Research
I explore how research, AI, and product thinking can help turn uncertain problems into better systems and decisions.
Research
04 focus areas, Research · Strategy · Engineering
What I'm paying attention to.
A small set of areas that come out of real product work: the questions that keep shaping what I build and how I decide.
Applied AI
Where AI genuinely helps inside a product, where it doesn't, and how to build and evaluate it responsibly.
- AI-Native Development
- Evaluation
- Product Integration
Research-Driven Product Development
Using user, market, and technical research to decide what to build before building it.
- Product Discovery
- Validation
- Evidence
Experimentation & Evidence
Running small, honest experiments and letting what they show shape product decisions, rather than opinion or momentum.
- Experiments
- Evidence-Driven Decisions
- Measurement
Learning Through Building
Treating prototypes and first versions as questions: building something small to find out what's actually true.
- Prototyping
- Iteration
- Research-to-Product
Where the research work lives.
Projects, papers, and notes are added as they become ready to share publicly. Each part fills in as the work does.
Current Research
Active questions and projects I'm working through right now.
In preparation
Current research projects are being prepared for publication here.
Selected Research Projects
Finished or published projects, with the question, the method, and what was learned.
In preparation
Selected projects will be shared here once they're ready for public view.
Publications & Papers
Papers, preprints, and formal write-ups, linked to where they're published.
In preparation
Publications and papers will be added here as they become publicly available.
Experiments & Research Notes
Shorter, working notes: experiments, methods, research logs, and observations.
In preparation
Experiments and research notes will be published here as they're written up.
Research is not separate from building.
It's how I decide what's worth building, and how I find out whether it worked. Most of it is practical: questions asked early, small tests, and honest checks after launch.
Reduce assumptions
Separate what we know from what we're guessing before it turns into scope, cost, and code.
Understand users and systems
Look at how people, tools, and constraints actually behave, not how we expect them to.
Test ideas
Use small experiments and prototypes to check a direction before committing to it.
Inform product choices
Turn findings into clear decisions about what to build, what to cut, and what to do first.
Evaluate outcomes
Check whether what shipped did what it was meant to, and feed that back into the next decision.
Working on a research problem, applied AI project, or experimental product?
Let's explore it. Share the question you're working on and where things stand today.