About
I’m an M.S. candidate in Psychology: Data Science in Human Behavior at UW–Madison (expected Dec 2026), with a B.S. in Psychology (neuroscience concentration, sociology minor) from UW–Parkside.
I build AI systems from start to finish: multi-agent document pipelines, transformer fine-tuning and calibration, LLM evaluation, computer vision classification, and behavioral prediction models. Before graduate school I spent five years in high-volume logistics and operations, working on quality assurance, documentation, and process improvement. That background is why I care about error rates, reproducibility, and cost as much as accuracy.
Interests
- Document AI: reading, sorting, and extracting information from real-world paperwork. See the document-intelligence projects.
- Evaluation and calibration: held-out testing, confidence that means something, and honest error analysis.
- Mechanistic interpretability: testing what model interventions actually change, with matched controls. See Neuron Atlas.
- Simulation and graphics: physics-based, interactive software in the browser. See Wave Laboratory.
- Behavior and cognition: what predicts how people act, and where models of people go wrong.
Technical Skills
- Languages & Data
-
Python (pandas, NumPy, scikit-learn, PyTorch, Hugging Face, PyArrow), R, MATLAB, SQL, Parquet, Hive, JavaScript/TypeScript, Git
- Machine Learning
-
transformer fine-tuning, neural networks, regularization & penalized models (Lasso/Ridge), ensemble methods (random forests, bagging), resampling methods, model evaluation & calibration, cross-validation, SHAP/feature importance, error analysis, computer vision
- AI Engineering
-
LangGraph (LangChain), multi-agent pipelines, LLM eval harnesses (incl. bias & robustness evals), Prompt Engineering, model serving (vLLM, Ollama, LM Studio, OpenRouter), ONNX + FastAPI deployment on Modal
- Engineering & Cloud Services
-
Docker, gRPC, Apache Spark, PySpark, HDFS, shell scripting (bash), pytest, Quarto, CHTC (high-throughput computing), AWS, Azure, GCP
Education
University of Wisconsin–Madison Madison, WI August 2025 – December 2026
M.S. Candidate – Psychology: Data Science in Human Behavior · GPA: 3.85
- Graduate program in which roughly 95% of coursework is data science and data engineering — building on an undergraduate foundation in neuroscience research, experimental design, and statistical analysis.
- Advanced training in machine learning (neural networks, regularized and ensemble models, transformer fine-tuning and LLM evaluation), statistical modeling, and large-scale data systems (Spark, gRPC services, HDFS, Parquet, SQL, Docker).
- Industry capstone with American Family Insurance (Digital-Mailroom).
University of Wisconsin–Parkside Kenosha, WI September 2020 – May 2025
B.S., Psychology
Concentration: Neuroscience. GPA: 3.93
- Coursework spanning cognitive and computational neuroscience, psychological theory, clinical psychology, psychopharmacology, psychometrics, psychological statistics, and early data science preparation — plus philosophy, neuroethics, and pre-law.
- Minor in Sociology; Provost’s List (7x), Dean’s List (8x), Distinguished Achievement Award (2025), and Psychological Research Certification (300 lab hours).
Research Training
- CITI Program: Conflicts of Interest training 2025
- CITI Program: IRB Training for Students and Faculty 2025
View the full résumé