Data scientist building retrieval, classification, and LLM evaluation systems. I pair production ownership with rigorous experimentation, model validation, and research on robustness and efficiency.
Built an AWS-based agentic oncology intelligence system that let researchers query emerging drug-disease-target relationships extracted from scientific conference abstracts, achieving 87% accuracy on the MIRAGE benchmark, 7.6 points above baseline.
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Designed ontology-driven normalization using HGNC and DOID to standardize biomedical entities and identifiers across literature sources.
Built a Neo4j knowledge graph with approximately 18,000 entities and 95,000 evidence-weighted relationships, each linked to canonical identifiers, source text, and provenance.
Integrated eight retrieval tools into a multi-agent LangGraph workflow with task-aware routing between Gemini Flash and DeepSeek-R1, so answers returned auditable evidence paths through planning, graph traversal, validation, and synthesis.
Jul 2023 - Jul 2024
Incedo Inc.
Data Scientist
Productionized a hybrid retrieval stack with BM25, dense retrieval, and cross-encoder reranking across 1,200+ technical manuals for a telecom router company. Top-3 exact match rose from 41% to 84%, answer F1 from 68% to 92%, and p95 latency fell from 2.3 seconds to 900 milliseconds.
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Developed backend components for a customer-facing document-intelligence pipeline that converted scanned PDFs, forms, and tables into schema-validated structured records, achieving 95% exact field accuracy across 5,000 labeled fields from 800 documents and reducing manual data-entry and correction time by approximately 60%.
Fine-tuned open-weight LLMs for medical and telecom named-entity recognition, improving micro-F1 from 0.75 zero-shot to 0.90 on a 2,500-example in-domain test set.
Measured out-of-domain decay to 0.61 F1 and used the analysis to define when fine-tuning outperformed prompt-based extraction.
Deployed document QA and multimodal RAG using FAISS on Azure ML with evaluation guardrails for relevance, citation support, factual consistency, completeness, and jailbreak behavior. The suite reached 87% precision and 82% recall, routing 12% of outputs to human review.
Jan 2023 - Jun 2023
Kinara AI
Software Engineering Intern
Prototyped a RISC-V vector extension and LLVM backend with custom lowering for vectorized memory and scatter-gather operations, accelerating ML-kernel execution in cycle-accurate simulation.
Feb 2026 - May 2026
Living Brands AI
Data Analyst Intern
Built the evaluation backend for a platform that lets brand-strategy teams benchmark prominence across six LLMs and more than 10,000 weekly prompts, tracking first-mention rank, share of voice, sentiment, and citation coverage.
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Developed the statistical methodology for the platform's cross-brand comparisons, so it reports one brand as surfacing above another only when the difference across sampled prompts is statistically significant rather than sampling noise.
Processed approximately 1 million responses through an embedding-retrieval and two-stage classifier, reaching approximately 93% intent accuracy and routing ambiguous mentions to human review.
Delivered React and TypeScript dashboards for weekly client reporting, cutting recurring cross-model analysis from approximately two days to under two hours.
Selected work
Robustness & Evaluation of Refusal in Open-Weight LLMs
Developed a training-time defense and evaluation harness for an open-weight LLM, then stress-tested whether refusal behavior could be removed through low-rank linear ablations.
Built a fully local physical-therapy coach that combines Qwen2.5-VL form feedback, MediaPipe repetition tracking, range-of-motion analysis, and spoken guidance in the browser.
Input-Only Suppression of Evaluation-Awareness Latents
Consistently suppressed targeted activations, including fully turning off one causally validated feature, but found that readable activations do not always control behavior.
