I'm a research assitant at UC Berkeley's Space Science Laboratories on a data management, processing, and visualization software for NASA's Magnetospheric Multiscale (MMS) mission.
I'm a pilot since 2017, and my passion for aviation fuels my interest in aviation technology that enhances flight safety.
I believe in furthering opportunities in tech to underserved students through my role as co-founder for Peer Rising AI Camp , a summer program for high schoolers to explore various aspects of computing.
I am currently rebuilding the official data visualization software used in the NASA Magnetospheric Multiscale mission to accomedate upcoming satellite formation changes in 2024. This software will use the PySPEDAS framework to process vectorized plasma data gathered by the MMS Satellites. We currently plan to integrate the new software into the MMS mission in early 2024.
My entrepreneurship endeavor started from UC Berkeley Haas School of Business's challenge lab. We are bringing tech education opportunities to underserved students in rural and small towns. Above is a picture from my cohort for Peer Rising AI Summer Camp in July 2023.
Gave a TED talk to students and families about rising trends in artificial intelligence, our perspectives as university students studying tech, and how students can thrive in a world with AI.
Built a flight tracker web application for planespotters (like myself). It obtains live flight information in AeroAPI and shows unique aircrafts for planespotters in their geographical region.
I have always been interested in teaching and pedagogy because of its power to foster a passion in computer science. I am a tutor for Berkeley's CS61C (Machine Structure) and a CSM Mentor for CS61B (Data Structures and Algorithms) . I teach weekly sections and pedagogy meetings, help students on assignments in Office Hours, prepare and host exam review sessions, and write questions for exams and worksheets.
I designed a fully functional CPU that executes the RISC-V instruction set, including the main datapath, register files, branch predictor, immediate generator, etc. The CPU has a 2-stage pipeline with instruction flushing.
Implemented a Feedforward Neural Network using to filter spam and phishing emails. Neural Net included 3 layers and 257 nodes to achieve 99.7% accuracy. Performed data cleaning, fine-tuned neural net architecture to achieve 99.7% accuracy.