About Me

I’m an applied AI scientist and behavioral systems modeler with a PhD in psychology and deep expertise in machine learning, causal inference, and computational linguistics. I specialize in building real-world predictive systems — the kind that work under ambiguity, involve human decisions, and demand interpretability.

My work spans full-stack ML development, experimental design, NLP, and human-in-the-loop evaluation. Whether I’m deploying multimodal models or simulating policy interventions, my focus is always the same: make complex systems understandable, adaptive, and aligned with real-world behavior.

I thrive at the intersection of disciplines — where human behavior meets data, where models meet decision-making, and where theory meets practice. I design tools and frameworks that not only perform well, but improve over time and across contexts.

I’m particularly passionate about fairness-aware modeling, safety in AI systems, and designing interpretable pipelines for high-stakes domains.

Core Expertise

Natural Language Processing (SLMs, LLMs, Transformers)
Applied Machine Learning & ML Engineering
Causal Inference & Experimental Design
Human-in-the-Loop Systems & Evaluation
Full-Stack ML Deployment (data → modeling → APIs)
Prompt Engineering, Fine-Tuning, & Model Alignment
Computational Social Science & Psychometrics
Decision Science & System Optimization

My Resume

How I Work