• Selective program and paid position to TA Stanford CS classes• Responsible for teaching weekly discussion sections, grading student assignments, and holding interactive grading sessions for 8-12 students per quarter• Taught students programming concepts in Python and C++, including object-oriented programming, data structures, recursive backtracking, and memory management• Holding office hours for students in CS 106A: Programming Methodology and CS 106B: Programming Abstractions at Stanford

• Performing research on multi-object 3D object tracking using JackRabbot data at SVL.The Stanford Vision and Learning Lab (SVL) at Stanford is directed by Professors Fei-Fei Li, Juan Carlos Niebles, Silvio Savarese and Jiajun Wu. We are tackling fundamental open problems in computer vision research and are intrigued by visual functionalities that give rise to semantically meaningful interpretations of the visual world.Our research addresses the theoretical foundations and practical applications of computational vision. We are focused on discovering and proposing the fundamental principles, algorithms and implementations for solving high-level visual perception and cognition problems involving computational geometry, automated image and video analysis, and visual reasoning. At the same time, our curiosity leads us to study the underlying neural mechanisms that enable the human visual system to perform high level visual tasks with amazing speed and efficiency.http://svl.stanford.edu/projects/jackrabbot/

Designed, implemented, and tested a new framework for the Google Local Services Ads experiment pipeline to replace the existing one•Decreased pipeline runtime by over 75%•Decreased file read operations by over 75%•Decreased file write operations by over 40%Designed, implemented, and tested a second framework to run experiments and select the ones with the best results

•Developed end-to-end in-browser Lottie animation player and editor based on Google’s Skia Graphics Library, used for editing ad templates that allows for image uploads and customized text•Wrote software to generate AI enhancements, including color palettes based on user uploaded images and placing text to avoid covering important parts of uploaded images •Developed using TypeScript, LitElement, Node.js, and Tensorflow.jsFrom conducting fundamental research to influencing product development, Google AI research teams impact technology used by billions of people every day. We make tools and datasets available to the broader research community with the goal of building a more collaborative ecosystem.

• Developed end-to-end Urban Air Mobility (UAM) system risk analysis software for NASA’s UAM project. The UAM project is developing a safe and efficient air transportation system for mission-critical taxi and cargo drones traveling within and between cities• Finished front-end development using HTML/CSS and Electron• Finished back-end development using Python and NodeJS• Wrote Python scripts to run a Monte-Carlo simulation and interpret results• This risk analysis software is used to simulate flight paths between cities so the user can determine the best flight paths based on potential costs of crashes.NASA's vision: reach for new heights and reveal the unknown for the benefit of humankind.NASA's mission: drive advances in science, technology, aeronautics, and space exploration to enhance knowledge, education, innovation, economic vitality and stewardship of Earth.