Conducted research on PFAS distribution across California.
Utilized machine learning techniques to model PFAS behavior over time.
Developed algorithms in Python to analyze and predict PFAS patterns.
Collaborated with a multidisciplinary team to interpret data and draw conclusions.
Presented findings in academic settings and contributed to research publications.
Collected CSV data and converted into tiff file and raster data
Projects
PFAS Contamination Prediction (Python, Scikit-learn, Pandas): predictive model to assess PFAS contamination in water sources across California.
Student Course Management System (React, Node.js, MongoDB): A web application for students to register for classes, track progress, and manage schedules.
AI Tic-Tac-Toe (Python, Minimax Algorithm)
Vulnerability Scanner (Python):a simple security scanner to detect common vulnerabilities in web applications.
Project in Algorithms and Data Structures: Zipzip tree