About Me

I’m a computer scientists interested in processor simulation and design as well as deep learning (specifically, deep reinforcement learning). I am open to colloboration on projects, and am actively looking for post-graduation opportunities.

I’m pursuing my M.S. in Computer Science at Georgetown University (expected to graduate spring 2021), and I received my B.S. in Computer Science/Math from Pepperdine Univeristy.

Current Interests

My time is currently split between two major projects. The first is exploring the applications of deep reinforcement learning to quantum gravity, in collaboration with San Jose St. University and Georgetown. We are exploring the topic of bakground independent quantum gravity, which analyszes the spontaneous evolution of space using graph theory.

The second project, Pep/10, is a colloboration with faculty at Pepperdine university. This project is an across-the-board architectural refresh of the Pep/9 computer. It will replace the backend of the Pep system with a RISC-V chip, with an embedded program to emulate the Pep/10 instruction set. The new computer features a macro-assembly language and improved operating system semantics. All these changes provide increased realism while easing students into the field of computer architecture.

Previous Works

The Pep9Milli project attempts to use symbolic verification to prove the correctness of the Pep/9 processor. This project detected multiple errors in existing processor microcode. While this work was unpublished, it remains a possible source of inspiration for future works.