Building an autonomous go-kart with Autonomous Motorsports Purdue. Created a vision stack which beat the previous autonomous laptime record.
Built an iOS app that allows people to have conversations with ads, using Sync.so's animation API and LLMs at BoilerMake, winning Sync.so's prize.
Hacked an RC car with a Raspberry Pi, camera, and LiDAR. Used imitation learning with CNNs to teach it to navigate around my house.
Developed a novel algorithm for probabilistic multi-agent path planning, which I presented at the 2024 American Control Conference.
Developed a method to improve the interpretability of autonomous driving systems, which I presented at the 2025 Purdue ICON student conference.
I'm an engineer focused on building systems combining machine learning and robotics. My work spans from turning RC cars into mini self-driving ones to building videos that you can talk with using video diffusion models.
Led design for an agentic LLM assistant for wargaming and analysis ($1.5M SBIR). Also ported astrodynamics and satellite control code from Python to Rust, achieving a 55x speedup.
Currently working on diffusion modeling for end-to-end control of autonomous vehicles. Previously presented my work on intepretable end-to-end models at the Purdue ICON student conference.
Researched probabilistic methods for collaborative control of robot swarms. Co-authored and presented "Line-of-Sight Visual Target Tracking via Particle-Based Belief Propagation" at the 2024 American Control Conference.
Designed and prototyped laser-cut and 3D-printed parts to alleviate a shortage of camera mounts, enabling $400,000 of inventory to be sold, and redesigned existing parts, reducing manufacturing costs by 80%.