Data scientist and M.S. Data Science candidate at Rochester Institute of Technology.
I build ML systems the way I build a PC: every component should have a clear job, fit the rest of the system, and remain testable after the machine turns on. My current work focuses on clinical risk, NLP, statistical evaluation, probability calibration, and model drift.
| Project | What I built | Verified result | Demo |
|---|---|---|---|
| Clinical Readmission Risk | Leakage-safe binary 30-day readmission pipeline with patient-disjoint cohorts, calibration, and drift audits | 0.661 ROC-AUC, 0.175 PR-AUC, 0.077 Brier on 10,822 held-out encounters | Open app |
| NewsSnap: Transformer News Classification | DistilBERT training and evaluation, FastAPI inference, a transparent Streamlit demo, and tested request handling | 0.870 accuracy, 0.869 macro-F1, 0.827 MCC on 12,000 held-out articles | Open app |
| DriftLab | Deterministic simulator for performance decay, class-prior shift, PSI, approximate KS, and alerts | Mixed shift: ROC-AUC 0.758 → 0.372, max PSI 0.608, three alerts | Open app |
The public NewsSnap API currently uses a clearly labeled demo-inference mode. The trained DistilBERT evaluation above comes from the saved project artifacts; I do not present the hosted heuristic as transformer inference.
- Graduate Researcher, Data Science at RIT: analyzed 351,501 Reddit and YouTube comments across 16 women-safety cases and evaluated Qwen3.5-9B with LoRA on 3,000 held-out examples. Research dashboard
- Author of a paper accepted at ASONAM 2026.
- GCCIS Technical Assistant at RIT: support 200+ Windows and Linux lab systems and maintain Excel/VBA reports.
- Research Assistant at RIT: refactored Pandas/NumPy scripts and validated PostgreSQL and Excel data for weekly reporting.
Languages
Python · SQL · R · Java
Machine learning and NLP
PyTorch · scikit-learn · CatBoost · XGBoost · Hugging Face Transformers · LoRA · DistilBERT · FT-Transformer
Statistics and evaluation
A/B testing · Mann–Whitney U · chi-square · G-test · ROC-AUC · PR-AUC · probability calibration · PSI / KS / JS
Data and ML engineering
Pandas · NumPy · SciPy · Spark · PostgreSQL · FastAPI · Streamlit · React · Docker · GitHub Actions · automated testing
I am finishing my M.S. in Data Science at RIT, with expected graduation in December 2026. I am interested in data science and ML engineering roles where careful evaluation matters as much as model training.