Tyler Chen

I’m an Assistant Professor of Computer Science at NYU Shanghai. My research focuses on the design and analysis of fast and theoretically justified (randomized) algorithms for core linear algebra tasks, with the overarching goal of developing tools to support the advancement of knowledge in current scientific applications.

I previously worked as an Applied Research Lead at JPMorganChase and as a Courant Instructor at New York University (sponsored by Chris Musco). I recieved my PhD in Applied Math from the University of Washington (advised by Anne Greenbaum and Tom Trogdon). Even earlier, I did my undergrad at Tufts University, where I studied math, physics, and studio art.

Here’s an introduction to my research and a list of upcoming travel and past slides.

I am happy with “they” or “he” as pronouns.

Students

I have funded positions available for PhD students and a limited number of undergraduate students. If you’re interested in working with me, please read this first!

Other Projects

In addition to research papers, I’ve also worked on a number of projects that may be of interest to the research community.

• Randomized Numerical Linear Algebra with Examples
  • Online book providing a first introduction to RandNLA, with many accompanying numerical examples.
• The Lanczos algorithm for matrix functions: a handbook for scientists
  • Accessible introduction to Lanczos-based methods for matrix functions, with a particular emphasis on conceptual understanding of the algorithm in finite precision arithmetic.
• Application Materials
  • Sample materials from my past applications and grant proposals.
• PolarExpress visualization
  • interactive visualization of the PolarExpress algorithm