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.

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. 

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.

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.