I’ve been working on ML/AI since before it was cool — started with a PhD in AI & Epidemiology at the University of Iowa, worked in big search engines, launched a couple of startups, and more recently co-founded Evidation Health, a $1B digital health platform deeply infused with AI, where I lead a cross-functional org spanning product, engineering, infrastructure, security, data science, and TPM.
Search Dir. of Technology at Ask.com - relevance, classification, 7 patents Ranking Dir. of Search at OneRiot - real-time social signals (→ Walmart) Vision Co-founded IMRSV, CTO - computer vision for billboards (→ Kairos) Health Co-founded Evidation, CTO - product & eng, wearables/EHR, health AI I’m a very active technologist, tinkering daily with hardware and software on many projects, from IoT sensors and embedded systems to LLM-powered tools. I advise and invest in early-stage startups, so let me know if you are building something cool.
I’m also a regular speaker at industry and academic conferences on topics ranging from AI and health tech to search and digital biomarkers. My research spans web search, disease surveillance, and digital biomarkers: 18 publications, 10 patents, and ~3,000 citations on Google Scholar. I am a TechStars and Rock Health alumn, and a Robert Wood Johnson Foundation award recipient. Feel free to reach out via email or connect on social media.
currently exploring
VS Extension for Coworking — Next-gen IDE will have to be built around LLMs/human interaction. I am experimenting with what that may be. Code, graphs, explanations on the edges. The machine writes the code but the human architects it and is in the know.
Claude Cowork — Pair programming with AI agents — how multi-agent collaboration is changing the way code gets written. OpenClaw — Open-source personal assistant with memory, personality and initiative. Trying to run my own on a cheap Fly.io machine and interact with it via Telegram. Requirements as Code — Encoding requirements so AI agents can follow them — moving from natural language specs to machine-verifiable constraints.
latest thoughts
Startups Are Now Just MVPs for Tech Giants — Until recently, startups had a genuine speed advantage. Big corporations were tangled in bureaucracy, committees, and endless approval chains. A small team could ship innovative products in weeks while enterprise companies took months (or years). But Large Language Models changed everything. Pumping the Brakes on AI-Driven Team Cuts — LLMs are incredible for writing code faster. I have seen productivity gains that feel almost magical. Features that used to take days now come together in hours. That said, nobody is quite sure yet if that 10x speed boost comes with hidden costs. Does it also mean 1/10th the stability? After all, nobody actually wrote or deeply reviewed that code. Are we looking at 10x the bugs (and will AI patch those up too)? Only time will tell. 