Data Scientist and AI Engineer working at the intersection of biomedical research and production ML. I fine-tune single-cell foundation models, build spatial transcriptomics pipelines, and turn complex signals into measurable, interpretable outcomes across omics, clinical AI, NLP, and LLMs.
Precision on a supervised ML fraud detection system processing 25K+ payslips at EY.
In operational inefficiencies surfaced via PySpark/SQL EDA across 5+ Saint-Gobain systems.
Reduction in manual reporting effort via automated ML pipelines at Media Sales Plus.
Unstructured financial records extracted via NLP and OCR pipelines to automate risk insights.
Classification accuracy on a Vision Transformer fine-tuned on the Cats vs Dogs dataset.
Faster leadership decision cycles from interactive Streamlit and Tableau dashboards.
I work across the full stack of biomedical and applied AI - from single-cell data and spatial omics through model deployment and LLM-powered insight layers.
Co-registers Visium and Xenium spatial transcriptomics with H&E image analysis, deploying scSimilarity, CellViT, and Path2Space on HPC clusters to link tissue morphology with gene expression for spatially resolved biomarker discovery.
End-to-end pipeline converting markerless sit-to-stand motion data into an interpretable ordinal falls-risk score for older adults, with biomechanical feature engineering, subject-independent validation, SHAP explainability, and a fairness audit.
Retrieval-augmented assistant for querying biomedical papers in natural language, built with Hugging Face embeddings, a vector store for semantic search, and LangChain orchestration returning grounded, citation-linked answers.
NLP pipeline for prompt generation and summarization using Python, TensorFlow, and Hugging Face. Increased dataset diversity by 30% and annotation precision to 90.2%, earning an honorable mention from the jury.
Retrieval-augmented GRU model summarizing chest X-ray reports via TF-IDF chunk retrieval on the OpenI dataset. Deployed as an interactive Streamlit app for clinicians with image-linked outputs.
Forecasting system across multiple product lines combining ARIMA and LSTM for 12-month revenue projections with 95% confidence intervals, packaged into Streamlit and Tableau dashboards that cut decision cycles by 30%.
Seeking full-time roles in Data Science, ML Engineering, and Biomedical AI. Drawn to teams building intelligent systems that bridge research and real-world impact. Open to relocation across the United States.
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