Summary
Yu Wang is a Research Fellow at MIT CSAIL and a soon-to-graduate PhD in EECS with a math & statistics minor, bringing nine years of research experience at the intersection of visual/geometric computing, applied mathematics, and machine learning. His work focuses on designing algorithms for manifolds, shapes, and surfaces—spanning geometric deep learning, differentiable programming, data-driven PDEs, and physical simulation—with applications from character animation to robotics. He has held research internships at Adobe and Microsoft Research and academic positions at UPenn and Tsinghua, demonstrating a consistent track record of translating theory into practical algorithms. Known for blending convex optimization and numerical methods with modern ML, he often frames inverse problems and control tasks in geometry-aware ways. Based in Greater Boston, he combines deep mathematical rigor (5.0/5.0 PhD record) with hands-on experience building tools for 3D vision and AI-for-science problems.
9 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Electrical Engineering & Computer Science, Ph.D. minor in Mathematics & Statistics, 5.0/5.0, Doctor of Philosophy - PhD, Electrical Engineering & Computer Science, Ph.D. minor in Mathematics & Statistics, 5.0/5.0 at Massachusetts Institute of Technology
Bachelor of Science - BS, Control Theory, Bachelor of Science - BS, Control Theory at Tsinghua University
Non-degree Undergraduate Student and Research M.S., Non-degree Undergraduate Student and Research M.S. at University of Pennsylvania
Chinese, English