I am a Research-Driven Engineer specializing in the intersection of Complex Systems and Agentic AI. My work focuses on building intelligent systems that don't just process data, but understand the underlying networks and dynamics within it.
- Agentic Orchestration: Developed
Elegance, a Python SDK for RFT-optimized workflows and cloud-native AI agents. - Knowledge Discovery: Architected an AI-powered discovery platform for UVM Medical Center, using hierarchical clustering to identify 20+ underrepresented research themes.
- Scalable Analytics: Optimized data infrastructure at Openlane, migrating complex logic to Snowflake/DBT, resulting in $700k+ in annual savings.
- Statistical Forecasting: Engineered end-to-end Bayesian pipelines for international shipping firms to predict auction pricing and bidder win likelihoods.
- 🎓 M.S. Complex Systems & Data Science | University of Vermont (NSF Full Scholarship, 4.0 GPA)
- 🎓 M.S.E. Software Engineering | Arizona State University (Incoming Jan 2026)
- 🔬 Research Focus: Semi-supervised classification, scaling LLM inference on HPC clusters (LLaMA 3.2), and multivariate Hawkes processes.
- Languages: Python (NumPy, PyTorch, LangChain), JavaScript/TypeScript (Next.js, D3), Go, Julia, Rust.
- AI/ML: Agentic Orchestration, Bayesian Inference, Graph ML, LLM Fine-tuning, NLP.
- Infrastructure: Snowflake, DBT, Neo4j, QDrant, AWS/Azure/GCP, Docker, Terraform.
- StoryKit — Plotly Dash dashboard for NLP tasks including topic modeling, sentiment analysis, and RAG.
- BetterBetter — Go-based CLI for scraping sports stats and training Bayesian models for arbitrage detection.