Mathematics Ph.D. (Syracuse University) working as an applied scientist. My background is in pure math — homological algebra, free resolutions, Betti numbers — and I now build and validate machine learning systems, with a focus on generative modeling and large-scale model optimization.

I care about the unglamorous half of ML: not just getting a model to train, but knowing whether you can trust what it tells you. A lot of my work is about calibration, uncertainty, and rigorous evaluation.

• Generative & vision-language models — fine-tuning LLMs/SLMs and VLMs (PaliGemma, Gemma family) with parameter-efficient methods (QLoRA/PEFT, 4-bit quantization) under tight compute budgets.
• Trustworthy ML — calibration (ECE), uncertainty quantification (Monte Carlo Dropout), robustness and distribution-shift testing, confound auditing.
• Predictive modeling — forecasting and classification with leakage-free, time-aware evaluation.

Python (advanced) · SQL · R · C++ (building proficiency) · PyTorch · Hugging Face · XGBoost / LightGBM · NumPy / Pandas / SciPy / Scikit-learn · Git · LaTeX

I spent years as the sole instructor of record for 400+ students, which shaped how I work: I try to take messy quantitative problems all the way to clear, actionable conclusions, and explain them to technical and non-technical audiences alike. I'm also a published researcher (Journal of Pure and Applied Algebra, 2026) — so I'm comfortable owning a problem end-to-end, from open question to validated result.

• I enjoy applying mathematics to real-world scenarios, like using sports data to teach statistics
• Fast learner who thrives in challenging environments
• Passionate about fostering inclusive learning environments in mathematics
• Enjoy participating in community service activities

Check out my recent projects and publications for more details about my work!