I'm a Computer Science & Data Science student at Purdue University. Check out my projects and feel free to reach out!
Contact MeInterested in the fields of Machine Learning and Data Science, I enjoy exploring how data can be leveraged to drive insights and decision-making. My passion lies in developing intelligent systems that can learn from data and improve over time.
Driven by a passion for robotics, I focus on building intelligent, reliable systems that bridge the gap between code and the physical world. I enjoy developing motion pipelines, working with real-time control, and integrating simulation with hardware to solve complex, interdisciplinary problems.
I am a versatile full stack developer with a solid foundation in both front-end and back-end technologies. I have worked on various projects using modern frameworks like React.
Java
C
C++
JavaScript
Python
TypeScript
TensorFlow
OpenCV
Mediapipe
React
Angular
HTML
CSS
SQL
R
May 2025 - Present
January 2025 - Present
June 2024 - August 2024
This website was made to display my skills, projects, and contact information. Designed to work responsively.
Built a robotic cello bowing system with MIDI-driven UR5e control and reinforcement learning in Purdue’s AI for Music group.
Custom K-means clustering algorithm created from scratch to analyze the performance of NFL receivers. Goal was to identify clusters of receivers with similar performance metrics.
Created a computer vision system, incorporating perspective transformation to map the chessboard and track the positions of 32 robotic chess pieces. Implemented algorithms for real-time object detection and tracking, enhancing the accuracy and responsiveness of the system.
Designed for the FIRST Robotics Competition game, Rapid React. Harnesses algorithms and data analysis to predict team rankings. Tool integrates data from The Blue Alliance REST API to provide accurate ranking predictions.
This AI-driven trading bot uses sentiment analysis of financial news to make automated trading decisions. It aims to optimize investment returns through machine learning and real-time data.
Project developed to recognize specific hand gestures to raise and decrease a user's system volume.