Tayebati Postdoctoral Fellowship Program
The Tayebati Postdoctoral Fellowship Program at MIT supports exceptional researchers whose work explores the transformative potential of artificial intelligence in science and music.
Sponsored by the MIT Schwarzman College of Computing, the program fosters research at the forefront of AI, where novel computational approaches can advance discovery in select scientific fields and expand the possibilities of music composition and performance. By encouraging ambitious, interdisciplinary scholarship, the fellowship seeks to generate meaningful progress both within these domains and in the development of new AI methods more broadly.
Tayebati Fellows benefit from an environment designed to support their academic and professional growth while enabling them to pursue ambitious, high-impact research. Each fellow is mentored by MIT faculty in both their chosen disciplinary area and artificial intelligence, fostering interdisciplinary collaboration and innovation. Fellows undertake projects in one of six disciplinary areas:
- Brain and Cognitive Sciences
- Biology/Bioengineering
- Chemistry/Chemical Engineering
- Materials Science and Engineering
- Music
- Physics
Fellows are awarded a stipend starting at $75,000 annually, plus MIT benefits. The program also provides recipients with travel grants for academic conferences as well as computer access and various programmatic activities.
2025-2026 Tayebati Fellows
Congyue Deng completed her PhD in computer science at Stanford University. Her research interests include 3D computer vision and geometric deep learning. Her work focuses on designing feature representations for visual understanding that incorporate symmetries and geometric relations. She received her BS in mathematics from Tsinghua University in 2020 with the top GPA in her class (1/114). She is also a recipient of the CPAL Rising Star Award in 2025.
Faculty mentors: Marin Soljacic (Physics), Marin Soljacic (Physics)
Berthy Feng completed her PhD in computing and mathematical sciences at Caltech. While receiving her PhD, she was advised by Katie Bouman. She received her undergraduate degree in computer science at Princeton University. Berthy works on computational imaging, computer vision, and machine learning. She is primarily interested in developing data-driven and physics-based methods for scientific imaging.
Faculty mentors: Mike Williams (Physics), Bill Freeman (EECS)
Gokul Gowri completed his PhD at Harvard in systems, synthetic, and quantitative biology. He builds computational and statistical tools to better understand living systems. At Harvard, he worked closely with Peng Yin and Allon Klein on problems related to information theory, representation learning, and single-cell genomics.
Faculty mentors: Jonathan Weissman (Biology), Stephen Bates (EECS)
Emily Oliphant completed her PhD in materials science and engineering and scientific computing at the University of Michigan. During her PhD, she was co-advised by Wenhao Sun and Emmanouil Kioupakis. She received her BS in physics from Idaho State University in 2020, where she worked in particle physics at the Idaho Accelerator Center before shifting her focus to materials physics.
Faculty mentors: Jeff Grossman (DMSE), Tess Smidt (EECS)
Jonas Rein completed his PhD in organic chemistry at Cornell University. He was born in Germany and grew up in Mainz, where he completed his BS in chemistry at Johannes-Gutenberg University in 2019, working with Siegfried Waldvogel on the electrochemical oxidation of arenes. During his PhD, he worked closely with Song Lin on organic redox chemistry and was supported by an ERP fellowship.
Faculty mentors: Masha Elkin (Chemistry), Connor Coley (ChemE/EECS), Stephen Bates (EECS)