Tyler Chen
I’m an Applied Research Lead on the quantum-inspired and randomized algorithms team at Global Technology Applied Research, JPMorganChase. 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 was previously a Assistant Professor / Courant Instructor at New York University (sponsored by Chris Musco) and a PhD student in Applied Math at the University of Washington (advised by Anne Greenbaum and Tom Trogdon). Even earlier, I was an undergrad at Tufts University, where I studied math, physics, and studio art.
Here’s an introduction to my research. Code to generate the figures from my papers can be found on Github. Please feel free to contact me with any questions about my research.
I am happy with “they” or “he” as pronouns.
Larger Projects
In addition to research papers, which can be found on my Google scholar, I’ve also worked on a number of larger 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.