I am an aerospace engineering MS graduate from Georgia Tech and a former Machine Learning Engineer at Rolls-Royce. I build ML systems that are useful beyond a notebook: the data and evaluation have to be sound, the service has to be deployable, and failures have to be visible.

I entered Georgia Tech's Aerospace Engineering PhD program, transitioned to the MS, and completed the degree in August 2026. That path sharpened my focus on applied machine learning and engineering systems.

My strongest work is in retrieval and ranking, ML evaluation, scientific ML, and deployment-focused engineering. I am targeting Machine Learning Engineer, Applied ML Engineer, and Retrieval / Evaluation Engineer roles.

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Retrieval · RAG evaluation · Agentic workflows · ML systems

Built an evaluation-first technical knowledge system over 3,233 citation-preserving NASA report chunks. It combines hybrid retrieval, fusion, reranking, pgvector search, evidence checks, controlled citations, bounded agents, and containerized FastAPI services.

The external QASPER track evaluates a frozen within-paper TF-IDF baseline on 888 answerable questions with human-selected evidence. It reaches 76.24% any-evidence recall@10. QASPER covers NLP papers, so I treat this as retrieval evidence rather than NASA-domain or answer-generation validation.

Recommendation · Real-time search · Distributed systems

Built a leakage-aware news recommender with chronological evaluation, then added a separate real-time path so new articles could become searchable without tying event delivery to the model server. Go, Kafka, PostgreSQL, and FastAPI now handle keyed ingestion, idempotent writes, freshness-aware ranking, dead letters, and consumer recovery; the verified Docker run accepted all 500 events and reached a 79 ms sampled index-freshness p95.

Scientific ML · Uncertainty · On-device inference

Built a real-flight anomaly track on NASA DASHlink data alongside a separately labeled generated aircraft-design deployment benchmark. The recorded-flight model reached 0.7380 macro F1 on 17,780 aircraft-disjoint approaches and stayed blocked by its release gates; ONNX consistency remained above 99.55% under tested sensor corruptions.

• IntegrityBench — Civil Comments moderation candidate whose false acceptance rose to 59.32% on 2,802 human-annotated ToxicChat prompts; the failed transfer test keeps the release blocked.
• AeroSynth-Eval — public AGDD real-image transfer study plus 1,735 GenAI-Bench human preference votes for general image-evaluator development. Neither track is presented as operational aircraft-inspection evidence.
• Equity Backtest — expanding-window signal evaluation with real prices, transaction costs, a complete variant log, and an explicit survivorship-bias boundary.
• Atlanta Mobility Resilience Digital Twin — checksummed OpenStreetMap disruption simulation with 50 Census tract origins from 2024 ACS estimates, representing an estimated 216,659 residents. Destinations and observed traffic calibration remain open.
• Surrogate Model Learning — public UCI airfoil and building experiments with grouped splits, seed sensitivity, conformal coverage, and an extrapolation guard. A normalized conformal diagnostic improved building-load coverage to 87.93% and 87.07%, still below its 90% target and still awaiting confirmation on untouched data.

Graduate Research Assistant under Prof. Dimitri Mavris · May 2025 – Aug 2026

• Audited a rocket-motor simulation study that was failing without errors or output, built a leakage-safe classifier with 96.8% accuracy across 15,120 simulations, and traced the failures to an uncapped convergence loop.
• Worked on source-aware retrieval and evaluation for the Delta Air Lines-sponsored HERO safety program.
• Built surrogate, uncertainty, demand, and life-cycle models for GREEN TEA and Project EAGLE; the GREEN TEA model remains in sponsor use.

Machine Learning Engineer · Jul 2023 – Apr 2025

• Built Python workflows for diagnostics, anomaly detection, predictive maintenance, and mixed-frequency aircraft-engine time series.
• Turned certification requirements into reproducible analyses and model checks, then reviewed the findings with lifecycle engineers.

Data Science Intern · May 2021 – Jul 2021

• Cleaned aircraft-engine sensor data, designed features, compared predictive and anomaly-detection models, and summarized recurring failure patterns for engineering review.

Languages and data: Python, Go, SQL, C++, MATLAB, pandas, NumPy, SciPy, DuckDB, PostgreSQL/pgvector

ML and GenAI: PyTorch, scikit-learn, Hugging Face Transformers, PEFT/LoRA, LangGraph, RAG, BM25, dense retrieval, reranking, recommender systems, uncertainty estimation

ML systems: FastAPI, Kafka, Docker/Compose, Kubernetes, GitHub Actions, CI/CD, Prometheus, OpenTelemetry, ONNX, Core ML, Qualcomm QNN

Engineering practice: leakage-aware evaluation, protected test design, bootstrap comparison, failure analysis, physics checks, reproducible experiments