Home

I am contributing to the OpenFAST software development on the computer science side, focusing on enhancing simulation capabilities for offshore wind energy systems. My role involves optimizing and integrating computational models to improve the performance of wind turbines in dynamic ocean environments. By working with complex codebases and simulations, I’m helping drive innovations in renewable energy through advanced software solutions.

As a course assistant for both graduate (CS-GY 6923) and undergraduate (CS-UY 4563) Machine Learning courses, I assist in preparing course materials, grading assignments, and providing support to students during office hours. I collaborate with professors to ensure students grasp key concepts in machine learning, deep learning, and algorithmic problem-solving. Additionally, I help facilitate discussions, explain complex topics, and guide students through hands-on coding exercises and projects.

As a research assistant, I developed a review-based group recommender system using deep learning techniques, including CNN and attention mechanisms. My research aimed to improve recommendation accuracy by leveraging user reviews, focusing on group decision-making dynamics. This work involved extensive experimentation with neural networks, data preprocessing, and model optimization, contributing to advancements in personalized recommendation systems.