Accelerating multi-GPU distributed training with NVSHMEM-optimized fine-grained communication.

Currently making 4D Gaussian Splatting faster by leveraging VLMs, with the Interactive 3D Vision & Learning Lab. Previously, developed an adaptive AR labeling tool in Unity C# and built software enabling navigation through 20+ realistic testing environments for a user study [paper].

• Graduate Deep Learning (Fall 2025) – (Going to) help students figure out transformers, autoencoders, diffusion, and more!
• Computer Graphics (Fall 2024) – Helped students trace rays and fight OpenGL, for the longest running computer graphics course in the known universe.
• Computer Vision (Spring 2024) – Helped students understand concepts from traditional computer vision to modern deep architectures.

A fast, action-conditioned next-frame generator for Minecraft, inspired by Oasis and the MAR framework for image generation. Designed for model-based control, it enables ~4× faster sampling than full-frame diffusion methods.

An educational cryptocurrency inspired by Bitcoin, featuring proof-of-work mining, transaction verification, and fork resolution. It simplifies blockchain mechanics using global balance tracking instead of UTXOs, and runs over a custom virtual OS and peer-to-peer network for decentralized coordination.

A from-scratch stable diffusion model for conditionally generating street view images from a corresponding satellite image. Given a satellite image, the model is able to generate a plausible looking view from the ground.

A simulation modeling the transport networks generated by Physarum (a slime mold). Implemented with Unity and powered by a compute shader, capable of individually updating the dynamics of 1 million+ interacting slime agents in real time.

A better Word Hunt! Developed with React and playable here! Click tiles to select them and type to place a letter. After creating your desired shape, press randomize to switch things up and drag together adjacent letters to form words. Longer words = more points!

A set of models for predicting country and coordinates from a given street view image. Scraped 50,000+ images from Europe using Selenium + Google StreetView API, and separately fine-tuned ConvNeXt and BiT for country and real-valued coordinate predicitions. Achieved 61% country classification accuracy (seems low, but you try it with a single street view image!) and 60% accuracy on coordinate prediction within 500km.