I am a student at UC Berkeley focused on the intersection between technology and health. My main personal initiative is to use software and hardware together to improve wellness for users of technology.
● Designed real-time, front-end components in React, connecting to the backend using GraphQL subscriptions
● Reduced synchronization latency of ServiceNow’s Agent Workspace platforms by 300% using Java APIs and JavaScript
● Created a customizable state management tool in JavaScript using directed graphs as a side project
● Reduced platform synchronization errors by 100% by implementing an algorithm to detect cycles in graph data structures
● Wrote functional tests for everything I built with over 90% code coverage, using JUnit (Java) and Jest (JavaScript)
● Reduced robot processing load 60% by building a Python Flask API for robots to recognize and react to facial expressions
● Developed object recognition features for robots with a 95% success rate using Google Cloud Vision APIs and ROS
● Implemented Neural Style Transfer in Python to process images in real-time for the robot’s “dreaming” feature
● Manipulated and connected servos to control and test robot movement using Arduino and C++
● Wrote two blog articles about 3D Printing and Neural Style Transfer
Focusing on the intersection between hardware systems, software, and health.
Fitmoh is an organizational health application available on web and mobile devices. It has been a pet project of mine since July 2017, started when I had no coding experience.
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Developed an unofficial API for MyFitnessPal (MFP) that allows users access their MFP data programmatically. I implemented a persistent cache using Python pickling for MFP food database scraping, improving performance by 200%. I also allowed foods to be stored in a CSV format, which allowed MFP foods to be used for various use cases. Credits to coddingtonbear for the initial codebase.
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This chatbot currently uses the Nutritionix API and Google Cloud Vision API to parse requests and send back appropriate responses.
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Built an iOS app for users to label data and earn money from companies that need labeled machine learning data. This is currently a WIP, so it's not currently public on Github or the App Store yet. It's built using Swift and Firebase.