AI/ML leader with 20+ years building intelligent systems — from co-founding a sound-recognition startup (acquired by Analog Devices) to designing agentic AI workflows and publishing 200+ open-source Python packages. Ph.D. in Mathematics, 6 issued patents, 15+ peer-reviewed publications.
My work is about putting AI to work in practice — (LLM-based and traditional) AI alike. For over 20 years I've done this across many domains with the AI that came before the current wave: statistics, machine learning, data science. The generative inflection of 2023 didn't replace that foundation — it extended it. Tasks that used to be the hard part, especially extracting and generating semantic information, are suddenly far more tractable. That shifts where the real challenge lies: not in the models, but in wiring them into the way a business actually operates.
So that's what I do now. I help people harness today's AI capabilities inside their existing workflows, and incrementally reshape those workflows — and the software underneath them — so they're ready to absorb what comes next: new model generations, a multiplying ecosystem of providers, a landscape that changes monthly. The goal is to capture the upside now without paying constant adaptation overhead later.
The work has three sides. I teach — consulting and workshops that help teams navigate the moving parts of AI integration. I design — architecting workflows and systems that exploit what's available today while staying open to future evolution and the multiplicity of models. I build — agentic pipelines, retrieval-augmented generation systems, semantic navigation tools, and no-code AI application frameworks. I work with C-level leaders, architects, and developers alike, translating between strategy and implementation. And because modern AI has lifted so much of the semantic heavy lifting, I can often take a proof of concept or MVP from idea to delivery on my own.
Artificial Intelligence eXtensions — composable tools for AI workflows.
Visualize large-scale network graphs and ML embeddings interactively.
Clean Python interface to OpenAI APIs. Composable, minimal, dict-like patterns.
Extract code exercises from code itself — structured skill definitions.
Generate MCP servers from Python functions — the py2X pattern for Model Context Protocol.
Operational stewardship tools for AI systems.
Context management for AI applications.
Backend framework for exploring and comparing RAG operations — retrievers, vector stores, and answering strategies.
Tools for working with embeddings — generation, storage, search, and analysis.
I co-founded OtoSense — a signal ML company that taught computers to "hear" sounds and "feel" vibrations. We developed language-model-inspired approaches to acoustic signals: decomposing sound into primitives, building syntactic representations, and mapping them to semantic labels. The technology served industrial monitoring, home security, accessibility for deaf users, and more.
OtoSense was acquired by Analog Devices in 2018, less than five years after inception. I then served as Director of Machine Learning at Analog Devices (2018–2023), where I built ML acceleration platforms, open-source tooling for data access and experimentation, and mentored data science teams across the company.
Building visualization and ML tools for graph and embedding-space exploration. Interactive UIs for semantic navigation in RAG, recommendation, and similarity search workflows.
Helped organizations adopt LLMs and generative AI. Delivered PoCs: resume-matching systems, no-code AI platforms, RAG-powered contextual assistants.
Led ML platforms and tooling strategy for IoT and signal ML. Built Python frameworks unifying data access, feature engineering, and experimentation. Mentored data science teams and shaped company-wide AI strategy.
Co-founded a sound-recognition AI company. Developed semantic audio models, led architecture and ML. Acquired by Analog Devices.
Attribution models, behavioral targeting, clickstream analytics, and KPI time-series for marketing intelligence.
NLP and semantic systems for AdWords optimization. Pricing, forecasting, and geographic data quality for large-scale marketing operations.
Independent ML consultant across Europe and the US. Recommender systems, Bayesian inference, duplicate detection, semantic analysis, forecasting, and optimization. Clients included Sanoma, Easyvoyage, Ricoh, Metron, and others.
A sustained, decade-long commitment to shipping reusable Python tooling. The ecosystem spans data access layers, ML pipelines, audio processing, visualization, storage abstraction, and developer utilities. Many packages are actively maintained and used in production workflows. Browse all on PyPI →
Base tools for building and transforming Data Object Layers — dict-like interfaces to any storage backend.
Link functions into callable DAG objects — the backbone of pipeline composition.
Quick HTTP web-service construction from Python functions.
Business analytics utilities.
A data-object-layer to get tables from a variety of sources with ease.
Data Object Layer for HuggingFace — dict-like access to models and datasets.
JSON schema utilities — validation, transformation, and generation.
Investigations in financial sentiment analysis.
Higher-order function utilities.
Peer-reviewed research spanning two domains: pure mathematics (graph theory, combinatorics, Hamiltonian systems) and applied social science (drug-use trajectories, longitudinal data measurement, harm reduction).
Graph theory & combinatorics. Advisor: Ronald J. Gould.
I write about Python architecture, ML tooling, and the craft of building systems that last. I also have a history of teaching probability, statistics, data science, and software design at universities and in industry workshops.