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Previous Positions

Executive Leadership Journey

With 22 years of engineering experience spanning startups to Fortune 500 companies, my career progression demonstrates consistent advancement from technical leadership to executive roles. Over 18+ years building and leading AI/ML platforms, I've grown from hands-on technical contributions to directing globally distributed teams of 25+ members including senior management across 10 countries, with full P&L responsibility for multi-million dollar programs. This journey culminated in executive leadership delivering 100x growth achievements and building complete AI organizations from zero to scale across diverse industries—from cancer research to consumer AI, biotechnology, agriculture, and clinical healthcare.

Mayo Clinic - Multiple Leadership Roles

Rochester, MN | 2024-2025

Creator and Team Leader - Agentic Knowledge Engine

As leader of a highly skilled team, I developed a cutting-edge, robust data platform engineered for multi-modal data that integrates seamlessly with diverse databases and data lakes, facilitating real-time ingestion into sophisticated knowledge graphs.

Key Objectives

Our core mission was to transform complex, disparate data into actionable insights, driving advancements across various domains within Mayo Clinic's healthcare ecosystem.

Major Achievements

Proprietary Algorithm Development We achieved significant breakthroughs in developing proprietary algorithms and sophisticated workflows that adeptly manage multi-modal data, extracting value from both structured and unstructured sources.

Knowledge Graph Innovation A cornerstone of our work lay in the creation, structuring, and iterative refinement of knowledge graphs, which are directly informed and enhanced by the outputs of our advanced AI-driven data processing.

Intelligent Agent Systems Our team successfully engineered and deployed intelligent agents and powerful toolchains to streamline data retrieval for complex question-answering flows. We also developed methodologies for neighborhood and shortest path summarization.

Advanced Agentic Capabilities Developed a robust execution graph for advanced agentic capabilities that:

  • Revolutionized operations and decision-making
  • Optimized patient care workflows
  • Accelerated scientific research processes
  • Enabled AI agents to perform complex, multi-step tasks
  • Drove significant improvements in effectiveness and outcomes

Technical Lead and Lead Solution Architect

Transformation Hub - Rochester, MN

Strategic Leadership

  • Led technical development of Transformation Hub products and core infrastructure assets
  • Partnered with product management to realize needs through solutioning and execution
  • Served as technical liaison between Transformation Hub and other Mayo teams

Architecture & Vision

Designed and executed the overarching Transformation Hub technical and product architecture that focuses on future state technologies and capabilities to bring the highest value to CDH and Mayo Clinic through:

  • Innovation acceleration
  • Knowledge generation
  • Analytics engines and artificial intelligence
  • Modular data and model assets for efficiency and scalability

Key Projects

Knowledge Hub Development Utilized data scraping and aggregation to build an advanced knowledge graph using LLMs, exposing data through:

  • APIs and chat interfaces
  • Document generation systems
  • Product integration through GraphRAG approaches
  • Support for institutional knowledge understanding and resource planning

Intake and Triage System Developed a web application that utilizes the Knowledge Hub and CDH Capabilities models through artificial intelligence to:

  • Guide innovators to quickest realization of CDH services
  • Provide internal insights around gaps and opportunities
  • Enable capacity balancing and staffing optimization

Senior Analytics Architect

Solution Enablement - Rochester, MN

Team Leadership

Served as team leader for a group of analytics architects to provide comprehensive solutioning to Center for Digital Health (CDH) and Enterprise needs.

AI System Development

Developed an artificial intelligence system to provide self-service analytic, dashboard, and insights capabilities from Mayo Clinic data, encompassing practice information to capabilities organization-wide.

Innovation Impact & Strategic Goals

Clinical AI Innovation

My work focused on modernizing predictive pipelines and modeling capabilities through knowledge graphs, agentic agents, and analytical AI, with strong emphasis on clinical implementation. This work builds on emerging research in biomedical knowledge graphs12, clinical federated learning34, and multimodal AI in healthcare56, with the goal of achieving the 15-30% accuracy improvements demonstrated in recent literature7.

Primary Innovation Areas

  • Multimodal Models: From naive builds to fine-tuning foundation models and GraphRAG, leveraging recent advances in multimodal biomedical AI89 and large language models in medicine1011
  • Predictive Assessment: Providing clinicians with actionable predictive data, targeting 48-72 hour advance prediction of clinical deterioration demonstrated in recent studies1213
  • Therapeutic Planning: AI-driven strategies for treatment optimization, incorporating digital twin simulations for personalized treatment pathways1415
  • Risk Identification: Early detection and scoring systems, aiming for the 15-30% reduction in preventable readmissions shown in predictive modeling research1617
  • Cognitive Complexity: Algorithm development to identify and quantify cognitive complexity in patient interactions, addressing the critical need to reduce clinician cognitive load and burnout1819

Ultimate Vision

To provide clinicians with comprehensive tools that:

  • Lower clinician fatigue by reducing cognitive load through intelligent automation and decision support20
  • Improve patient outcomes through early risk detection and personalized treatment optimization
  • Optimize care delivery workflows with seamless integration into existing clinical systems
  • Enable data-driven decision making with transparent, explainable AI that builds trust and adoption2122

Syngenta - Global Leadership Roles

2024-2024

Global Traits Digital Science Lead

Global Traits Discovery and Delivery Program Lead

Led Trait (gene, phenotype) Discovery and Delivery efforts within R&D and IT Digital Science across the entire Syngenta global space.

Key Achievements

Generative AI Program Leadership - Created, built, and led Generative AI program to automate and optimize gene delivery processes - Developed multimodal modeling with structured data and foundation LLMs for knowledge generation - Implemented prompt-based experimental design and regulatory audit capabilities

Technical Architecture - Owned complete stack from data model to application landscape - Built API integrations, MLOps, and DevOps workflows for gene discovery to trait introgression - Led cross-functional teams spanning business, IT, and science domains

Innovation & Research - Research and deployment of foundational Large Language Models (LLMs) for protein design - Predicted expression levels of molecular biology constructs as software workbench for researchers - Emphasized multimodal data and contextualization/fine-tuning of foundation models

AgBiome - Head of Data Science/ML/AI

2023-2024

Strategic Vision & Technical Leadership

Served as Head of Data Science/Machine Learning/Artificial Intelligence strategy and innovation initiatives for biotechnology company focused on microbial discovery.

Major Accomplishments

GENESIS Digital Twin Platform - Developed proprietary predictive AI decision guidance system - Combined genomics and geospatial data modeling for microbial product discovery - Generated cross-indication predictive models to accelerate lead identification

Team & Strategy Development - Led growing team of 7-20+ Data Scientists across multiple disciplines - Developed 4-year strategy and vision for Data Science, Data Engineering, and Bioinformatics - Onboarded Generative AI and Large Language Models for genomic information

Research Innovation - Developed novel algorithms using interpretable machine learning model ensembles - Created platforms for mode of action discovery from small datasets - Built cross-indication prediction platform for multi-target identification - Achieved 2 publications and multiple patentable IP technologies in first year

Firstleaf - Head of Data Science/ML/AI

2017-2023

Principal, Research and Machine Learning

Led the creation of Data Science from scratch, achieving rapid 10x user growth and key KPIs through AI-driven personalization program.

Technical Innovation

Patent-Winning Algorithms - Created and maintained core, patented algorithms behind Firstleaf wine club - Implemented models with over a billion parameters serving real-time recommendations - Operated realtime, 24/7 AI platform achieving millisecond response times with 98%+ accuracy - Delivered business optimization algorithms including 5 patents for innovative ML work

Revolutionary User Profiling - Built industry-first user profiles from billions of data points per user - Developed interpretable model algorithms for personalized recommendations with multiple industry awards - Created data-driven product creation AI optimized through MCMC parameterization

Team Leadership & Impact - Built and led distributed team of 5-15 Data Scientists and ML engineers - Achieved 5 patents (3 awarded, 2 in final review) - Drove full spectrum B2B and B2C solutions across Marketing, Finance, and Business Intelligence

University of North Carolina - Postdoctoral Research Fellow

2014-2017

Biological Machine Learning and AI

Lineberger Cancer Center

Funding: American Heart Association funded research fellow in AI-driven precision medicine

Research Focus

Single Cell Analysis - Identified, modeled, and understood noise in single cell signaling during stress responses - Utilized live cell imaging and machine learning on big data - Developed suite of ML algorithms to understand single cell signaling noise

Infrastructure & Innovation - Built and maintained live cell imaging infrastructure - Secured two fellowships and contributed to high-impact publications - Mentored graduate students and postdoctoral fellows - Extensive time series data analysis from signal processing to ML algorithm development

Career Progression Summary

Leadership Experience

  • 22 years engineering experience with 18+ years building and leading production AI/ML platforms
  • 8+ years Director/VP level leadership and strategy development
  • 11+ years ML/AI architecture solutioning
  • 25+ member globally distributed teams across disciplines in data and product, spanning 10 countries

Industry Impact

  • 5 patents awarded or in final status
  • Multiple high-impact publications in biotechnology and AI
  • Proven revenue growth through AI-driven innovations
  • Global team management across cultural and knowledge bases

Domain Expertise Evolution

  • Academic Research: Cancer therapeutics and cell biology (2014-2017)
  • Consumer AI: Wine personalization with real-time ML systems (2017-2023)
  • Biotechnology: Microbial discovery and genomics (2023-2024)
  • Agriculture: Gene discovery and trait development (2024)
  • Clinical AI: Healthcare digital twins and patient outcomes at Mayo Clinic (2024-2025)
  • Digital Pharmacy AI: AI engineering leadership at Gifthealth (2026-present)

This progression demonstrates a unique blend of deep technical expertise with strategic leadership, consistently driving innovation at the intersection of AI and life sciences.

References

  1. Nicholson, D.N., & Greene, C.S. "Constructing knowledge graphs and their biomedical applications." Computational and Structural Biotechnology Journal, 18, 1414-1428, 2020. ↩

  2. Mohamed, S.K., et al. "Biological applications of knowledge graph embedding models." Briefings in Bioinformatics, 22(2), 1679-1693, 2021. ↩

  3. Rieke, N., et al. "The future of digital health with federated learning." NPJ Digital Medicine, 3(1), 119, 2020. ↩

  4. Xu, J., et al. "Federated learning for healthcare informatics." Journal of Healthcare Informatics Research, 5(1), 1-19, 2021. ↩

  5. Acosta, J.N., et al. "Multimodal biomedical AI." Nature Medicine, 28(9), 1773-1784, 2022. ↩

  6. Huang, S.C., et al. "Fusion of medical imaging and electronic health records using deep learning: a systematic review and meta-analysis." NPJ Digital Medicine, 3(1), 136, 2020. ↩

  7. Li, X., et al. "Graph neural network-based diagnosis prediction." Big Data, 8(5), 379-390, 2020. ↩

  8. Huang, S.C., et al. "Fusion of medical imaging and electronic health records using deep learning: a systematic review and meta-analysis." NPJ Digital Medicine, 3(1), 136, 2020. ↩

  9. Soenksen, L.R., et al. "Integrated multimodal artificial intelligence framework for healthcare applications." NPJ Digital Medicine, 5(1), 149, 2022. ↩

  10. Singhal, K., et al. "Large language models encode clinical knowledge." Nature, 620(7972), 172-180, 2023. ↩

  11. Nori, H., et al. "Capabilities of GPT-4 on Medical Challenge Problems." arXiv preprint arXiv:2303.13375, 2023. ↩

  12. Churpek, M.M., et al. "Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards." Critical Care Medicine, 44(2), 368-374, 2016. ↩

  13. Shamout, F.E., et al. "An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department." NPJ Digital Medicine, 4(1), 80, 2021. ↩

  14. Venkatesh, K.P., et al. "Digital Twins for Health: Opportunities, Challenges, and Practical Implications." Nature Medicine, 28(11), 2188-2190, 2022. ↩

  15. Laubenbacher, R., et al. "Using digital twins in viral infection." Science, 371(6534), 1105-1106, 2021. ↩

  16. Kansagara, D., et al. "Risk prediction models for hospital readmission: a systematic review." JAMA, 306(15), 1688-1698, 2011. ↩

  17. Zhou, H., et al. "Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review." BMJ Open, 6(6), e011060, 2016. ↩

  18. Gardner, R.L., et al. "Physician stress and burnout: the impact of health information technology." Journal of the American Medical Informatics Association, 26(2), 106-114, 2019. ↩

  19. Shanafelt, T.D., et al. "Changes in Burnout and Satisfaction With Work-Life Integration in Physicians and the General US Working Population Between 2011 and 2020." Mayo Clinic Proceedings, 97(3), 491-506, 2022. ↩

  20. Linzer, M., et al. "Worklife and Wellness in Academic General Internal Medicine: Results from a National Survey." Journal of General Internal Medicine, 35(5), 1586-1594, 2020. ↩

  21. Tonekaboni, S., et al. "What clinicians want: contextualizing explainable machine learning for clinical end use." Proceedings of Machine Learning for Healthcare Conference, 359-380, 2019. ↩

  22. Jacobs, M., et al. "How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection." Translational Psychiatry, 11(1), 108, 2021.  ↩