The School of Engineering and Applied Science has honored Ben Eysenbach and Ellen Zhong with junior faculty awards for early-career excellence in research and teaching. Zhong is one of three recipients of the E. Lawrence Keyes, Jr./Emerson Electric Co. Faculty Advancement Award, and Eysenbach is the recipient of the Alfred Rheinstein Faculty Award. They are two of six assistant professors to receive a junior faculty award this year. Each recipient will receive $50,000 to support their research.
An assistant professor of computer science, Ben Eysenbach focuses on reinforcement learning, a type of machine learning that uses rewards and feedback to teach an autonomous agent to make intelligent decisions through trial-and-error. Eysenbach’s work draws connections between different areas of machine learning and reinforcement learning, with the goal of designing more robust, simpler methods to address important problems in science and society. “Professor Eysenbach is a star in the making, who promises to extend the reach of reinforcement learning to a variety of new problems in science and engineering,” writes Szymon Rusinkiewicz, chair of computer science. Since starting at Princeton in 2023, he added, Eysenbach “has well over a dozen publications at the most prestigious conferences in his field.” In addition to his outstanding research contributions, Eysenbach has also had an important impact on teaching, co-developing a new course on reinforcement learning in 2024 and earning an outstanding teaching commendation from the Engineering School for a graduate course he taught in 2023. He completed a Ph.D. in machine learning at Carnegie Mellon University and spent a number of years at Google Brain/Research before and during his doctoral work. He has an undergraduate degree in mathematics from MIT.
Ellen Zhong, an assistant professor of computer science, uses artificial intelligence methods to expand the range of possibilities for biological imaging techniques. She has pioneered the use of cryo-electron microscopy, especially focusing on the reconstruction of proteins at the atomic scale. For proteins, minute variations in structure imply major differences in function, so understanding their shapes in such fine detail has had transformative impacts on medicine. “This area of work has brought on a revolution in the field of structural biology, overcoming some of the limitations of earlier imaging methods,” said Szymon Rusinkiewicz, chair of computer science. “Her record more broadly is impressive, with publications spanning the range [of scientific journals] from those in artificial intelligence focused on fundamental image-processing methods all the way to more-applied work in structural biology focused on specific proteins,” he added. In addition to her research, Zhong has made a strong impact as a teacher. Since joining Princeton in 2022, she has developed a graduate seminar called “Machine Learning for Structural Biology,” for which she earned an outstanding teaching commendation from the Engineering School; taught an undergraduate course on “Mathematics for Numerical Computing and Machine Learning,” and shown exceptional leadership as an adviser and mentor to her students. Zhong previously interned at DeepMind on the AlphaFold team, and she earned her Ph.D. from MIT in 2022.