Sitan Chen

Sitan Chen is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences. His research focuses on foundational aspects of machine learning, quantum information, and algorithm design, with a particular emphasis on provable guarantees for generative modeling, deep learning, and quantum learning. He is affiliated with the Theory of Computation group, the ML Foundations group, and the Harvard Quantum Initiative.

• Education:
• PhD in EECS from MIT (advised by Ankur Moitra)
• Bachelor's in Mathematics and Computer Science from Harvard (advised by Salil Vadhan and Leslie Valiant)
• Grants & Awards:
• NSF CAREER Award (CCF-2441635)
• NSF Small (CCF-2430375)
• Harvard Dean's Competitive Fund for Promising Scholarship
• Research Interests:
• Generative models and diffusion processes
• Quantum tomography and quantum learning
• Algorithmic foundations for inverse problems
• Provably efficient learning algorithms
• Advising & Teaching:
• Advises PhD/Masters students in quantum computing and machine learning
• Teaches courses like Quantum Learning Theory and Algorithms for Data Science
• Key Contributions:
• First polynomial-time algorithms for learning narrow neural networks
• Advances in quantum state estimation with minimal resources
• Provably efficient sampling methods for diffusion models