarter and more human-like algorithms for artificial intelligence. Max primarily researches social intelligence where the scale, scope, and sophistication of humans is distinct in the natural world and where even a kindergartener exceeds the social competence of our best AI systems. He builds computational cognitive models of social intelligence using tools from Bayesian inference, reinforcement learning, and evolutionary game theory. These models give precise accounts of human social cognition and make fine-grained predictions that he can test empirically in multi-agent behavioral experiments.
Proctor

Jonathan Proctor

HDSI Postdoctoral Fellow
Center for the Environment
Harvard Faculty of Arts and Sciences

Jon develops and pairs methods in econometrics, spatial statistics and machine learning with global socio-environmental datasets to empirically estimate the relationships that govern our climate and agricultural systems. For example, in recent work Jon uses volcanic eruptions as natural experiments to provide the first empirically-based estimates of how solar geoengineering might impact agricultural yields. In a second strand of research he develops, characterizes and democratizes new algorithms for planetary-scale monitoring using satellite imagery.... Read more about Jonathan Proctor

GonzaloMena

Gonzalo Mena

HDSI Postdoctoral Fellow
Department of Statistics
Harvard Faculty of Arts and Sciences

Gonzala Mena aims to develop methods for turning large volumes of data collected in Science into useful knowledge. One major challenge is scalability, but others equally important: algorithms need to be robust since corruption is an ubiquitous problem in data collection. Also, since data cannot always tell the entire story, uncertainty must be properly accounted for to avoid overconfident wrong inferences. 

... Read more about Gonzalo Mena

Hannah Correia

Hannah Correia

HDSI Postdoctoral Fellow
Department of Biostatistics
Harvard T.H. Chan School of Public Health

Hannah Correia's research focuses on the formulation of novel statistical methods to increase accuracy of quantifying causal relationships and creating models to predict potential ecosystem variations. She is also interested in advancing statistical methods for modeling high-dimensional data common to ecological and climatic studies, where it is difficult to apply basic nonparametric approaches. Each year she participates in the Masamu Program, a collaborative workshop in southern Africa focusing on advancing mathematical sciences research with U.S. and African students. Some of the research projects she has been involved with at Masamu include modeling elephant population dynamics, determining the effects of stigma on the spread of HIV/AIDS, and modeling multiple paternity among clades of animals.... Read more about Hannah Correia

Former HDSI Postdoctoral Fellows

Minsuk Shin

Minsuk Shin

Assistant Professor
Department of Statistics
University of South Carolina

Minsuk Shin's research focuses on developing high-dimensional and semi-parametric Bayesian methods motivated by improving practical performance in real world applications and studying theoretical properties of such proce- dures. His current and previous work focuses on procedures for high-dimensional Bayesian model selection and developing continuous shrinkage priors on functions in nonparametric settings. Minsuk has also worked on a scalable stochastic search algorithm to explore the model space of high-dimensional linear models. In general, Minsuk is broadly interested in obtaining theoretically-grounded solutions to applied biomedical and machine learning problems. One of his fundamental research goals is to develop general purpose tools for inferences, predictions, and hypothesis testing in high-dimensional settings, while also developing scalable computing algorithms for implementing such tools. Recently, he has started a new research project for scalable uncertainty quantification via generative processes. The main idea of this is to circumvent a computational bottleneck in traditional frameworks, like posterior computation via MCMC, by constructing a generator of parameter samples from the corresponding posterior distribution.... Read more about Minsuk Shin

Eric Schulz

Eric Schulz

Principal Investigator
Computational Principles of Intelligence Lab
Max Planck Institute for Biological Cybernetics
Eric Schulz did his undergrad in Psychology at Humboldt University in Berlin, followed by a MSc in Cognitive and Decision Sciences at University College London (UCL), a MSc in Applied Statistics at the University of Oxford, and a MRes in Computer Science again at UCL. He finished his PhD at UCL in 2017, where he worked on generalization and exploration in reinforcement learning and was supervised by Maarten Speekenbrink. From 2017 to 2019, he was a Data Science Postdoctoral Fellow at Harvard University, where he worked with Samuel Gershman and Joshua Tenenbaum on computational models of learning and decision making. He is a recipient of the Robert J. Glushko Prize for Outstanding Doctoral Dissertation in Cognitive Science and a Jacobs Foundation Research Fellowship.
Xu Shi

Xu Shi

Assistant Professor
Department of Biostatistics
University of Michigan

Xu Shi is an Assistant Professor in the Department of Biostatistics at University of Michigan. She was a postdoctoral fellow at the Harvard Data Science Initiative, working with Tianxi Cai and Eric Tchetgen Tchetgen in the Department of Biostatistics at the Harvard TH Chan School of Public Health. She received her Ph.D. in Biostatistics at University of Washington, under the supervision of Andrea Cook and Patrick Heagerty. She obtained a B.S. in Mathematics and Applied Mathematics at the Chu Kochen Honors College from Zhejiang University, China.... Read more about Xu Shi

Kun-Hsing Yu

Kun-Hsing Yu

Assistant Professor
Department of Biomedical Informatics
Harvard Medical School
Kun-Hsing "Kun" Yu, MD, PhD is an Assistant Professor in the Department of Biomedical Informatics at Harvard Medical School. He integrates cancer patients' multi-omics (genomics, epigenomics, transcriptomics, and proteomics) profiles with quantitative histopathology patterns to predict their clinical phenotypes. He developed the first fully-automated algorithm to extract thousands of features from whole-slide histopathology images, discovered the molecular mechanisms underpinning the microscopic phenotypes of tumor cells, and successfully identified previously unknown cellular morphologies associated with patient prognosis. Dr. Yu's research interests include quantitative pathology, machine learning, and translational bioinformatics.