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About

I'm an AI leader with a scientist's training, working across research, product, engineering, data science, and platforms. I have 22 years of engineering experience, 18+ of them building and leading production AI/ML platforms, and 9+ years at Director/VP level, growing organizations from zero to 25+ people, including senior managers.

I currently lead AI infrastructure at Autonomous Solutions, Inc. (ASI): hybrid compute, a company-wide data platform with data science on top, and the pipelines that take autonomy research to product (current role). Earlier I led AI at GiftHealth, Mayo Clinic, Syngenta, AgBiome, and Firstleaf. Across those roles the work has spanned consumer AI, biotech, agriculture, and clinical medicine. Since 2006 I've also been building Loom, an independent healthcare AI platform.

Background

I trained in quantitative cell biology. For my PhD at Thomas Jefferson University I identified a novel cell-division checkpoint by combining biochemistry, live-cell imaging, and computational analysis. As a postdoc at UNC's Lineberger Comprehensive Cancer Center (American Heart Association fellow), I built statistical and ML models for precision oncology. From there I moved into industry. I built recommendation systems for millions of users, then knowledge graphs and digital twins of biological, clinical, and business systems, then the organizations that run them.

Education

  • PhD, Biochemistry & Molecular Biology (Computational Biology), Thomas Jefferson University
  • BS, Biochemistry & Molecular Biology, Pennsylvania State University, Eberly College of Science
  • Additional: Stanford AI (Thrun & Norvig inaugural online course, 2011); leadership training; Crucial Conversations

What drives the work

Building AI platforms that model complex systems at scale, so organizations can predict, optimize, and change outcomes.

The common thread is the digital twin: a continuously updated model that brings many data streams together around one entity. That entity has been a consumer, a microbe, a crop trait, or a patient. My strength is shortening the path from research to value. I take a new method, make it work reliably in production, and build the team that keeps it running.

Skills & focus areas

Leadership

9+ yrs Director/VP 0-to-scale org building Distributed teams of 25+ $M+ budgets AI strategy & governance Executive communication

LLMs & agentic AI

Fine-tuning / PEFT RAG & GraphRAG LangChain / LangGraph MCP vLLM / TGI Prompt & context engineering

Knowledge graphs & ontologies

Neo4j Amazon Neptune Graph ML (PyTorch Geometric) Adaptive ontologies Vector DBs Digital twins

Modeling

PyTorch TensorFlow scikit-learn Recommendation & ranking Computer vision NLP

Experimentation & causal inference

A/B testing Multi-armed bandits Causal inference Bayesian optimization Self-serve experimentation

Responsible AI

SHAP · LIME · Captum Fairlearn · AI Fairness 360 Bias mitigation Model validation Audit trails & governance

Compute & infrastructure

Hybrid / neocloud compute GPU training (DDP/FSDP) Slurm Spark · Ray · Dask Kubernetes · Docker Terraform

Data platforms & MLOps

Databricks · Delta Lake Azure Data Explorer Snowflake · dbt Kafka · Flink MLflow · W&B · Airflow Azure · AWS · GCP

Healthcare & regulated environments

HIPAA / PHI FDA · GxP-aligned workflows EHR / FHIR SNOMED CT · ICD-10 RxNorm · LOINC Genomics / omics

Track record

  • 100× user growth at Firstleaf, from thousands to 300K+ members with >95% satisfaction, plus +40% customer lifetime value and +25% retention
  • Enterprise agentic AI platform for 70,000+ users at Mayo Clinic, taken from idea to production
  • 98%+ prediction accuracy in production recommender systems serving millions of recommendations a day at sub-100 ms latency
  • Loom knowledge graph of 2.3M+ patients, with 2.3B+ nodes and 4.8B+ relationships
  • 5 patents (list)
  • Peer-reviewed publications including Nature Communications (list)
  • Fast Company Most Innovative Companies, Data Science (2022), and an American Heart Association Postdoctoral Fellowship (2015–2017)
  • Media interviews and keynotes as a subject-matter expert in AI and data science

How I lead

  1. Start from the outcome. Turn an ambitious technical vision into a roadmap with clear business results.
  2. Build the organization. Grow diverse, distributed teams from zero and give them real ownership.
  3. Make production the default. Research counts when it ships and stays reliable.
  4. Work across functions. Keep engineering, science, product, and leadership on the same page.
  5. Measure it. Judge success by impact and ROI, not activity.

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