Date February 19, 2019
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Sloan Foundation awards early-career fellowships to four Brown scientists

Four university assistant professors are among the 126 early-career scholars named as Alfred P. Sloan Foundation fellows this year.

Sloan FoundationPROVIDENCE, R.I. [Brown University] — Four Brown University assistant professors earned research fellowships from the Alfred P. Sloan Foundation for 2019.

On Tuesday, Feb. 19, the foundation announced the winners of the fellowships, which are awarded annually to early-career scientists and scholars from the U.S. and Canada identified as the next generation of scientific leaders.

Assistant professors Lorin Crawford (biostatistics); Kathryn Mann (mathematics); Brenda Rubenstein (chemistry); and Amitai Shenhav (cognitive, linguistic and psychological sciences), will each receive a two-year, $70,000 fellowship to further their research.

“Sloan research fellowships are among the oldest and most prestigious awards given to early-career scientists in many fields,” said Jill Pipher, vice president for research at Brown and a professor of mathematics. “For each of Brown’s four winners, this fellowship is an important honor, giving them the opportunity to further develop their outstanding research and its impact on the world. Beyond these individual achievements, this year Brown joins a small group of outstanding private research universities with four or more new Sloan fellows. We are proud of these scientific leaders and of the distinction that these awards signify for their research fields at Brown.” 

Lorin Crawford, Department of Biostatistics, School of Public Health

Lorin Crawford
Lorin Crawford

Crawford’s award will support his research building deep learning algorithms able to detect the complex interactions between genes that contribute to diseases, such as cancer. Deep learning uses multiple layers of artificial neural networks to learn complex patterns in data. It has been used commercially for recognizing and classifying images and recognizing and parsing language. One challenge with these algorithms is that it can be difficult to understand what data features they recognize and prioritize, Crawford said. He is developing tools to address this challenge when it comes to ranking genetic markers. Another research focus of his is combining information from clinical images — such as MRI results showing the shape of brain tumors — with the results of genetic tests of cancer-causing mutations.

Kathryn Mann, Department of Mathematics

Kathryn Mann
Kathryn Mann

Mann's research is in geometric topology and dynamics. Dynamics is the study of systems under change, and Mann is interested in distinguishing phenomena that are stable under perturbation from systems that are very flexible or sensitive to small changes. A major motivating theme is that stability often comes from unexpected extra symmetries, or what mathematicians call geometric structures. While many people in dynamics are motivated by physical world phenomena, Mann brings this approach to studying fundamental objects within the world of mathematics itself: group actions, manifolds and their transformations. 

Brenda Rubenstein, Department of Chemistry

Brenda Rubenstein
Brenda Rubenstein

For decades, quantum chemists have been forced to make a difficult choice: use highly predictive, many-body simulation techniques that are too slow to apply to real molecules and materials, or, use faster, one-body methods that are significantly less accurate. This compromise has limited the impact of quantum chemistry in helping to design the technologies of the future. As a Sloan fellow, Rubenstein will focus on developing new stochastic electronic structure methods that are at once highly accurate and scale well with system size to help bridge this divide and enable theory-driven materials design.

Amitai Shenhav, Department of Cognitive, Linguistic and Psychological Sciences

Amitai Shenhav
Amitai Shenhav

Shenhav will use his award to advance his research on the brain’s role in motivating humans to achieve goals. In particular, he is researching what makes some tasks more challenging than others, how people evaluate the costs and benefits of putting in the mental effort to overcome those challenges, and how long they persist in those efforts when there are tempting alternatives. To answer these questions, he uses computational modeling and brain imaging techniques, such as functional magnetic resonance imaging and electroencephalography. He primarily studies motivation in healthy people, but he plans to collaborate with clinicians to better understand what leads to impaired motivation in patients with certain psychiatric and neurologic disorders, such as depression and Alzheimer’s, and how to help those patients better achieve their goals.


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