CS grad student and builder, working across cryptography, GPU/HPC, applied machine learning, and security.
I'm a recent CS graduate now finishing my master's degree, with a focus on cybersecurity, hardware-accelerated cryptography, and applied machine learning. My interests span intrusion detection, red teaming, GPU and hardware acceleration, classical and deep ML on tabular data, and full-stack development, particularly where low-level thinking and real-world impact intersect.
Outside of academics, I've been deeply involved in the computing community on campus. I founded and served as the first president of the largest computing club at my university, and chaired the ACM chapter at Hofstra, an organization with over 50 years of history. These roles shaped how I think about collaboration, leadership, and building things that last.
I recently completed my master's thesis on hardware acceleration for Zero Knowledge Proof systems under Dr. Xiang Fu, comparing multi-scalar multiplication performance across CPU, consumer GPU, and a multi-GPU HPC cluster. The final implementation reached a 17.9x end-to-end speedup over the CPU baseline on eight NVIDIA A100s, and produced empirical guidance on which hardware tier to use at which input size.
Alongside the thesis, I've been deepening my applied machine learning practice through graduate coursework, building end-to-end pipelines for tabular classification, regression, and reinforcement learning. Recent projects include a Random Forest and ANN sweep predicting Formula 1 race outcomes on the Ergast dataset, PyTorch MLPs for TMDB box-office regression and Titanic survival, and tabular Q-learning vs SARSA on Gymnasium's LunarLander.
I also serve as a Senior Student IT Technician at the Maurice A. Deane School of Law, where I've led projects to upgrade and deploy systems that improve campus security and operational efficiency. Alongside that, I tutor and TA for computer science courses, helping students build intuition for concepts that don't always come easy.