Who Is Behind Via Rigore
Background
Thirty-four years of engineering at the frontier of medical device technology. The same rigorous thinking that produced 34 patents in energy systems and manufacturing now applied to knowledge architecture, AI systems, and the organizations building both.
The Arc
From batteries to knowledge graphs — the same discipline, different substrate.
A career in medical battery design is, at its core, a career in complex systems: electrochemical reactions, thermal dynamics, mechanical integrity, regulatory compliance, and the long time horizons of implantable device lifetime. Every battery that powers a cardiac device or a neurostimulator is a system whose failure modes must be understood before the first cell is manufactured.
That work demanded the full toolkit: physics-based predictive models, designed experiments to isolate the variables that actually matter, fault tree analysis to map the causal chains from raw material to patient outcome, and — critically — a persistent skepticism about conclusions that arrived too easily.
Over 25 years at Medtronic, that orientation produced 34 patents, advancement to Technical Fellow and Distinguished Engineer, Chief Engineer responsibility across ICD and pacemaker battery programs, and a body of work spanning electrochemical cell design, cathode and anode materials science, electrolyte chemistry, manufacturing process development, and the reliability engineering frameworks that govern all of it.
The pivot to knowledge architecture was not a departure from that work — it was the recognition that the same problem exists at every scale. An organization's knowledge about its products, processes, failures, and decisions is itself a complex system. When that system is unmanaged, the costs are concrete: unpredictable schedules, decisions made without relevant precedent, effort spent rediscovering what was already known, and outcomes that surprise because no one had mapped what was connected to what. Left unstructured, it degrades, fragments, and becomes inaccessible. Structured rigorously — as a knowledge graph, with provenance and relationship and causal chain intact — it becomes the foundation for everything an AI-enabled organization needs to do well.
Breadth
Where the domains intersect.
The most interesting problems live at the intersection of disciplines. Mechanical behavior has thermal implications. Chemical systems have electrical signatures. A failure that looks like a manufacturing defect turns out to have a design root cause. A knowledge retrieval problem turns out to be an ontology problem.
The disciplines that intersect in this practice:
Chemical Engineering
Electrochemical Systems
Reaction kinetics, thermodynamics, mass transport, electrode materials, electrolyte chemistry, equivalent circuit modeling, and parameter extraction — the frameworks that connect electrochemical behavior to device performance and lifetime prediction.
Reliability Engineering
Failure Analysis & Prediction
Physics of failure as the organizing principle — causal mapping of failure mechanisms from material behavior and design decisions to system outcome, structured by FTA, FMEA, ALT, and Weibull analysis.
Process Engineering
Thermal Systems & Scale-Up
Continuous and batch thermal processing: heat and mass transfer design, scale-up from bench to full production, and equipment commissioning across a wide range of product chemistries and process types.
Design Methodology
Design for Reliability & Manufacturability
Structured product development from voice of customer through production control — requirements flowdown, robust design, DFR, DFM/DFA, and the statistical tools embedded throughout the development cycle.
Knowledge Engineering
Graph Architecture & AI Systems
Knowledge graph design, semantic modeling, RAG architecture, and the data governance frameworks that make AI systems trustworthy rather than merely capable.
Planning & Modeling
Engineering Program Planning
Monte Carlo scheduling, uncertainty propagation, and experimental sample optimization — integrated into directed graph models that connect tasks, deliverables, decisions, and dependencies across engineering programs.
Via Rigore et Opere
Credentials
Licensed Professional Engineer (PE) — Chemical Engineering, Minnesota
Master Black Belt (DFSS) — Design for Reliability & Manufacturability
Chief Engineer, High Power Batteries — Implantable Cardiac Defibrillators, Medtronic
Chief Engineer, Medium Rate Batteries — Pacemakers & Neurostimulators, Medtronic
Technical Fellow — Medtronic
MIT Professional Education — Architecture and Systems Engineering: Models and Methods to Manage Complex Systems
MIT Professional Education — Quantitative Methods in Systems Engineering
MIT Professional Education — Model-Based Systems Engineering: Documentation and Analysis
MIT Professional Education — Models in Engineering
MIT Professional Education — Architecture of Complex Systems
Highlands Ability Battery Certified Consultant
34 U.S. Patents — energy systems, electrochemical design, manufacturing process
FIRST Robotics Competition — Mentor, White Bear Lake High School (20 years)