About
I'm an aerospace engineering student in MIT's Department of Aeronautics and Astronautics (Course 16), driven by a fascination with what lies beyond our atmosphere. My work sits at the intersection of autonomous systems, machine learning, and spacecraft design.
I've always been the kind of person who dives headfirst into challenges just to see what happens. In high school, I built a giant ninja star out of 256 sheets of paper for my Geometry class — starting from 1, then 4, 16, 64, 256 — only stopping because my teacher ran out of unused packets. I taught myself AP Calc AB and BC in two weeks just because Precalc wasn't challenging enough. I finished every AP World History chapter over Thanksgiving break on a whim. The worst that can happen is I fail, and learn something new in the process.
That same mindset drives my work today. I'm passionate about building autonomous machines that can perceive and navigate complex environments — from lunar landers that teach themselves to touch down safely, to satellites that detect their own anomalies. Beyond autonomy, I'm deeply interested in the propulsion systems that will take us further: designing future spacecraft capable of covering vast interstellar distances in short times, potentially through nuclear propulsion technologies.
Right now, I'm a research assistant in MIT's AeroAstro department, working on flight software for satellite systems and optimizing orbital event detection with neural networks on embedded hardware. When I'm not in the lab, I'm usually training reinforcement learning agents to land on the Moon or engineering AR/VR systems for next-gen helmets.
Experience
Research Assistant · MIT AeroAstro
Utilized the F Prime framework to compile and execute flight software on the RP2350 chip for satellite applications. Currently conducting a comparative analysis between CNNs and algorithmic methods on NVIDIA Jetson Nano to optimize orbital event detection under strict power and memory constraints.
Independent Developer · Curious Cardinals
Developed a predictive model for NVIDIA stock by scraping the top 100 daily news articles over a 30-day period and using VADER's compound normalization to quantify market sentiment for regression analysis. Engineered a validation pipeline mapping historical price action to VADER sentiment indices.
Projects
Education
Massachusetts Institute of Technology
Candidate for B.S. in Aerospace Engineering (Course 16).
Relevant Coursework: Introduction to Autonomy
Advanced Learning (Audit): Deep Learning (Graduate Level), Advances in Computer Vision (Graduate Level)
The Coding School
Completed two-semester courses in Artificial Intelligence and Quantum Computing, developing neural network, NLP, and reinforcement learning models for real-world applications. Implemented foundational quantum algorithms using the Cirq library.