M.S. Data Science and Engineering, City College of New York (GPA 3.97) · B.A. Computer Science and Data Science, NYU (GPA 3.96, summa cum laude)
I build applied computer vision and machine learning systems. My thesis, developed in collaboration with Nearabl, Inc., is a hybrid pipeline combining template matching and vision-language models to detect electrical symbols in MEP engineering drawings — achieving 95% precision and 76% recall on real-world data without any model training or fine-tuning. The thesis will be made public after a one-year embargo lifts in May 2027.
Outside of ML, I lead web development at the Science Mentorship Institute, a nonprofit connecting students with mentors to learn how to do college-level research. I manage a volunteer engineering team, oversee account registration and mentor-mentee matching, and am currently leading a migration from legacy PHP to a modern MERN stack. I also have broader full-stack experience with React and Node.js.