Ray Wu

Ray Wu

CV • Google Scholar • LinkedIn • ORCID • rwu [at] cs [dot] toronto [dot] edu

If you are looking for the CSC 338 webpage (UTM, Jan-Apr 2023), click here

About Me

I'm a PhD candidate at the University of Toronto's Department of Computer Science, where I am advised by Prof. Christina Christara. My research is in using sparse grids to overcome the curse of dimensionality in numerical solvers for high-dimensional partial differential equations (PDEs). Currently, I am also working on high-order methods and their use on sparse grids for computational finance problems.

My previous work has been on using neural networks to solve PDEs, on methods of testing and validating numerical software, on the efficient simulation of stiff ODEs, and on the implementation of simplex methods for linear programming and l1 regression.

I received my undergraduate degree (BSc Honours) in Computer Science and Statistics from the University of British Columbia, where I was advised by Prof. Uri Ascher.

I am fortunate and grateful to be funded/have been funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada, the Ontario Graduate Scholarship (OGS) Program, and the Department of Computer Science throughout my studies.

Papers

  1. Wu, R and Christara, C. "The combination method for multidimensional Black-Scholes partial differential equations", accepted to Proceedings of The VI AMMCS International Conference, 2023. [pdf] (web-friendly version)
  2. Christara, C and Wu, R. "Penalty and Penalty-like methods for HJB PDEs", In Applied Mathematics and Computation, July 15, 2022. [pdf]
  3. Wu, R. "Penalty Methods for Nonlinear Problems in Financial Option Pricing", Master's Thesis, 2021. [pdf]
  4. Wu, R and Mitchell, I. "Mutant Accuracy Testing for Assessing the Implementation of Numerical Algorithms", In Proceedings of Numerical Software Verification, 2019. [pdf]
  5. Wu, R. "Analysis of Generalized-alpha vs theta-methods in Physics-based Computer Simulation of Soft Body materials", Undergraduate Honours Thesis, 2018. [pdf]

Talks

  1. Convergence remedies for Option Pricing on Sparse Grids (CAIMS/SIAM 2024), June/July 2024 [slides]
  2. The combination method for multidimensional Black-Scholes PDEs (SONAD 2023/CAIMS 2023/ICCF 2024), May 2023/June 2023/April 2024 [slides]
  3. Deep Galerkin Method with Timestepping (SONAD 2022/CAIMS 2022), May/June 2022 [slides]
  4. Penalty methods for HJB Equations (CAIMS 2021), June 2021 [slides]
  5. Penalty methods for HJB PDEs (SIAM 2020), July 2020 [slides]
  6. Mutation Testing (UBC research group talk), August 2018 [slides]

Teaching

Projects