01
Machine Learning & Model Evaluation
Classifier ambiguity, calibration, threshold-dependent behavior, finite-horizon learning-curve prediction, conservative extrapolation, experiment tracking, and reproducible benchmarks.
Physics PhD · Data Scientist · Research Scientist
Scientific computing meets applied AI.
I build machine-learning and document-intelligence systems with a research scientist's habit of asking: What can fail, how do we know, and how do we measure it? My work connects physics, high-dimensional modeling, reliable evaluation, and practical AI tools.
Focus
Three areas, one thread: careful modeling.
My background moves from physics and numerical simulation into applied machine learning. The connecting idea is the same: build models, test their limits, and explain when they should be trusted.
02
Document Intelligence & RAG
PDF parsing, structured extraction, native text-layer workflows, retrieval-augmented question answering, hallucination reduction, and evaluation of extraction quality.
03
Scientific Computing & Physics
Quantum materials, noncollinear magnons, molecular and defect-mediated transport, numerical simulation, high-dimensional analysis, and high-performance computing.
Selected work
Projects with a research spine.
These projects are designed to be more than portfolio demos. They test reliability, expose failure modes, and connect mathematical ideas to usable AI systems.
Ambiguity Range Framework
A diagnostic toolkit for evaluating binary classifiers beyond a single threshold, with ambiguity-focused metrics, calibration analysis, confidence intervals, and statistical testing.
View projectSequence Acceleration Benchmark
A reproducible benchmark for finite-horizon gradient-boosting learning-curve prediction across many convergence regimes, noise levels, and extrapolation methods.
View projectPharmaDoc-AI
A pharmaceutical document-intelligence and RAG project that combines PDF extraction, structured information retrieval, document question answering, and evaluation workflows.
View projectLumberLex
A lumber product-name normalization engine that maps inconsistent seller strings to canonical structured outputs using fuzzy matching, dimension extraction, treatment detection, confidence scoring, a Streamlit interface, and chatbot-style explanations.
View projectHeart Disease Prediction Pipeline
An end-to-end MLOps pipeline with DVC, MLflow, pytest, GitHub Actions, model-performance checks, and drift monitoring with Evidently.
View projectResearch
Physics roots, machine-learning branches.
My research background spans quantum materials, crystal-field theory, noncollinear magnons, molecular and defect-mediated transport, dark matter phenomenology, and modern ML diagnostics.
Publications
- Under review — Finite-Horizon Learning-Curve Prediction for Gradient Boosting: Regime Dependence, Failure Detection, and Conservative Extrapolation Rules.
- Under review — The Ambiguity Range Framework: A Diagnostic Toolkit for Operational Evaluation of Binary Classifiers.
- Nano Letters, 2025 — A modular approach to solid-state integration of zero-dimensional quantum systems.
- Physical Review B, 2025 — Crystal fields, exchange and dipolar interactions, and noncollinear magnons of erbium oxide.
Research interests
Scientific ML
Classifier ambiguity
Learning curves
Calibration
Document intelligence
RAG
High-dimensional modeling
HPC
Numerical simulation
Experience
A bridge from research to applied AI.
My path combines academic research, teaching, scientific software, and project-based machine-learning training.
Assistant Professor of Physics · Creighton University
Teach undergraduate physics, develop optics and electronics lab materials, supervise teaching assistants, and integrate AI tools into scientific workflows.
AI & Machine Learning Training · TripleTen
Completed project-based training across supervised learning, unsupervised learning, deep learning, NLP, computer vision, SQL, Git, and end-to-end ML workflows.
AI-Powered Document Intelligence Externship · Pfizer / Extern
Built retrieval-augmented and document-intelligence workflows for healthcare documents using native PDF extraction, LLMs, and RAG architectures.
Graduate Research Assistant · University of Iowa
Developed high-dimensional numerical models, optimized scientific simulations, analyzed noisy data, and contributed to peer-reviewed work in condensed matter physics and quantum materials.
Graduate Research Assistant · Creighton University
Built simulation-based models using Python and Fortran, including N-body simulation, molecular dynamics, and parallelized scientific computing workflows.
Tools
Technical stack.
Machine Learning
Scikit-Learn, XGBoost, PyTorch, TensorFlow, Transformers, calibration, model evaluation, NLP, RAG, MLflow.
Engineering
Python, SQL, FastAPI, Pydantic, Docker, Linux/Bash, Git, GitHub Actions, Jenkins, CI/CD, pytest.
Scientific Computing
NumPy, Pandas, Polars, Numba, Fortran, HPC, MPI, numerical optimization, Monte Carlo simulation.
Document Intelligence
PDF parsing, information extraction, OCR-aware workflows, structured extraction, evaluation of extraction quality.