My Experience
A timeline of my professional experiences.
Machine Learning Engineer
Morph Labs (Internship)
May 2025 – Present
- Developing a brain-computer interface that translates EEG signals into prosthetic controls instructions.
- Experimenting with Mamba State Space Models, Graph NNs, Deep Q-networks, and more.
- Ultimately, building small, accurate models for on-device deployment.
PyTorch
Signal Processing
Mobile Development
AWS Sagemaker
Our prototype prosthetic arm in action.
Researcher
Duke Cyber-Physical Systems Lab
Oct. 2024 – Present
- Improving policy estimation for Robot Reinforcement Learning under Dr. Pajic and Jiwoo Kim (on track for an ICLR and an ICRA publication)
- Building a diffusion model based on AVDiT to generate both a policy architecture and its corresponding weights
- Generating training data (RL policies) using HyperPPO and evaluating policies in ManiSkill and Brax simulations
Reinforcement Learning
Diffusion Models
Robotic Simulations (ManiSkill, Brax)
PyTorch
A video demo of a generated policy doing the PushT task in Maniskill.
Machine Learning Summer Research Intern
Duke University, CS+
May 2024 – Aug. 2024
- Worked with Dr. Xiaowei Yang to develop a self-updating database of internet PoP topology
- Built image segmentation model with 91% Mean IoU, using LoFTR for transformer-based feature extraction
- Automated map extraction from ISP websites using Puppeteer and a classifier
- Generated 100,000 synthetic map images on Duke's Computing Cluster
Computer Vision
Transformer-based Models (LoFTR)
Web Scraping
PyTorch
Slurm
Research Poster I presented in Summer 24