About

Reinforcement learning, optimization and forecasting — mostly in production

I work on decision-making under uncertainty. In practice that means reinforcement learning, mathematical optimization and forecasting — usually applied to systems where a wrong decision costs real money, and where the textbook version of the method turns out to need considerable adjustment before it survives contact with production.

That thread runs through everything here: a PhD on routing under uncertainty, a day job forecasting electricity prices, a book on applying RL to business problems, and the writing on this site about what actually works.

What I’m working on

I’m a Senior Data Scientist at Enverus, where I own the end-to-end development and operation of locational marginal price forecasting across more than 10,000 pricing nodes in US and Canadian electricity markets — feature engineering, applied research, distributed training, deployment, APIs, monitoring and the unglamorous maintenance that keeps a forecast trustworthy.

Alongside the forecasting work I research large-scale mixed-integer linear programming for power-flow and security-constrained unit commitment, including using reinforcement learning to guide cutting-plane selection — teaching a solver which cuts are worth adding, so branch-and-bound converges faster on problems that are otherwise intractable at market speed.

Before Enverus I led technical work at Bits in Glass, building a hierarchical RAG platform over 20 million oil-and-gas documents and a dynamic fuel-pricing system that combined integer programming with contextual bandits. Earlier still I built RL-based surge pricing at Snapp, the largest ride-hailing platform in the Middle East, running across roughly 200,000 orders a day. My CV has the full history.

Research

I’m a PhD candidate in Geomatics Engineering at the University of Calgary, defending on 9 October 2026. My research applies hierarchical reinforcement learning to real-time policy optimization — vehicle routing, disaster-response logistics and transportation problems where demand is stochastic, the fleet is heterogeneous, and a decision has to be made now rather than optimally.

Published work appears in Computers & Operations Research, Transportation Research Part C, ACM SIGSPATIAL and the KDD supply-chain workshop. The research page lists everything.

The book

I’m the author of Applied Reinforcement Learning (Manning), currently in early access with full release expected January 2027. It covers RL for business optimization — warehouse logistics, dynamic pricing, routing, resource allocation — through to fine-tuning language models with PPO, GRPO and verifiable rewards.

I wrote it because most RL material stops at games and simulations. The gap between a working policy in a clean environment and one that holds up against real constraints, messy data and stakeholders who need to understand the decision is where the actual difficulty lives, and it deserved a book.

Teaching and writing

Teaching keeps me honest — it is difficult to hand-wave through an explanation when someone is going to try running the code. I’ve been a teaching assistant at Calgary for Engineering Design, Programming with Data, and Spatial Data Mining, and I’ve built Udemy courses on contextual bandits and reinforcement learning for operations research. I also review for machine learning, transportation, optimization and spatial-computing journals.

This site is where the shorter-form version of that lives: articles and tutorials, notes on papers I’m reading, implementations, and occasional writing about the professional side of doing this work. Some of it is paired with videos on my YouTube channel, PhiloAI.

Elsewhere

I’m always glad to hear from people working on RL or optimization in production, and from students trying to get started. If you’re writing about something in this space, send it to me — I’d rather read your work than another press release.