Independent Research @ Northwestern University. Winter 2022 → Present
Studied benefits of multi-modal prompting. Explored using the noise predictions as the message in graph neural networks, both in image generation and molecular conformation domains. Designed and implemented modifications on gaussian splatting.
[molecular conformation research ideas]
Undergraduate Researcher @ Northwestern Assistive & Rehabilitation Robotics Laboratory. Fall → Winter 2022
Built tools in ROS to enable consistent and reliable control of 100+ experiments. Developed control code to sequence and synchronize up to 10 experiment phases with a robotic arm.
Co-founder, Backend Developer, Graphics Researcher @ Licon AI. Fall → Winter 2022
Designed novel 3D object reconstruction and view synthesis techniques using graph neural networks and differentiable rendering. Reimplemented and customized NeRF models including Plenoxels and InstantNGP with CUDA for accelerated training and inference. Built a Golang-based backend with gRPC for multiuser live document editing/management. Deployed solutions using Docker and Kubernetes. Integrated text-to-image and interactive video segmentation models for generative video background replacement.
Team Lead @ RoboCup@Home Education Competition. Summer 2021
Designed a vision-based system for autonomously carrying luggage and user-following tasks using pose estimation, object detection, and patch-based image similarity. Implemented with TensorFlow and PyTorch, optimized for open environments using Coral TPU accelerators and TensorRT. Led the team to the international finals.
Co-founder, Backend Developer @ 3D-printed PPE Distribution Non-profit. Summer → Winter 2020
Built infrastructure within three weeks to match nationwide PPE needs during COVID-19. Facilitated the distribution of 26,030 pieces of PPE by designing deployment architecture and contact/mailing systems. Hosted on Google Cloud Compute Engine using Gunicorn, NGINX, and Django.
Machine Learning Research Intern @ UCSD San Diego Supercomputer Center (SDSC). Summer 2019, 2020
Developed machine learning models to classify support tickets using Decision Trees, Naive Bayes, and SVMs. Improved classification accuracy by 15% over the existing solution using domain-specific data. Implemented models in base Python libraries and Scikit-learn.