I am a computer science and applied mathematics undergrad at UMass Amherst. My interest lies in leveraging machine learning and mathematical modeling to solve complex problems. Currently, I am a research fellow under the theoretical computer science group, exploring elimination order's impacts on the efficiency of tree decomposition algorithms. Outside academics, I recently interned at Coda Payments, where I developed several functionalities of the company's deployment system (CDS).
Education
University of Massachusetts Amherst
Bachelor's of Science double majoring in Computer Science and Applied Mathematics
- GPA: 4.0
- Current Coursework: Computer Systems Principles, Web Programming, Artificial Intelligence, Introduction to Machine Learning Leadership Practicum, Directed Research Group,
- Past CS Coursework: Object Oriented Programming, Data Structures, Programming Methodology, Reasoning Under Uncertainty, Introduction to Computation, Introduction to Research in the Discipline
- Other Past Coursework: Math Puzzle, Calculus II, Multivariate Calculus, Introduction to Linear Algebra, Physics I
Skills
- Languages
- Developer Tools
- Data Science / Machine Learning
- Others
Experience
Research Fellow | Early Research Scholar Program (ERSP) | Sep 2024 - Present
- Exploring how different elimination orders impact the efficiency of tree decomposition algorithms and influence properties such as a bag size of the resulting tree.
Infrastructure Engineering Intern | Coda Payments | May 2024 - Aug 2024
- Designed an alert system for invalid configurations in ECS/Codedeploy deployment in the company-wide Deployment System (CDS), enhancing deployment reliability and troubleshooting capability.
- Delivered linear time complexity solution in validating the workflow definitions (similar to AWS Step Functions), improving deployment efficiency.
- Resolved a bug of incorrect deployment timestamps by integrating the Boto3 library with a MySQL database, ensuring accurate deployment tracking.
- Executed a feature in the Coda Deployment Tracker to export deployment details as a CSV file through a user-friendly datepicker, streamlining data analysis and reporting processes.
Data Science Intern | TISCO Financial Group | Sep 2022 - Oct 2022
- Collaborated with the Digital Banking team to predict second-hand car prices using a Scikit-Learn linear model.
- Proficiently retrieved web-scraped data from Google Big Query using SQL language, removed outlier values, and selected features such as a car’s make and model and manufactured year for model training.
- Designed an intuitive web application that allows users to make a prediction with the Streamlit framework.
- Completed daily exercise on code refactoring and Test-Driven Development principles in Python.
Project
Backtestr
- Developed a versatile back-testing platform for personalized investment strategies, allowing users to customize technical indicators, formulate complex buy/sell signals, and visualize portfolio performance over time.
Caesar Cipher Encoder-Decoder
- Customized a user-centric Caesar Cipher encryption/decryption web application with adjustable keys, space removal, and string reversal features to minimize the possibilities of brute-force decryption.
- Leveraged Google’s 1-gram corpus and spaCy for accurate text tokenization and Named Entity Recognition, enhancing the precision and formatting of decrypted messages.
NASA Space App Challenge
- Forecasted solar Kp index with TensorFlow Long Short-Term Memory neural network utilizing data from Deep Space Climate Observatory (DSCOVR).
- Nominated for global round judging.
StarClusterPlot
- Published Python library on PyPI that helps generate a colorized star cluster’s Hertzsprung-Russell diagram and scatter plot visualization of the star cluster using data from the Sloan Digital Sky Survey (SDSS).
- Developed python function that help convert star’s red and green magnitude to color in RGB using star’s magnitude transformation, Planckian locus approximation, and color conversion algorithms.