University of Pennsylvania studying Mathematics and Computer Science. I specialize in building full-stack systems, probabilistic models, and the occasional passion project that keeps me up at night.
Tech and math have been part of my life for as long as I can remember. As a kid I was taking apart anything with a circuit board. By high school I was building computers and teaching myself to code. At 18, online poker led me to discover a vulnerability in a crypto casino's shuffling algorithm that created predictable patterns every few hundred hands. I taught myself to exploit it (ethically, of course), and was hired to help fix their system. Since then I've been obsessed with applying algorithmic reasoning through game theory, probabilistic thinking, and machine learning to real-world problems.
I'm excited by projects that are ambitious, creative, and technically challenging. I enjoy turning complex ideas into intuitive and functional digital experiences, whether it's a rough concept or a fully formed vision. When I'm not studying or coding, you'll find me at Penn Boxing, lifting, playing poker, or listening to some good music.
Python
C++
R
OCaml
Java
MATLAB
HTML5
CSS
JavaScript
NumPy
Pandas
Scikit-learn
PyTorch
React
TensorFlow
Matplotlib
MySQL
PostgreSQL
MongoDB
Node.js
Git
Building real-time adversarial models to detect and score fraudulent transactions across Visa's global payment network.
Built a low-latency prediction market arbitrage engine in Python that detected and executed cross-platform pricing discrepancies in under 50 milliseconds across 50+ trading pairs.
Built automation scripts and predictive models at a CPA firm to streamline large-scale financial reconciliation and enable forward-looking tax advisory for corporate clients.
Built computer vision and NLP pipelines to digitize 50,000+ pages of historical defense records and conducted quantitative analysis that surfaced significant shifts in government spending patterns.
Optimized Monte Carlo plasma simulations at a DOE fusion research lab through low-level SIMD vectorization and parallel computing, and automated large-scale regression testing infrastructure.
Resume will be sent upon request. Send me an email at thomasou@sas.upenn.edu