Alumni

GonzaloMena

Gonzalo Mena

Florence Nightingale Bicentennial Fellow and Tutor in Computational Statistics and Machine Learning
University of Oxford

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. 

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Max Kleiman-Weiner

Max Kleiman-Weiner

Co-founder and CEO
Common Sense Machines
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. Max is Co-founder and CEO of Common Sense Machines, a new startup building human-level artificial intelligence. 
Herren

Cristina Herren

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. 
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.
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.
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

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

Melanie Pradier

Melanie F Pradier

Senior Research Scientist
Microsoft Research

Melanie F. Pradier is currently a senior research scientist at Microsoft Research MSR in Cambridge, UK. Her current research interests include interpretable machine learning, probabilistic graphical models, approximate inference techniques (MCMC and variational techniques), and biomedical applications. Some of the general questions she is trying to answer are: How can we better quantify uncertainty (when should you trust your model)? How can we design expressive, interpretable priors in Bayesian models? How can we combine human knowledge with data-driven evidence? A detailed CV and list of publications or patents can be found at her personal website.... Read more about Melanie F Pradier

Michelle Ntampaka

Michelle Ntampaka

Assistant Astronomer
Space Telescope Science Institute

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 stepped into the role of tenure-track Assistant Astronomer at the Space Telescope Science Institute.... Read more about Michelle Ntampaka

Sam Mehr

Dr. Samuel Mehr

Research Associate
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