Tomer Galanti

Short bio

Tomer Galanti is an Assistant Professor in the Department of Computer Science and Engineering at Texas A&M University. His research focuses on the theoretical and algorithmic foundations of deep learning and large language models. Combining theory and experimentation, his work addresses core challenges in deep learning efficiency — including reducing data requirements, designing compressible networks, enabling model adaptation to new tasks, accelerating inference, and improving training stability.

Prior to joining Texas A&M, he was a postdoctoral associate at MIT's Center for Brains, Minds & Machines, where he worked with Tomaso Poggio. He received his Ph.D. in Computer Science from Tel Aviv University, advised by Lior Wolf. In 2021, he interned as a Research Scientist at Google DeepMind, collaborating with Andras Gyorgy and Marcus Hutter.

Teaching