E. Lawrence Keyes, Jr./Emerson Electric Co. Faculty Advancement Award

Danqi Chen

An assistant professor of computer science, Danqi Chen has broad interests in natural language processing (NLP) and machine learning. Her research is driven by the goals of developing effective and fundamental methods for learning representations of language and knowledge. She also works to build practical systems including question answering, information extraction and conversational agents. Chen co-leads the Princeton Natural Language Processing Group, and is part of the larger Princeton Artificial Intelligence and Machine Learning group and affiliated with the Center for Statistics and Machine Learning. She was named a 2022 Sloan Research Fellow, and in 2021 she was awarded an Innovation Research Grant from Princeton Engineering to build a neural interface to represent real-world knowledge that will integrate seamlessly with many knowledge-intensive natural language processing tasks. In nominating Chen, computer science chair Jennifer Rexford said, “Deep neural nets are a key enabling technique for NLP, and Danqi’s work leads the way in enabling widespread applications of deep neural nets in this field. Just as knowledge retrieval systems from 20 years ago powered the internet explosion and rise of big tech corporations, Danqi’s research may help transform technology and society in the coming decade.”

Minjie Chen

An assistant professor of electrical and computer engineering and the Andlinger Center for Energy and the Environment, Minjie Chen leads the Princeton Power Electronics Research Lab. His team is reimagining the power and energy systems of the future, from theory to applications. They aim to make fundamental breakthroughs in power electronics to enable important and emerging applications, including smarter power electronics at the grid edge, smaller power electronics for robotics and electric vehicles, ultra-efficient power electronics for information and data systems, and design methods and software tools for power electronics and system architectures. Chen received a 2019 NSF CAREER award, as well as 2019 and 2022 commendations for outstanding teaching from Princeton Engineering. Along with colleagues at Intel, Google and Dartmouth College, Chen’s group recently developed systems that increase power delivery to high-speed computers by 10 times beyond the current state of the art. In nominating Chen, department chair James Sturm said, “The strength of his lab is its integrated nature. One end is work on the theory of such circuits and how to model them, especially when multiple energy forms are present. … On the other end, complete energy control and management systems built in his lab are going out the door for testing at industrial collaborators such as Intel. This end-to-end approach requires high energy, but the synergies lead to new advances that could only happen in a few places.”

Howard B. Wentz, Jr. Junior Faculty Award

Karthik Narasimhan

An assistant professor of computer science, Narasimhan conducts research that spans the areas of natural language processing and reinforcement learning, with a view toward building intelligent agents that learn to operate in the world through both experience and existing human knowledge. He is especially interested in developing autonomous systems that can acquire language understanding through interaction with their environment while also using textual knowledge to drive their decision-making. Along with Danqi Chen and computer science professor Sanjeev Arora, Narasimhan co-leads the Princeton Natural Language Processing Group, and is part of the larger Princeton Artificial Intelligence and Machine Learning group and affiliated with the Center for Statistics and Machine Learning. He is the recipient of a 2022 Google Scholar Research Award; 2019 research awards from Amazon, Princeton’s Schmidt DataX Fund, and Princeton Engineering’s Project X fund; and a 2018 commendation for outstanding teaching from Princeton Engineering. Narasimhan “is a superstar whose work has been instrumental in major advances in natural language processing” computer science chair Rexford said in nominating him for the award. “He is also a wonderful teacher, and the NLP classes he has started have attracted huge interest at the undergrad as well as graduate levels.”

Michael Webb

An assistant professor of chemical and biological engineering, Webb uses theory and simulation to characterize, understand and guide the design of novel soft materials for health and sustainability. Some of his group’s current interests are motivated by the use of both natural and synthetic polymers in technologies such as batteries, fuel cells, water treatment, tissue engineering and drug delivery. They aim to use predictive modeling frameworks to study aspects of charge-transport phenomena in polymeric media, stimuli-responsive behavior of biopolymer-based solutions and gels, and the interfacial physics and properties of polymer-composite materials. Webb is an associated faculty member of the Andlinger Center for Energy and the Environment, the Princeton Institute for Computational Science and Engineering and the Princeton Institute of Materials. This year, he received a grant from the engineering school’s Project X innovation fund for the project “Molecularly informed investigation of contact charging between insulating polymer surfaces.” In nominating Webb, department chair Athanassios Panagiotopoulos said that Webb “has a unique ability to identify important problems and apply tools from sophisticated statistical mechanics and machine learning to provide solutions” and “has made a strong start in generating truly novel, creative ideas and in building cross-disciplinary interactions.”

Alfred Rheinstein Faculty Award

Daniel Cohen

An assistant professor of mechanical and aerospace engineering, Cohen focuses on bioengineering with applications to biomaterials and tissue engineering. His lab takes the swarming and collective behaviors that allow tissues to heal injuries, grow, and form complex structures and connects them to engineering approaches that enable new ways to control these behaviors. For example, his group has built devices to herd the migration of hundreds of thousands of cells in a manner analogous to sheep herding. This technology may enable better control of tissue growth and faster wound healing. Cohen is an associated faculty member of the Department of Molecular Biology, the Department of Chemical and Biological Engineering, and the Quantitative Cell Biology graduate program at the Lewis-Sigler Institute. He is the recipient of a 2021 NSF CAREER award and a 2019 NIH/NIGMS Outstanding Investigator Award. Cohen was awarded Innovation Research Grants from Princeton Engineering in 2018 and 2020, as well as four commendations for outstanding teaching. “Daniel is a very creative scientist who integrates ideas from several areas to think about new interdisciplinary biological applications. He stimulates others with his ideas and suggestions that have led to collaborations and new projects. He is a superb teacher, motivates students, and is an outstanding mentor,” said Howard Stone, the chair of mechanical and aerospace engineering, in nominating Cohen for the award.

Boris Hanin

An assistant professor of operations research and financial engineering, Hanin is a mathematician working on deep learning and semi-classical problems in quantum mechanics. In 2021, he received an NSF CAREER award for the project for the project “Random neural nets and random matrix products.” This work combines ideas from random matrix theory, stochastic processes, and perturbation theory to develop a range of principled techniques for understanding key aspects of how neural networks work in practice and how to make them better. Hanin was awarded a commendation for outstanding teaching from the engineering school for fall 2021. He is also the founder and organizer of the annual Princeton Machine Learning Theory Summer School, to be held on campus this June. “In an area with thousands of authors and papers and computational experiments, Boris’s work stands out in its depth and its rigor,” said department chair Ronnie Sircar in nominating him for the award. “His papers push towards an understanding of the important parameters for successful training, particularly for deep neural nets.”