About

I am a third-year Electrical Engineering and Computer Science student at the University of California, Berkeley, originally from Palo Alto, CA. Driven by a passion for technology and impactful problem-solving, I am interested in pursuing a path in software engineering and exploring its potential to drive innovation and foster positive change across diverse fields and communities. My experiences span professional software engineering, academic research, and collaborative projects, where I’ve built scalable, user-focused solutions and tackled complex technical challenges. This summer, I am excited to take the next step in my professional journey and join Amazon as a Software Development Engineer Intern in their Sunnyvale office. As I navigate my last semesters here at UC Berkeley as well as my minor in Data Science, I am especially interested in pursuing artificial intelligence and machine learning to learn how to best leverage data to drive insightful decision-making and develop innovative, perceptive solutions to complex and relevant problems. Thank you so much for stopping by, please feel free to reach out and/or connect with me at one of the links to the left. Talk soon!

Education

University of California, Berkeley • 2022-26

Electrical Engineering and Computer Science (BS)

Data Science (Minor)

Experience

Software Engineer Intern

Helped migrate codebase from React Native to Swift to develop a native iOS application to significantly enhance performance and efficiency and optimize app functionality and user experience. Assisted in the preliminary development of Glocal’s AI model using data from pilot programs in London and Chicago to offer deliverable insight and analysis on community incentives to boost civic engagement. Conducted and submitted rigorous, biweekly code reviews to maintain Glocal’s quality standards, objectives, and best practices, worked on bug fixes and performance enhancements to ensure a seamless user experience, and documented technical processes and developments for internal use and knowledge sharing.

Research Software Engineer

Leveraged Python/Jupyter Notebooks to solve and/or model complex physical and mathematical equations and concepts including aerodynamics (drag, lift, Bernoulli’s), propulsion (thrust, power), control (stability, inertia, basic control theory), structural analysis (stress, strain, buckling), and energy (consumption, efficiency). Presented research to offer comprehensive analysis and insight to help facilitate the preliminary development of the physical systems.

Research Assistant

Worked under Dr. Waqas Khalid on a joint research product with UCSF building nanotechnology-based portable blood sampling devices. Contributed to the development of the team’s supervised machine learning model (PyTorch) trained on historical blood sample data to predict potential health conditions and recommend future diagnostic tests from the results of their devices.

Projects

SIXT33N: Built a voice-controlled model car by integrating machine learning techniques (PCA, SVD) with C++ programming to process four voice commands; implemented system on a microcontroller, utilizing analog circuits and custom PCBs for hardware control, engineered op-amp and BJT circuits for closed-loop control logic, and audio signal amplification and filtering circuits to process the voice commands.

ANN Network: Wrote RISC-V assembly code to execute a simplified artificial neural network (ANN) on the Venus RISC-V simulator; built and integrated fundamental operations such as vector dot products, matrix-matrix multiplications, argmax computations, and activation functions to load and execute a pre-trained network to classify handwritten digits from the MNIST benchmark set.

Drive: Designed and built a secure file sharing system (based on Google Drive) in Go with rootKey-based authentication, cascading access revocation for dynamic permission updates, integrity validation, and delegated access control using encryption keys and file location pointers; supported user creation/deletion and file saving, editing, loading, and sharing/revocation.

CPU: Built a fully functional, 32-bit CPU with a pipelined RISC-V datapath in Logisim capable of running machine code converted from RISC-V assembly code; supports efficient instruction processing and adheres to the RISC-V ISA specifications.

NGram: Replicated Google’s Ngram Viewer in Java using HashMaps, Collections, Iterators, and the Princeton Algorithms Library for reading, parsing, and converting text datasets to track history of word usage, enabling query handling and scalability for linguistic data processing.

WordNet: Built a visual and numerical model of hyponym and hypernym correlations and tracked word tree lineage in Java, using Directed Acyclic Graphs, HashSets, and ArrayLists, as well as algorithms including depth-first search graph traversal and weighted quick union.

2D World Generation: Designed and implemented the game functionality, UI, and data storage of a 2D tile-based, user-interactive world exploration engine in Java that uses pseudo-random algorithms to generate custom worlds and gameplay.

Scheme: Implemented the core features for a lisp interpreter in Python using a recursive descent parser, utilizing lexical and syntactic analysis as well as input parsing.

Arcade Games: Built fully rendered replications of the classic puzzle games 2048 and Tetris in Java that incorporate user data storage for scores, responsive user controls through keyboard inputs (WASD, UDLR), and various Java data structures and algorithms for game logic.

Website: Built using HTML and CSS, hosted on GitHub Pages. View the code here.

Skills

Languages: Python, Java, C/C++, Go, SQL, RISC-V, Swift, HTML, JavaScript, CSS

Developer Tools: Git, Google Cloud, VS Code, IntelliJ, Xcode, Figma, SwiftUI

Libraries: NumPy, Matplotlib, pandas, React, React Native