Passionate about technology, development, and innovation. Interested in software engineering, API development, data science, big data, natural language processing, and deep learning.

Computer Science Classes

Upper Division:

CS164 - Programming Languages and Compilers - Fall 2020 (Compilers Project)

CS170 - Efficient Algorithms and Intractable Problems - Spring 2019

CSW186 - Introduction to Database Systems - Spring 2019 (Database Project)

CS161 - Computer Security - Spring 2019 (Encrypted File Sharing System Project)

CS168 - Introduction to the Internet: Architecture and Protocols - Spring 2020 (Network Routing Project)

CS195 - Social Implications of Computer Technology - Spring 2019

CS197 - Field Study (Academic Intern) - Spring 2020

Lower Division:

CS61A - Structure and Interpretation of Computer Programs - Spring 2017 (Yelp Maps Project, Scheme Interpreter Project)

CS61B - Data Structures - Fall 2017 (Gitlet Project)

CS61C - Great Ideas in Computer Architecture (Machine Structures) - Fall 2018

CS70 - Discrete Mathematics and Probability Theory - Spring 2018

EE16A - Designing Information Devices and Systems I - Spring 2018

Math 54 - Linear Algebra and Differential Equations - Fall 2017

High School:

CS Capstone Project - Senior Year

AP Computer Science - Junior Year (HiVolts Project)

Advanced Java - Sophomore Year

Intro to Java - Freshman Year

Data Science Classes

Upper Division:

CSW182 - Designing, Visualizing, and Understanding Deep Neural Networks - Spring 2020 (Robust and Generalizable Computer Vision Classification Paper)

Info 159 - Natural Language Processing - Fall 2018 (Seq2Seq Chatbot Project)

CS188 - Introduction to Artificial Intelligence - Fall 2018

Data 100 - Principles and Techniques of Data Science - Fall 2018 (Taxi Rides Project)

Geog C188 - Geographic Information Science - Fall 2020

Data 104 - Human Contexts and Ethics of Data - Spring 2020

Lower Division:

Data 8 - The Foundations of Data Science - Spring 2017

CivEng 88B - Time Series Analysis - Spring 2017

Robust and Generalizable Computer Vision Classification

May 14, 2020

Abstract:

We created a robust computer vision classifier that performs well in a dataset that contains perturbations. To achieve this, we used various data augmentation and other deep learning model techniques, such as model ensembling, image denoising, adversarial training, and attention networks. These methods helped improve the robustness against both naturally perturbed and adversarial datasets. In addition, we created class action map visualizations for our models to help understand how the model makes its classification decisions. Through these techniques, we achieved a 71.1% Top 1 Accuracy and 90.0% Top 5 Accuracy on the Tiny-ImageNet classification challenge.

Links:

Paper, GitHub Repo, Academia, ResearchGate

Compilers Project:

Worked with a project group to build a compiler for the ChocoPy language. Completed lexing & parsing, semantic analysis, and code generation parts.

Link:

More Info

Network Routing Project:

Implemented distributed routing (for example distance-vector routing) & transport algorithms.

Link:

More Info

Seq2Seq Chatbot Project:

Built a chatbot using an encoder-decoder model for the final project in Info 159. Utilized Python and Keras.

Link:

More Info

More projects listed here: Project Showcase

TMP All-Star Award Winner - June 2023

Compilers Tournament Award (Team29) - Dec 2020

Dean's Honors List - Jan 2017

National AP Scholar Award - May 2016

National Merit Finalist - January 2016

Languages:

Frameworks:

Software Engineering, Big Data, NLP, Microservices, Backend Engineering, Data Science, Deep Learning, Cloud Computing

Cal Alumni, Academia, ResearchGate, Wellfound, TikTok, Threads, Kaggle