I am a third-year student at the University of California, San Diego, pursuing my Bachelor of Science in Computer Science. My research interests focus on employing adversarial/deceptive planning and strategic obfuscation within reinforcement learning agents. I specialize in harnessing big data, recommender systems, and natural language processing to transform products and software with machine learning.

I am actively pursuing internship opportunities in Machine Learning and Software Engineering, eager to showcase my skills and gain insights into industry-standard practices. Down the line, my goal is to transition into a Product Management role, aligning with my deep-seated interest in advancing data-driven technology and leveraging my innate abilities in external communication and leadership.

Selected Experience

Research

I was previously an Undergraduate Computer Graphics Researcher as a part of the ERSP program at UCSD Computer Science and Engineering Department under the advisory of Prof. Ravi Ramamoorthi and Prof. Tzu-Mao Li from the Center for Visual Computing and Differentiable Rendering Research Group. I lead a team of four in evaluating Slang, a new differentiable shading language, through efficiency and performance analysis, in collaboration with Nvidia.

Here is the research proposal: Image View Synthesis and Differentiable Rendering in Slang
Here is the research poster: Evaluating Slang for View Synthesis

Another research project I worked on was Assessing the limitations of QAGNN (Question Answering using Language Models and Knowledge Graphs) in which I proposed the implementation of a Natural Language Inference model to verify that the model’s predicted answer would follow the logic of a question, as well as addressing ambiguous priority of question-answer groups by re-ranking answers with an MRR-based (Mean Reciprocal Rate) Neural Network. The papers and codebase can be found in this repository: QA-GNN Analysis Study.

Professional Experience

As a Software Engineer Intern at GymBuddy in Fall 2023, I led a dynamic SWE team in collaboration with the UC Berkeley UX Club to enhance existing web applications. I strategically managed project workflow with JIRA and ensured feature quality through continuous integration and deployment (CI/CD) pipelines with GitHub Actions. Notably, I prototyped an Advertiser Dashboard web application using the MERN stack featuring frontend with React.js, AWS Amplify, and Cognito for secure authentication, while also deploying the backend infrastructure with Google Firebase, Express.js, and Node.js to ensure a seamless user interface and facilitate data queries for advertising partners.

As a Software Engineer at Endeverus in Fall 2022, I specialized in developing baseline APIs and backend systems using the Django framework in Python, focusing on integrating core functionalities and ensuring seamless communication between user event handlers and data queries for web applications. Furthermore, I integrated EC2 Servers with cloud-based data through AWS DynamoDB within automated systems, optimizing data flow and enhancing the efficiency of client applications. In addition, I pitched into product-centric decisions such as designing the user interface, employing quality assurance with CI/CD pipelines, as well as managing the user experience of client components.

Projects & Initatives

In team projects, I frequently assume leadership roles, honing my technical prowess and managerial abilities. A notable example is Codelog, a user-centric developer journal aimed at illuminating and sharing the intricate journey of passion projects. In this initiative, I led a software engineering team of ten students, emphasizing agile methodologies to manage sprints using JIRA, implementing CI/CD pipelines for code maintenance, and conducting full-stack development with HTML, CSS, and JavaScript. In addition, I spearheaded a team of CSE students in the development of machine learning models (DNN, SVM, Log Reg) to predict loan repayment behavior, Predictive Modeling for Loan Repayment Optimization, achieving 94% accuracy on testing data.

In a more individual passion project, I crafted a KNN Forecast Machine Learning TradingView Indicator and Algorithm. Here, I created a K-Nearest Neighbors regression model to forecast future price points, yielding a remarkable 2461.42% return on TSLA and 767.49% return on NVDA. Additionally, I enhanced a third-party script using Python, Skopt, and Selenium with a new feature to refine parameter selection, contributing to open-source trading optimization: TradingView Machine Learning Scripts

Throughout the past, I’ve consistently demonstrated a keen focus on driving innovation and growth within various ventures. As the Webmaster of the Administrative team at Virtual Tutoring SF, I founded and spearheaded the development of the organization’s Computer Science branch, overseeing the creation of web applications and implementing Chatbot APIs to streamline communication between our staff and tutee customers. Additionally, my role as Co-Founder of Blossom Supply involved managing the development of web applications and a mobile app, while leading cross-functional teams to conceive and execute innovative e-commerce platforms.

Other Interests and Qualifications

Currently, I’m on the path to obtaining my California Real Estate Salesperson license, driven by my commitment to thrive in our family business. Outside of real estate, I hold certification as a USATF Official and have a background as a sprinter in club-college athletics. Additionally, I’ve contributed to my high school alma mater as a Defensive Backs and Linebackers coach. My interests also extend to music, where I’ve earned an Associate of the Royal Schools of Music Piano Diploma and have actively collaborated with producers as an audio engineer.