I'm a masters student at MIT studying robot intelligence. I graduated MIT in 2021 with a Bachelor of Science in Computer Engineering (6-2). I've worked on quantitative finance at Bridgewater Associates, deep learning acceleration at Intel PSG, and machine learning at MIT CSAIL and Cornell Tech.
I'm currently working on legged locomotion in the Improbable AI lab and robotic manipulation at Tutor Intelligence. I'm interested in problems in robotics, hardware acceleration, economics, and education.
Contact me via linkedin, github, or email me at jgru at mit.edu!
Otherwise, here's my resume.
Stats/ML: 6.438 Algorithms for Inference (grad), 6.890 Learning Augmented Algorithms (grad), 18.065 Matrix Methods In Data Analysis, Signal Processing, & Machine Learning, 14.32 Econometric Data Science, 14.33 Econ Research and Communication
Robotics: 6.881 Intelligent Robot Manipulation (grad), 6.832 Underactuated Robotics (grad), 6.302 Feedback System Design
Systems: 6.172 Performance Engineering of Software Systems, 6.881 Dynamic Computer Language Engineering, 6.905 Large-scale Symbolic Systems, 6.858 Computer Systems Security (grad), 6.111 Digital Systems Lab, 6.046 Advanced Algorithms
Battlecode: MIT's longest running programming competition. Led technical development of novel software infrastructure used by thousands of competitors as president from 2018-2019.
Atls.ml: a loss surface visualization tool for high-dimentional optimization processes. Final project for 18.065 Matrix Methods In Data Analysis, Signal Processing, & Machine Learning.
Lhyra: a framework designed to automatically find efficient recursive trees given arbitrary solvers, data distributions, and optimization criteria. Final project for 6.890 Learning Augemented Algorithms (grad).
Rave in a Box: live music segmentation and laser graphics on an FPGA. Final project for 6.111 Digital Systems Lab.
Cassandra: monte-carlo dispersion analysis in the browser using OpenRocket, for MIT Rocket Team.
OpenRec: modular Tensorflow-based library for recommendation systems. Early contributor (5k lines) while interning at Small Data Lab @ Cornell Tech.
Fido: few-shot reinforcement learning for simple robots with human-administered reward. Done in 2016 for Intel ISEF, won 2nd in category.
- 6.884 TA 2020: teaching assistant for "Computational Sensorimotor Learning" Spring 2021, a graduate robotics RL class.
- 6.08 LA 2020: lab assistant for "Introduction to EECS via Interconnected Embedded Systems" Spring 2020.
- HSSP 2020: co-taught "Brainy Bots," a robotics and probability lab in Python.
- Cascade 2019: co-taught "Dolla Dolla Bills," an introduction to finance and macro-economics.
- 6.009 LA 2019: lab assistant for "Fundamentals of Programming" Spring 2019.
- HSSP 2019: re-taught Inference and Optimization Systems, co-taught introductory nuclear engineering sampler.
- 6.S193 LA IAP19: lab assistant for FPGA digital design competition class IAP 2019.
- 6.147 Instructor 2018/19: ran and occasionally lectured for MIT Battlecode during 2018 and 2019 intersessions.
- Cascade 2018: re-taught Inference and Optimization Systems.
- HSSP 2018: co-taught "Learning to Code with Battlecode," an interactive introductory Python programming course where students manipulated virtual agents with code, and "Inference and Optimization Systems," a hand-on introduction to machine learning from an optimization perspective.