I am a student at the University of Maryland pursuing a degree in Computer Science, with a minor in Statistics.
My interests include full stack web development, data science, artificial intelligence, automation, and more.
I have internship experience at companies like Morgan Stanley, Capgemini Government Solutions, and Bloomberg.
I am always eager to learn new things and am interested in any potential job opportunities! Please feel free to reach out about any opportunities or if you want to learn more about me.
Skills
Python
JavaScript
TypeScript
Java
Haskell
OCaml
C
C#
SQL
HTML
CSS
React
Angular
Express
Flask
Django
Linux
Git
Docker
Node.js
Android
Unity
UiPath
Grafana
Jira
Education
Honors: Summa Cum Laude (GPA: 4.0/4.0); Dean's List; College Park Scholars: Environment, Technology, & Economy Citation
Honor Societies: National Honor Society; Computer Science, Math, Business, Science, and Latin honor societies
Awards: National Merit Scholar, 2020 NASA Conrad Innovator, AP Scholar with Distinction, High Honor Roll
Experience
Teaching assistant for CMSC131: Object-Oriented Programming I, an introductory computer science course taught using Java
Conducted weekly office hours, providing guidance to students on class material and project-related questions
Graded quizzes, exams, and projects for style and accuracy, offering constructive feedback to enhance students’ understanding
Enabled individuals within Lending Products & Services and other teams to quickly draw meaningful insights from data by developing a versatile machine learning platform for model building, evaluation, and prediction
Developed user-friendly web application using React (TypeScript) for the frontend and Flask/Db2 (Python) for the backend
Supported complex pipelines with preprocessing and feature selection steps as well as hyperparameter tuning via grid search
Led the development of a tool for classifying and extracting information from financial documents using OpenAI’s GPT-4 model during Morgan Stanley’s Generative AI Hackathon
Improved the efficiency of the Software as a Service (SaaS) capability team by leveraging robotic process automation (RPA) to automate the provisioning of users in Salesforce using data exported weekly from an HR system
Reduced processing time by over 80% and enhanced data accuracy by developing a UiPath attended bot
Presented findings on viability of virtual reality to top executives, following hands-on testing and extensive market research
Contributed to the Fleet Automation Services team by developing several features for a web application used to manage machine turnaround (maintenance operations) for a fleet of over 30,000 servers
Provided operators with the ability to set different retry actions per host when rerunning failed jobs, using Angular (TypeScript) for the frontend and Django/PostgreSQL (Python) for the backend
Created reproducible development/build environments for the Angular frontend by containerizing it using Docker
Enhanced data visibility by publishing metrics for maintenance events, actions, and failures to a telemetry system via the Django backend and creating a filterable Grafana dashboard displaying time series graphs of these metrics
Assisted the Environment Support Site Reliability Engineering team by providing visibility into data on a fleet of over 30,000 servers, which the team automates machine turnaround (maintenance operations) for
Developed an interactive web application to visualize metrics such as runtime, failure rates, and failure reasons at the host, schedule, and cluster levels, using React (TypeScript) for the frontend and Django/PostgreSQL (Python) for the backend
Projects
A few of my projects are shown below. To see more, check out my GitHub profile.
Created and deployed a responsive, mobile-friendly portfolio website showcasing my skills, education, experience, and projects
Built the site using React/Next.js (TypeScript), implementing a component-based approach for easy updates via edits to JSON data
Created an AI-powered restaurant search chatbot that you can chat with using natural language to search for restaurants, ask questions, summarize reviews, and more
Built the site using React/Next.js (TypeScript) for the frontend/backend, Google's Gemini 1.5 Flash for the model, the Google Maps Platform Places API for restaurant information, and Redis for saving chat history
Analyzed the factors influencing teams’ playoff success using Python, going through each step of the data science pipeline
Accurately predicted the number of playoff games a given team would win by leveraging regular season data to train 8 different machine learning models, with the best (a decision tree) achieving a mean deviation of less than 2.5 games
Performed an exhaustive grid search with 5-fold cross-validation to find the optimal parameters for each model