I have a strong background in theoretical computer science and take pride in writing clean, maintainable, and performant code. My approach is grounded in sound theory and mathematical modeling, ensuring that solutions are both principled and practical. I believe in doing things the right way, and doing so comes naturally to me. I'm drawn to fundamental challenges and strive to solve problems at their core.
A framework for typeful ML programming is explored. By applying principles from programming language theory and design, this framework aims to make scalable, modular, and reusable code for complex machine learning models and algorithms practically achievable.
Fides is a formal language designed for distributed adversarial environments, where trust is not a given. It is inherently concurrent and supports unlimited metaprogramming. Crucially, Fides provides an interaction-universal primitive that allows parties to cooperate with each other.
Via rigorous study of fluid (heat) exchangers, a family of exchangers is found that far outperforms traditional designs.
Generative AI is applied to the problem of asset allocation, leading to more effective, holistic and personalized portfolio optimization.