A selection of projects, notebooks, and experiments. Some shipped, some in motion, some still ahead. Each one is a different angle on the same study of intelligence.
An avatar and recommendation system for sake recommendations and food pairing. The challenge is not simply recommending a bottle, but understanding why a person likes what they like — combining structured tasting data, flavor embeddings, and food-pairing knowledge with conversational interaction. The long-term vision: a digital avatar that acts as your personal sommelier.
A production forecasting platform running live on GCP, predicting daily behavior for 500 content series at horizons up to 180 days. Real-world forecasting is never one model: the system backtests a multi-family model zoo (statistical, gradient-boosted, deep, and in-warehouse BigQuery ML) and automatically selects a champion per series, per horizon. New series with no history are forecast from day one using a digital-twin method: launch curves from similar series, weighted by a hype signal, refined by pattern clustering once real data arrives.
Every forecast is written to an append-only ledger and scored against actuals as they land, so accuracy at the end of the horizon is measured, not simulated. Fully reproducible from a single repo: Terraform-managed infrastructure, keyless CI/CD, monitoring, alerting, and a teardown path.
A real-time computer vision pipeline that reads facial expressions from a webcam, builds a representation of what the face is doing, and drives a generative avatar to mirror it back as the user reacts.
The interesting questions sit between three problems: what does a face actually communicate, how do you encode that into a representation a model can manipulate, and how do you render a believable mirror without uncanny lag.
Real-time facial landmark detection, expression vectors, and gaze tracking from a single webcam feed.
Mapping landmark geometry into a pose-and-expression space the avatar can act on. The translation layer between human and twin.
Generative avatar rendering that follows the face closely enough to feel like reflection.
A study at the intersection of computational neuroscience and AI — what the brain teaches us about agents that perceive, predict, and adapt. The current focus is active inference: the idea that cognition is, at its core, a process of prediction.
The free-energy principle frames cognition as a continuous loop of prediction-minimization. A brain — or any agent — maintains a generative model of the world and acts to shrink the gap between what it expected and what it observed. The connection to modern self-supervised learning is direct: prediction error is the training signal. The connection to organizational decision-making is what makes the theory interesting outside the lab: teams, like brains, are systems that update beliefs from feedback, and the ones that update faster usually win.
Notebooks, experiments, and works in progress land on GitHub as they happen.