Aaron Huang Email & Phone Number

Foster City, California, US

Various projects working on reinforcement/imitation/value learning and multimodal foundation models

Pittsburgh, PA, US

- Researched deep reinforcement learning and imitation learning methods for self-driving cars in simulation- Created a failure analysis and visualization toolbox for autonomous driving performance on the CARLA Leaderboard benchmark- Developed an autonomous driving stack on a small-scale MIT RACECAR testbed that comes with LiDAR, stereo cameras, and a.

Foster City, California, US

Used machine learning to predict interaction graphs for planning and decision-making in autonomous vehicles

San Francisco, California (CA), US

- Used PyTorch to design and train convolutional neural networks for real-time drivable path detection and object detection. Deployed models on the production self-driving stack as ROS nodes- Designed a CNN training procedure that jointly trained drivable path and object detection models with a semi-supervised composite loss function. Explored RNN models.

Cambridge, MA, US

- Studied robotic navigation through structured, unknown environments using novelty detection and deep learning to identify situations where machine learning models should not be used- Implemented a state-of-the-art autoencoder approach for novelty detection in TensorFlow and trained the model to identify input images that are outside of the training.

Cambridge, MA, US

- Developed a heuristic-based method in CommonLisp for fast multi-vehicle coordination with direct application to a proposed NASA lander and undersea explorer mission to Europa- Invented Risk-Aware Decentralized Model Predictive Control (RADMPC) to rapidly approximate the brute-force solution and decouple the computationally expensive problem into.

Manassas, VA, US

- Researched deep learning for aerial hazard detection, aircraft tracking, and operator awareness systems- Prototyped CNN solutions for internal trade studies and testing with deployment hardware including the Intel Movidius Neural Compute Stick (NCS) and an Nvidia GTX 1080 GPU- Used the Keras and TensorFlow frameworks with transfer learning and data.

Manassas, VA, US

- Designed a system for optimal task allocation on the Traveling Salesman Problem as foundation for optimal task allocation for teams of cooperative UAVs- Implemented a branch and bound algorithm on a decision tree representation of the Traveling Salesman Problem using a greedy search as the initial solution algorithm- Abstracted objectives into subtask.

Pasadena, CA, US

- Developed a high-fidelity, closed-loop hardware simulation for the Landing Vision System (LVS) used by NASA’s Mars 2020 rover mission for computer vision guided Entry, Descent, and Landing (EDL)- Designed models for LVS hardware peripherals that emulate the hardware down to register access, bus control, and peripheral interface levels- Integrated.

Cambridge, MA, US

- Assisted in testing and development of control algorithms for computer vision aided autonomous navigation and docking of the SPHERES satellites aboard the ISS- Transcribed live ISS test sessions, brainstormed improvements to existing localization algorithms, assisted glass table testing- Automated testing and analysis of a closed-loop MATLAB simulation.

Santa Cruz, CA, US

- Worked with a PhD candidate on his work detecting extragalactic outflow around galaxies- Created a Python script to filter and co-add light spectra data from a data pool of >2000 galaxies drawn from the DEEP2 survey- Retrieved the first generalizable instance of extragalactic outflow on a statistically significant set of data- Coauthored a research paper.

Bachelor Of Science - Bs, Aerospace Engineering And Autonomous Systems

Master Of Science - Ms, Robotics

High School Diploma