Alkis Gotovos

Alkis Gotovos is a Postdoctoral Fellow at the Massachusetts Institute of Technology (MIT) within the Computer Science and Artificial Intelligence Laboratory (CSAIL), supervised by Professor Stefanie Jegelka. He is affiliated with the Department of Electrical Engineering and Computer Science in MIT's School of Engineering and maintains an office in the Stata Center (32-G488).

His academic background includes a doctoral degree from ETH Zurich, where he conducted research in the Learning & Adaptive Systems group under Professor Andreas Krause's supervision.

Dr. Gotovos specializes in theoretical and applied machine learning, with core expertise in discrete optimization frameworks and probabilistic modeling techniques. His work explores submodularity applications in model design, develops novel sampling methodologies for approximate inference, creates scalable learning algorithms, and implements these approaches in computational biology contexts—particularly for analyzing mutation interactions in cancer-related genes. This research bridges advanced machine learning theory with tangible biomedical challenges.

He actively contributes to MIT CSAIL's research ecosystem, focusing on algorithmic innovation for complex real-world problems in optimization and probabilistic systems.