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 robotic systems and prediction models that operate under real-world constraints. 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, building hardware, or iterating on side projects that scratch a curiosity itch.
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.
Projects
UFO Project
Building a centrifugal system that offsets linear acceleration with spin: a 2D RL policy torques an inner vessel during short ~5-10g outer pulses. Current demos still show multi-g on the inner pod mid-pulse; episode-mean inner load is lower because most of the run is quiet.
6-Axis Arm
Adaptive inverse kinematics that keeps learning through hardware deformities using a Hebbian control policy, so everyday IK can tolerate looser (cheaper) mechanical tolerances instead of perfect calibration forever.
Deep Q-Network for Autonomous Lunar Landing
Engineered a PyTorch D3QN agent to solve a 2D landing environment with >95% success, then extended into a custom 3D rigid-body MuJoCo simulation with accurate 1.62 m/s² lunar gravity. Upgraded to DDPG for a 13-dimensional continuous state space with a hybrid PD-RL attitude controller mapping actions across 5 thrusters.
Kalshi Optimization
Autonomous sports-contract scanner that fuses Elo, bookmaker odds, and team stats into a fair-value model, then sizes edges with Kelly under statistical-mechanics risk dampers and Telegram alerts.
Project Erwin
AI secretary that schedules, reminds, and commands from your Obsidian vault, speaking as Commander Erwin via ElevenLabs-trained weights deployed on Piper TTS.
Spike Detector
Pre-market binary classifier for significant intraday moves: two-stage XGBoost with FinBERT sentiment and a stat-mech layer, fully automated paper trading via Alpaca and Telegram.
Community
RiseEd · Accessible STEM Learning
Built RiseEd to make STEM approachable for younger students, especially those from low-income backgrounds. Designed a website of simple, engaging lesson slides and booklets, iterating on feedback from teachers to turn intimidating topics into resources students and educators could actually use.
CoolBreeze · Low-Cost Air Conditioner
Designed and built a low-cost air conditioner grounded in thermodynamics principles, optimizing insulation and efficiency on a tight budget, then documented the full build in a video to make the science reproducible for anyone who wanted to follow along.
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.