AI/ML engineer and computational biologist building trustworthy AI systems for biomedical research—from AI agents and foundation models to rigorous evaluation and production-ready deployment.
An auditable agent workflow for hereditary-cancer germline variant research that combines HGVS normalization, ClinVar, PubMed/PMC, gnomAD, gene-level context, optional TCGA cohort analysis, grounded synthesis, and human-review handoff.
- Separates evidence for the exact variant, broader gene context, and optional somatic cohort context to prevent scope overclaiming.
- Evaluates citation validity, scientific faithfulness, latency, cache behavior, token use, estimated cost, and recurring failure categories.
- Uses deterministic validation, an independent Check Agent, and a persistent human-review queue rather than treating model output as scientific truth.
- Delivered with a browser workbench, batch/history/export tools, 81 automated tests, 32/32 intake evaluation cases, and passing GitHub Actions CI.
An end-to-end protein language-model project spanning biological and classical baselines, frozen embeddings, multi-seed fine-tuning, homology-filtered external evaluation, subtype failure analysis, long-protein inference, and a tested FastAPI service.
- Fine-tuned ESM-2 150M achieved 0.936 validation F1 and 0.982 ROC-AUC across three seeds.
- External evaluation identified peripheral membrane proteins as the main failure mode.
- Subtype-aware modeling improved peripheral-protein recall from 0.325 to 0.549.
- Delivered with FastAPI, Docker, GitHub Actions, and 40 automated tests.
A patient- and sample-aware comparison of conventional Scanpy/PCA and frozen scGPT representations across 29,614 NSCLC cells from 10 metastatic lymph-node samples.
- scGPT improved broad-cell sample-held-out kNN accuracy from 0.832 to 0.923.
- The evaluation also found stronger platform separation and weaker performance for some fine-grained CD8 T-cell states.
- Sample-level analysis compared cell composition, functional programs, and scGPT pooling without presenting the small cohort as a validated clinical predictor.
- Bioinformatics: single-cell RNA-seq, microbial and viral genomics, metagenomics, genome assembly, comparative genomics, sequencing QC
- AI and machine learning: PyTorch, Hugging Face Transformers, scGPT, ESM-2, LLM agents, LangGraph, grounded synthesis, evaluation and tracing, model benchmarking, error analysis
- Scientific software: Python, R, Bash, Linux/HPC, Git, automated testing, GitHub Actions, scientific APIs, FastAPI, Docker
I am focused on scientific AI and computational biology roles where biological domain knowledge, reproducible analysis, and rigorous evaluation are essential to building trustworthy systems.