E-mail: wangyu9 at mit.edu
Address: D475A, 32 Vassar St, Cambridge, MA 02139
This is the webpage of Yu Wang and hosted on GitHub pages.
I am broadly interested in applying computing, mathematics, learning, and optimization to solving real-world problems. My previous works bridge visual/geometric computing and mathematics, specializing in designing algorithms to process, learn, and analyze manifolds, shapes, and surfaces.
Visual & Geometric Computing, Computer Graphics & Vision, Applied & Computational Mathematics, Inverse Problems & Optimal Control, Machine Learning, 3D Vision & Shape Analysis, Geometric Deep Learning, AI for Science & Data-driven PDE, Differentiable Programming, Physical Simulation, Character Animation, Robotics, Convex Optimization & Numerical Algorithms.
Geometric Computing beyond the Laplacian.
Ph.D. Thesis, Massachusetts Institute of Technology, 2023. PDF
My PhD thesis initiates a program to systematically design novel geometric algorithms by replacing the ubiquitous Laplacian operator with better or even optimal alternatives.
Variational Quasi-Harmonic Maps for Computing Diffeomorphisms.
Yu Wang, Minghao Guo, Justin Solomon.
ACM Transactions on Graphics 42(4). ACM SIGGRAPH 2023 (Journal Track). OpenAccessPaper Dropbox 26 pages. Code Release in Progress (Check back later this month!).
When working with 3D/2D shapes/images, it is a foundational task/requirement in computer science, computational mathematics, and physical simulation to compute a map, parameterization, shape, image, correspondence, or deformation that is intersection/inversion-free, injective/bijective, or diffeomorphic/homeomorphic. We present a critical advance on this classical problem, converting deep theoretical advances in PDEs and geometric graph theory to elegant optimization algorithms, with many orders of magnitude improvement.
Learning Geometric Operators on Meshes.
Yu Wang, Vladimir Kim, Michael Bronstein and Justin Solomon.
International Conference on Learning Representations (ICLR) 2019 Workshop.
Representation Learning on Graphs and Manifolds. Paper
Steklov Spectral Geometry for Extrinsic Shape Analysis.
Yu Wang, Mirela Ben-Chen, Iosif Polterovich and Justin Solomon.
ACM Transactions on Graphics 38(1). Presented at ACM SIGGRAPH 2019. OpenAccessPaper.
Steklov Geometry Processing: An Extrinsic Approach to Spectral Shape Analysis.
Master Thesis, Massachusetts Institute of Technology. Paper
Linear Subspace Design for Real-Time Shape Deformation.
Yu Wang, Alec Jacobson, Jernej Barbič and Ladislav Kavan.
ACM Transactions on Graphics 34(4). ACM SIGGRAPH 2015. Paper
Grid-Based Nonlinear Elasticity with Spline Constraints for Image Deformation.
Rajsekhar Setaluri, Yu Wang, Nathan Mitchell, Ladislav Kavan, Eftychios Sifakis.
ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA) 2015. Paper
Vision-based Probabilistic Localization for Soccer Robots.
Yu Wang, Senior Thesis, Tsinghua University.
Ph.D. in Computer Science, Massachusetts Institute of Technology.
Ph.D. minor in Mathematics.
- B.S. in Control Theory, Tsinghua University.
- Non-degree Undergraduate Student and M.S., Computer & Information Science, University of Pennsylvania. Supported by a Full Fellowship.
- Teaching Fellow, Recitation Instructor, (MIT 6.867) (Graduate) Machine Learning, with Prof. Tommi Jaakkola, Costis Daskalakis, Pulkit Agrawal.
- Guest Speaker (CMU-MIT) Scientific Machine Learning.
- Co-Instructor, (Penn EAS205) Applications of Scientific Computation.
- Lead Teaching Assistant, (Penn CIS563) Physically Based Simulation, with Prof. Ladislav Kavan.
- Lead Teaching Assistant, (Penn EAS205) Applications of Scientific Computation.
- Research Intern, Creative Intelligence Laboratory, Adobe Research.
- Research Intern, Visual Computing Group, Microsoft Research.
- Visiting Research Fellow, Institute for Pure and Applied Mathematics, Los Angeles.
- Research Assistant, Tsinghua National Laboratory for Information Science and Technology (TNList).
- ACM Transactions on Graphics (TOG)
- IEEE Transactions on Systems, Man, and Cybernetics (TSMC)
- SIAM Journal on Imaging Sciences (SIIMS)
- IEEE Transactions on Visualization and Computer Graphics (TVCG)
- ACM SIGGRAPH and ACM SIGGRAPH ASIA
- International Conference on Machine Learning (ICML)
- Neural Information Processing Systems (NeurIPS)
- AAAI Conference on Artificial Intelligence (AAAI)
- Artificial Intelligence and Statistics (AISTATS)
- Eurographics and Pacific Graphics (EG & PG)
- Computers & Graphics
- and a few others
Program Committee Member
- Graphics Replicability Stamp Initiative (GRSI)
- Shape Modeling International (SMI)
Technical Skills & Subjects
(at the graduate level)
Math & Phys
Differential Geometry and Manifold, Measure Theory, Real and Functional Analysis, Partial Differential Equations, Complex Analysis, Mathematics of Modern Physics, Modern and Optimal Control Theory, Inverse Problems, Optimal Transport, Computational Physics and Math, Physically Based Simulation, Numerical Methods for PDEs;
Stats & Opt & EE
Statistical Inference II, Bayesian Modeling and Computing, Computational Learning Theory, Information (Theoretic) Inference, Topics in Probability and Statistics, Modern Convex Optimization, Numerical Nonlinear Optimization, Signal Processing and Analysis, Operations Research, Statistical Computing and Monte Carlo Methods;
CS & AI
Advanced Algorithms, Distributed System Engineering, Machine and Robotics Intelligence, Computer Architecture, Software System Engineering, Modern Computer Networks, Modern Operating System, Algorithms for Big Data, Theoretical Computer Science, Advanced Computer Vision, Advanced Machine Learning.