Yu Wang

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, where I am a member of the Geometric Data Processing (GDP) Group, advised by Prof. Justin Solomon.

Research Interests

My research interests include geometric, graphical, probabilistic and computational problems arising in computer graphics and vision, convex optimization and statistical machine learning. I am currently working on spectral and operator approaches to these problems. Broadly speaking, I am interested in mathematical foundations of algorithms used in computer and computational sciences, with applications to real-world computing problems.

Selected Publications

  • Fast Quasi-Harmonic Weights for Geometric Data Interpolation.
    Yu Wang and Justin Solomon.
    ACM Transactions on Graphics 40(4). ACM SIGGRAPH 2021. OpenAccessPaper 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:1707.07070. Paper. 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 Deformations.
    Rajsekhar Setaluri, Yu Wang, Nathan Mitchell, Ladislav Kavan, Eftychios Sifakis.
    ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2014 (SCA 2014). Paper

  • Sound Localization and Multi-Modal Steering for Autonomous Virtual Agents.
    Yu Wang, Mubbasir Kapadia, Pengfei Huang, Ladislav Kavan, Norman I. Badler.
    ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games 2014 (I3D 2014). Paper

  • Vision-based Probabilistic Localization for Soccer Robots.
    Yu Wang, Senior Thesis, Tsinghua University.

Professional Service

Reviewer

  • ACM Transactions on Graphics (TOG)
  • ACM SIGGRAPH and ACM SIGGRAPH ASIA
  • SIAM Journal on Imaging Sciences (SIIMS)
  • IEEE Transactions on Visualization and Computer Graphics (TVCG)
  • 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

  • AAAI Conference on Artificial Intelligence (AAAI)
  • Graphics Replicability Stamp Initiative (GRSI)
  • Shape Modeling International (SMI)

Education

  • Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology.
    Ph.D. minor in Mathematics & Statistics.
    • PhD-level Subjects in areas spanning Statistical Learning, Machine Intelligence, Computer Systems and Algorithms; as well as Advanced Real Analysis, Geometry and Manifolds, Mathematical Physics, Theoretical Statistics.

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).

Honors & Awards

Skills and Coursework

I consider myself as a Computer Scientist, Applied Mathematician and Statistician, Electrical Engineer, and Full-Stack Programmer.

Ph.D.-level Advanced Graduate Subjects in the Following Areas:

  • Statistical Machine Learning
  • Artificial Intelligence
  • Optimization
  • Computer System
  • Algorithms and Theoretical Computer Science
  • Probability and Stochastic Process
  • Statistical Sciences
  • Analysis, Algebra, and Geometry
  • Theoretical & Mathematical Physics & Mechanics
  • Computational Mathematics and Sciences
  • Information, Signal, and Control

Representative Courses

Advanced Algorithms, Distributed System Engineering, Machine and Robotics Intelligence, Modern Computer Architecture, Modern Operating System, Software System Engineering, Modern Computer Networks, Theoretical Computer Science, Programming Languages, Advanced Computer Graphics, Advanced Computer Vision, Advanced Machine Learning;

Bayesian Modeling and Computing, Computational Learning Theory, Information (Theoretic) Inference, Statistical Learning Theory, Theoretical Statistics and Inference, Topics in Probability and Statistics, Advanced Operations Research, Advanced Stochastic Process, Modern Convex Optimization, Numerical Nonlinear Optimization;

Physically Based Simulation, Theoretical Foundations of Physics, Computational Physics and Math, Differential Manifold and Topology, Functional and Real Analysis, Geometric Differential Equations, Mathematics of Modern Physics, Modern and Optimal Control Theory, Numerical Analysis of Elliptic PDEs, Statistical Signal Processing.

Miscellaneous