Four Princeton Engineering professors have been selected as 2026 Sloan Research Fellows: Maria Apostolaki, Ben Eysenbach, Yasaman Ghasempour and Bartolomeo Stellato.
The fellowship recognizes creative early-career researchers in the sciences and social sciences. Seven faculty members from Princeton have been awarded fellowships this year, and 259 Princeton faculty have received Sloan fellowships since they were first awarded in 1955.
Sloan fellows receive a two-year, $75,000 fellowship which can be used flexibly. Fellowship candidates are nominated by their fellow scientists and winners are selected by an independent panel based on research accomplishments, creativity, and potential to become a leader in their field. Former fellows have gone on to receive the highest accolades, including Nobel Prizes and Fields Medals.
Maria Apostolaki
Apostolaki, an assistant professor of electrical and computer engineering, focuses on the design of secure, reliable and high-performance networked systems for the internet and modern data centers. She develops neurosymbolic networking foundations that unify machine learning with formal reasoning to diagnose failures, detect attacks and enforce correctness and safety properties in large, complex network environments.
Apostolaki joined the Princeton faculty in 2022. She earned her Ph.D. from ETH Zurich and completed postdoctoral research at Carnegie Mellon University. She is associated faculty in the Center for Information Technology Policy, DeCenter and the Next-G Initiative, Princeton’s program in advanced communications technology. She has received an NSF CAREER award, a Google Research Scholar Award, two Applied Networking Research Prizes from the Internet Engineering Task Force , and multiple Commendations for Outstanding Teaching from Princeton’s School of Engineering and Applied Science.
Ben Eysenbach
An assistant professor of computer science, Eysenbach researches how AI systems learn and explore. Much of his work uses reinforcement learning techniques, a type of machine learning that uses rewards and feedback to teach an autonomous agent to make intelligent decisions through trial and error. His lab is now pushing the boundaries of reinforcement learning by exploring ways AI systems can learn without rewards. The goal is to design more robust, simpler methods to address important problems in science and society.
Eysenbach joined the Princeton faculty in 2023. He is affiliated with the Princeton Program in Cognitive Science, the Princeton Language Initiative and the Natural and Artificial Minds initiative. His work has been recognized by a NeurIPS Best Paper Award, an NSF CAREER Award, and a Hertz Fellowship. He has also received an Alfred Rheinstein Faculty Award and recognition for outstanding teaching from the School of Engineering and Applied Science. He completed a doctorate in machine learning at Carnegie Mellon University and spent several years at Google Brain and Google Research before and during his doctoral work. He has an undergraduate degree in mathematics from MIT.
Yasaman Ghasempour
Ghasempour, an assistant professor of electrical and computer engineering, studies the fundamental mechanisms behind wireless communication and sensing. She develops advanced tools that use high-frequency signals, especially in the millimeter-wave and terahertz bands of the electromagnetic spectrum. She has pioneered techniques, systems and protocols to access and efficiently use this part of the spectrum, addressing its unique challenges to enable ultra-fast connectivity and high-resolution sensing for future wireless networks.
Ghasempour joined Princeton in 2021 after completing her Ph.D. at Rice University. She is associated faculty in computer science and co-directs Princeton’s NextG Initiative. Her awards include a Young Investigator Program Award from the Air Force Office of Scientific Research, a National Science Foundation CAREER Award, a Marconi Young Scholar Award and two Commendations for Outstanding Teaching from Princeton’s School of Engineering and Applied Science. She has been selected by the National Academy of Engineering to take part in the U.S. Frontiers of Engineering Symposium and she is featured in the Smithsonian Institution’s Museum of Natural History as a change-making innovator in wireless technology.
Bartolomeo Stellato
Stellato, an assistant professor of Operations Research and Financial Engineering, focuses on creating algorithms that can make critical decisions in real time. The decisions, whether guiding drones through a city, dispatching power across an electrical grid, or timing a trade in a chaotic market, must be both fast and accurate. Stellato has pioneered methods that balance computational efficiency without sacrificing reliability. A central challenge in mathematical optimization is that classical theory often predicts far worse performance than what algorithms achieve in practice. Stellato’s work closes this gap: he develops new analytical frameworks that explain why algorithms work well on real-world problems and uses those insights to design faster ones.
Stellato joined the Princeton faculty in 2020 after earning his doctorate from the University of Oxford and completing a postdoctoral appointment at MIT’s Sloan School of Management. His honors include the Beale-Orchard-Hays Prize from the Mathematical Optimization Society, awarded for outstanding computational optimization software; a CAREER Award from the National Science Foundation; and a Young Investigator Program Award from the Office of Naval Research. He received the Howard B. Wentz, Jr. Junior Faculty Award from the School of Engineering and Applied Science. Affiliated with the Robotics at Princeton and AI at Princeton initiatives, he is a fellow of Whitman and Yeh Colleges.
