Awarded the Undergraduate Teaching & Research Award (UTRA) to collaborate on advancing 3D computer vision research under Professor Srinath Sridhar. Assembled and utilized a 90-camera system to capture detailed spatial and temporal data, leveraging Semantic Gaussians with the goal of enabling text-to-scene edits.

Serving as a teaching assistant for CSCI 1230: Computer Graphics in the Fall 2024 semester. Currently involved in course development, as well as creating an interactive OpenGL state diagram visualizer to help students debug their code more effectively.

Collaborate cross-functionally with developers and designers to create tailored applications and websites for Brown's community, most notably the Conversational AI Lab's web platform. Designed and deployed a robust content management system for blog posts using Firebase, streamlining content creation and management processes. Planning and leading upcoming workshops focused on modern full stack development using frameworks and tools like React, Redux, Next.js, Tailwind CSS, and Firebase.

Contributed to the development of an adaptive augmented reality labeling tool, enhancing skills in AR development and image analysis. Developed and deployed a Unity-based AR software suite for navigating over 20 realistic testing environments. Conducted a user study with 18 participants to enable real-time interaction and evaluation of AR labeling models. See paper here.

Served as a teaching assistant for CSCI 1430: Computer Vision in the Spring 2024 semester. Host weekly office hours to support students with coursework, clarifying complex concepts and methods in visual computing. Collaborate with course staff to develop and update course materials, including lectures, project guidelines, and grading tools.

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.