Jason Altschuler
Assistant Professor of Statistics and Data Science
Jason Altschuler's research interests are broadly at the interface of optimization, probability, and machine learning, with a focus on the design and analysis of large-scale algorithms.
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Victor Amelkin
Former Warren Center Postdoctoral Fellow
Victor Amelkin is currently a Research Scientist at Amazon Robotics. He works on network science, with a general emphasis on analysis, modeling, and control of network processes, and a specific emphasis on network (process) resilience.
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Sebastian Angel
Raj and Neera Singh Term Assistant Professor of Computer and Information Science
Sebastian Angel's work studies different aspects of systems, security, privacy, and networking. He's particularly interested in understanding how systems that we use today unintentionally leak information and building the next generation of anonymous and censorship-resistant communication systems.
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Tom Baker
William Maul Measey Professor of Law and Health Sciences
Tom Baker, a preeminent scholar in insurance law, explores insurance, risk, and responsibility using methods and perspectives drawn from economics, sociology, psychology, and history. His current research interests include the regulation of, by, and through algorithms and choice architecture.
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Yoseph Barash
Associate Professor of Genetics
Yoseph Barash develops machine learning algorithms that integrate high-throughput data to infer RNA biogenesis and function, followed by experimental verifications of inferred mechanisms.
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Danielle Bassett
Eduardo D. Glandt Faculty Fellow, Associate Professor of Bioengineering
Danielle Bassett studies biological, physical and social systems by using and developing tools from network science and complex systems theory. Her broad goal is to isolate problems at the intersection of basic science, engineering, and clinical medicine.
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Hamsa Bastani
Assistant Professor of Operations, Information and Decisions
Hamsa Bastani’s research interests center around optimizing service operations by developing novel data-driven statistical decision-making tools using techniques from machine learning, and designing improved performance-based contracts using detailed outcomes data on strategic firms and workers.
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Richard Berk
Professor of Criminology and Statistics
Richard Berk works on topics in applied statistics including causal inference, statistical/machine learning, and methods for evaluating social programs. Among his criminology applications are inmate classification and placement systems, and law enforcement strategies for reducing partner violence.
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