Computer Science and Artificial Intelligence Laboratory
Electrical Engineering and Computer Science
Massachusetts Institute of Technology

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.

About Me

I am a Ph.D. student of Computer Science at MIT with a PhD minor in Mathematics, where I am a member of the Geometric Data Processing (GDP) Group, advised by Prof. Justin Solomon.

Research Interests

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.

Research Areas

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.

Selected Publications

• Geometric Computing beyond the Laplacian.
  Yu Wang.
  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.}

• Fast Quasi-Harmonic Weights for Geometric Data Interpolation.
  Yu Wang and Justin Solomon.
  ACM Transactions on Graphics 40(4). ACM SIGGRAPH 2021. OpenAccessPaper. 15 pages. Code

• Intrinsic and Extrinsic Operators for Shape Analysis.
  Yu Wang and Justin Solomon.
  Processing, Analyzing and Learning of Images, Shapes, and Forms, 2019. Preprint Publisher

• 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.
  arXiv. Code

• 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.

Education

• 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 Experience

• 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.

Selected Awards

• Siebel Scholar.
• MIT Presidential Fellow.

Work Experience

• 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).

Professional Service

Reviewer

• 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.