About

The Intersection

Most people see either the tech problem OR the people problem. I see how they interact.

I have a PhD in Behavioral Neuroscience from the University of Illinois Chicago, where I studied how the brain processes signals under complexity and cognitive load. My research focused on visual perception—specifically, why the same signal can be highly visible or completely invisible depending on context, position, and surrounding noise.

I discovered that perception isn't about signal strength. It's about:

Where the signal appears in your visual field
What other signals are competing for attention
How your brain is already loaded with other tasks
The orientation and framing of the information

Then I spent 10 years leading engineering teams at scale—plus fractional CTO and startup advisory work.

As Fractional CTO for an early-stage startup, I provided technical leadership and strategic guidance on product-market fit and technical architecture decisions. I've mentored startup co-founders on technical strategy, team building, decision-making under uncertainty, and navigating the transition from founder-led to team-led engineering.

At The Aspen Group (2019-2024), I was Senior Director of Engineering on the executive team, leading 20+ engineers across 5 national brands. I drove 60% performance improvements on location pages through technical optimization and led platform modernization initiatives.

At Peapod Digital Labs (2018-2019), I led an 87% performance improvement (Time to Interactive: 29.9s → 3.9s) that directly impacted conversion and user experience for a high-traffic e-commerce platform.

At ECRA Group (2015-2018), I led software development for educational assessment platforms serving K-12 schools nationwide, managing cross-functional engineering teams.

What I discovered across all these roles: Engineering teams fail to detect system problems for the same reasons brains fail to detect visual signals.

It's not that the signal isn't there. It's that:

The problem emerges gradually (low contrast)
The metrics are in the wrong place (positional bias)
There are too many competing alerts (crowding)
The data is poorly framed (orientation problem)

I call this "Behavioral Drift"—when system behavior gradually diverges from intended behavior, but remains imperceptible until it's too late.

This insight became the foundation for everything I do now.

Background

Education

PhD, Behavioral Neuroscience (2007-2013)
University of Illinois Chicago
Dissertation: Visual perception and signal detection under complexity
MS, Computer Science (2008-2012)
University of Illinois Chicago
MS, Behavioral Science (2008-2012)
University of Illinois Chicago
Focus: Cognitive load, attention, decision-making
BA, Psychology (2002-2006)
Augustana College

Experience

Founder & Principal | Aurvia Group & Ask Jentic AI Innovation Lab | 2025-Present
Decision science consulting for CTOs and engineering executives. Mentoring co-founders on technical strategy, team building, and decision-making
Fractional CTO | RudyApp | 2024-2025
Technical leadership and strategic guidance for early-stage startup
Senior Director of Engineering | The Aspen Group | 2023-2025
Led 20+ engineers across web platforms, integrations, and vendor partnerships. Drove 60% performance improvements on location pages. Managed complex platform migrations and digital transformation across 5 national brands.
Director of Front-End Engineering | Peapod Digital Labs | 2020-2023
Led 87% performance improvement (Time to Interactive: 29.9s → 3.9s). Rebuilt front-end architecture for grocery e-commerce platform. Managed engineering team for high-traffic consumer application.
Director of Software Engineering | ECRA Group | 2015-2020
Led software development for educational assessment platforms. Built systems serving K-12 schools nationwide. Managed cross-functional engineering teams.

Key Accomplishments

Led Platform Modernization at Aspen Group
Managed complex platform migrations and digital transformation initiatives across 5 national brands, improving system performance and team efficiency.
87% Performance Improvement at Peapod
Led front-end rebuild that reduced Time to Interactive from 29.9s to 3.9s. Measurable impact on user experience and business outcomes.
60% Performance Gains at Aspen Group
Technical optimization of location pages that significantly improved load times and user experience across 5 national education brands.
Fractional CTO & Startup Advisory
Provided technical leadership for early-stage startups and mentored multiple co-founders on technical strategy, team building, and decision-making.
Cross-Domain Expertise
Rare combination: PhD studying how brains process complexity + 10 years leading engineering teams at scale + fractional CTO and advisory experience.

My Framework: Behavioral Drift

I coined this term to describe a phenomenon I observed both in my PhD research and in engineering organizations.

Behavioral Drift occurs when system behavior gradually diverges from intended behavior, but remains imperceptible due to specific conditions that prevent detection.

In visual neuroscience, we know that the same stimulus can be highly visible or completely invisible depending on:

1. Contrast — Low-contrast changes (gradual shifts) are harder to detect than high-contrast changes
2. Position — Signals in peripheral vision or "low-attention zones" are more likely to be missed
3. Crowding — When multiple signals compete for attention, perception of any single signal decreases
4. Orientation — How information is framed affects whether it's detected at all

The exact same patterns show up in engineering teams:

1. Contrast → Gradual Performance Degradation
Systems slowly get worse, but because the change is gradual, no one notices until it's critical
2. Position → Metrics in Low-Attention Zones
Important indicators buried in dashboards no one checks, or in systems people have learned to ignore
3. Crowding → Alert Fatigue
So many alerts, warnings, and notifications that teams become numb to all of them
4. Orientation → Poorly Framed Data
Metrics shown in ways that obscure meaning or don't connect to decisions

Traditional engineering metrics show WHAT is happening.

My work reveals WHY teams miss the signals that matter—and how to make invisible problems visible before they become crises.

This framework powers both Aurvia (decision consulting) and Operion (team analytics platform).

Two Applications of the Same Insight

I run two companies that apply behavioral science to engineering leadership:

Aurvia

Decision Science Consulting + Applied Research Lab

I help CTOs and engineering executives unstick high-stakes decisions that have stalled for months.

These are decisions with:

• Multiple stakeholders with competing priorities
• Unclear tradeoffs and hidden assumptions
• Diffused ownership and accountability
• Technical complexity mixed with organizational politics

Consulting: 2-4 week engagements that surface the hidden patterns blocking your decision and create a clear path forward.

Lab (Ask-Jentic AI): Where I test new decision frameworks, build AI-assisted analysis tools, and experiment with behavioral measurement approaches before bringing them to clients. Current lab projects include AI agent orchestration patterns and early-warning systems for team cognitive load.

→ Learn more at aurvia.io

Operion

Team Behavioral Health Analytics

I built this platform to make invisible work visible—detecting burnout signals, cognitive overload, and collaboration breakdowns before they become problems.

Based on the same research that powers Aurvia consulting:

• Statistical pattern detection (not just dashboards)
• Behavioral signals (not just delivery metrics)
• Early warning system (not post-mortem analysis)

The Challenge: Build production-worthy SaaS using AI agentic teammates with human-in-the-middle orchestration. I'm learning firsthand what works (and what doesn't) about leveraging AI in real engineering workflows—and how to run a business as CEO.

Currently in private beta for engineering leaders managing 10-50 person teams.

→ Learn more at operion.app

← Back to home