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Postdoctoral Fellows
The DSI Postdoctoral Fellows program’s goals are not only to train the next generation of leaders in data science, but also to help define the field, given that data science is still evolving. This program looks for candidates from diverse disciplinary backgrounds with the expectation that the postdoctoral researchers will be co-mentored by a data scientist and a domain expert. To broaden participation in the postdoctoral program across Columbia, DSI has partnered with Cancer Dynamics, the Center on Poverty and Social Policy, Department of Psychiatry, the Neuro Technology Center, Public Health, and the Zuckerman Institute. The institute also appoints postdoctoral researchers funded by DSI faculty members.
Arpit Agarwal | University of Pennsylvania, Computer and
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Information Science
Agarwal is interested in the human decision-making process and its interaction with machine learning algorithms — how humans choose items based on recommendations, how can we learn from their behavior and recommend better items, and how do these recommendations influence their tastes in the long-term?
Faith Anderson | Dartmouth College, Molecular and
Systems Biology
Anderson’s work is centered on the characterization of inflammatory and cellular death mechanisms in Parkinson’s disease. She continues her training in the identification and characterization of environmentally-triggered neurodegenerative disease processes.
Christian Alexander Andersson Naesseth | Linköping
University (Sweden), Electrical Engineering
Andersson Naesseth focuses on approximate statistical inference, causality, representation learning, and artificial intelligence. He develops new algorithms, theories, and practical tools to help solve challenging problems in the field of data science.
Brielin Brown | University of California, Berkeley, Computer
Science
Brown’s research lies at the intersection of machine learning and genomics. He is broadly interested in understanding how genetics and environmental factors change people’s cellular functions and lead to disease. He develops machine learning algorithms for modeling and inference in large-scale genomic studies.
Meghan Bucher | University of Pittsburgh, Neuroscience
Bucher works at the intersection of environmental health and neuroscience. Specifically, she focuses on the “middle step” between exposure and disease—the mechanisms that occur after environmental exposure or genetic mutation that can lead to neurodegeneration and disease.
Craig Connolly | The University of Texas at Austin, Marine
Science
Connolly provides effective solutions to combat the global threat of arsenic exposure by conducting interdisciplinary, transformative research that merges environmental chemistry and biogeochemistry, satellite remote-sensing and geospatial analysis, data science and machine learning, and public health.
Kyle Davis | University of Virginia, Environmental Sciences
Davis combines environmental, economic, and social considerations with direct stakeholder engagement to inform agricultural decision-making and to improve nutrition, environmental sustainability, and climate adaptation strategies. He also explores the environmental and livelihood impacts of large-scale land investments, variability and shock propagation through food trade networks, the relationship between human migration and global environmental change, and farmer coping strategies for climate variability and extremes.
Caitlin Dreisbach | University of Virginia, Data Science
Dreisbach focuses on the co-creation of health care technologies with patients. As a former labor and delivery nurse, she is interested in reimagining the current state of fetal monitoring during labor and delivery and using data, in combination with the real-world experiences of women, to enhance the care provided at the bedside.
Christian Ka’ikekūponoaloha Dye | University of Hawai’i at
Mānoa, Molecular Biosciences and Bioengineering
Dye explores the interface between environmental exposures and metabolic diseases, utilizing epigenetic information to develop novel biomarkers of disease risk. He seeks to elucidate the potential epigenetic mechanisms of disease pathogenesis and bridge his work with inclusive, community-based research among underrepresented populations, including Native Hawaiians and Pacific Islanders.
Ipek Ensari | University of Illinois at Urbana-Champaign,
Kinesiology
Ensari implements mobile health and machine learning techniques for patientgenerated health data (PGHD) to investigate disease characterization and patient symptom self-management. She investigates between-individual variability in chronic disease symptoms in response to self-management approaches; longitudinal, reciprocal fluctuations in disease symptoms to find the right point of intervention at the individual- and disease-level; and integration and summarization techniques for complex, temporal meaningful PGHD to improve their sense-making.
Kira Goldner | University of Washington, Computer Science
and Engineering
Goldner’s research is in algorithmic mechanism design: designing algorithms that guarantee that the designer’s objectives are achieved, even when the data they run on is produced by individuals acting in their own self-interest. She studies social good questions in privacy and health care, revenue maximization problems, and understanding more complex behavioral and informational models.
Yinqiu He | University of Michigan, Statistics
He develops theory and methodology to analyze large-scale and complexstructured data and address scientific problems arising from interdisciplinary studies. Her interests include high-dimensional and large-scale statistical inference, rare-event simulation, mediation pathway analysis, network analysis, statistical machine learning, and applications in statistical genetics and genomics and metabolomics.
Aviv Landau | University of Haifa (Israel), Social Work
Landau is developing an AI system that assists in the identification of child abuse and neglect in hospitals to reduce racial bias in identifications and interventions of child abuse. He is also analyzing a dataset of social media photos of Black Chicago youth affiliated with local gangs to understand the meaning of friendship, grief, and social status through their posted photos and tweets to obtain a broader and new understanding of Black youth culture.
Abraham Liddell | Vanderbilt University, History
Liddell applies data-driven methods to examine the social networks of free and enslaved Africans and their descendants in the early modern Atlantic world and maps observed social changes in their communities onto local and global historical events. A large part of his interest lies in developing ways to extract, transform, and analyze historical data to generate more precise insights into the historical past.
Jackson Loper | Brown University, Applied Mathematics
Loper produces analytical tools to understand datasets arising from new single cell experimental methods, which yield measurements for tens of thousands of features of a single cell. In which ways is it possible—or impossible—to use these kinds of measurements to understand the diversity within cell populations?
Mandal researches the design of large-scale voting systems that aggregate individual preferences and focuses on verifying and ensuring the fairness of AI systems. His thesis focused on designing systems that can help people in different fields improve their ability to make informed data-driven decisions.
Miller formulates new algorithms, statistical methods, and machine learning models that may be used to improve medical science. His applied work ranges from problems in astronomy to health care and sports analytics.
Moran develops statistical methods to analyze high-dimensional data in the sciences. Her collaborative projects include analyzing CRISPR data to identify gene interaction effects, and predicting the formation of perovskites inexpensive materials with promising photovoltaic properties for solar cells.
Sandrine Müller | Cambridge University (United Kingdom),
Psychology
Müller uses smartphone sensor data to examine how mobility patterns (e.g., the places people visit, their daily routines, distance traveled, etc.) can inform our understanding of personality and mental health. Her research aims to provide a deeper theoretical understanding of these psychological phenomena, while fostering the development of personalized interventions to promote wellbeing.
Annie Nigra | Columbia University, Environmental Health
Sciences
Nigra focuses on understanding the relationship between metal exposures (arsenic, mercury, lead, etc.) and related chronic disease, and on assessing population-level metal exposures using both biological and environmental monitoring. She evaluates federal regulations intended to reduce metal exposures and related adverse health outcomes.
Adèle H. Ribeiro | University of São Paulo (Brazil), Computer
Science
Ribeiro explores different topics at the intersection of computer science, statistics, and artificial intelligence in health care, including causal inference and learning, deep generative and discriminative models, statistical genetics and neuroscience, and multi-omics analysis. She is particularly interested in the emergent field of causal health sciences, which focuses on discovering, integrating, generalizing, and personalizing causal findings by taking advantage of the vast, but imperfect and heterogeneous, amounts of observational and experimental data available.
Alexander Root | Cornell University, Systems Biology
Root investigates human biology and ancient organisms like cyanobacteria. His work includes zooming in on structures and functions of their molecules and zooming out to see their evolution. He has developed technology to measure genes, cells, and individuals, alongside models to predict their behavior.
Aaron Schein | University of Massachusetts Amherst,
Computational Social Science
Schein uses statistical models to understand and predict factors that drive voter turnout in U.S. political elections. He measures causal effects of friend-to-friend voter mobilization efforts through large-scale digital field experiments and partners with a voter mobilization app and a polling company to use modern data science to answer questions in political science.
Yevgeny Rakita Shlafstein | Weizmann Institute of Science
(Israel), Materials and Interfaces
Rakita Shlafstein researches data harvesting during chemical synthesis using an approach known as MOSY, or movement motivated synthesis. His goal is to harvest the entire history of a chemical reaction and create a “global intuition” that is based on every experiment (successful and failed ones) relating to the process and property of materials.
Andrew Sonta | Stanford University, Civil Engineering
Sonta works on data-driven modeling, analysis, and design techniques for the improvement of social and environmental goals in the built environment. His work spans data science, engineering, design, and social science and aims to address urban sustainability challenges through a multidisciplinary lens.
Miranda Spratlen | Johns Hopkins University, Environmental
Health and Engineering
Spratlen uses metabolomics analyses in infants and children as both biomarkers of exposure and disease and to provide insight on potential pathophysiological mechanisms between early life environmental exposures and subsequent health outcomes. She is focused on effects of prenatal environmental exposures resulting from the World Trade Center disaster.
Emily Spratt | Princeton University, Art and Archaeology
Spratt investigates the development of AI-enhanced technologies for the analysis, generation, and curation of art and architecture, the ethics surrounding this subject, and the philosophical and legal implications of the use of digital images.
Dhanya Sridhar | University of California, Santa Cruz, Computer Science
Sridhar uses text and network data and adapts probabilistic models and deep learning methods to find causality. She applies causal inference in various ways, such as studying how language affects persuasion or political outcomes, how influence spreads in social networks, and whether algorithmic decisions learned from historical data are fair.
Christopher Tosh | University of California, San Diego,
Computer Science
Tosh studies theoretical machine learning and derives rigorous guarantees for learning algorithms and representations. His interests include the representational capabilities of fly olfaction, the design of automated experimentation algorithms for cancer drug discovery, and the underlying structure of modern artificial neuralnetwork representations.
Vanguri focuses on the usage of novel machine learning techniques in combination with large observational datasets to further the understanding of biological interactions. His work includes searching for rare variants responsible for adverse drug reactions and disease subtypes and the utilization of high performance computing with clinical data.
Mingzhang Yin | The University of Texas at Austin, Statistics
Yin is interested in the dependence structures underlying different data structures, such as samples, variables and functions. He is exploring the interface of machine learning and causal inference methods and hopes to apply data science to help understand the causal mechanism and treatment effect of cancer.
Isabelle Zaugg | American University, Communication
Zaugg studies language and culture, media, and digital technologies in the public sphere. She investigates the relationship between gaps in support for digitallydisadvantaged languages and patterns of mass extinction of language diversity. Her primary focus has been the digital history and online vitality of the East African languages that utilize the Ethiopic script.
