Energy Modelling · Quant Research
Industrial
Mathematician
I build statistical and optimization models for capital and energy markets.
About Me
Rigor in analysis. Clarity in decision-making.
I am passionate about applying quantitative methods and statistical modeling to capture the volatility of capital markets, with a strong intellectual curiosity to explore the mathematics behind data patterns. I have a PhD in engineering and subsequently worked as a postdoctoral research scientist at the University of Manchester.
PhD
Engineering
CFA
Level I Passed
5+
Years Research
Topic Hubs
Topic Clusters
Stablecoins & Payments
Stablecoins and payments are turning into a stack ownership fight
A reading hub on stablecoin rails, payment networks, treasury workflows, and the compliance layer that now decides who captures the margin.
Agents & Governance
Agents are moving into production, and governance is now part of the product
A reading hub on MCP, enterprise deployment, autonomous coding, evals, and the safety layer that keeps agent systems usable in production.
Power Markets & Data Centres
AI load is forcing electricity markets to price capacity, allocation, and off-grid power differently
A reading hub on PJM capacity pricing, data-centre load growth, dedicated generation, and the macro effects of energy shocks.
Expertise
My expertise lies in optimization and prediction.
Optimization
I specialize in transforming intricate financial and regulatory constraints into structured, solvable frameworks. Whether ensuring compliance with evolving market regulations or optimizing resource allocation, I build solutions that balance efficiency and robustness.
Prediction
I analyze trends and anomalies within data series using both linear and nonlinear models. I help businesses anticipate market movements, optimize strategies, and make informed financial decisions.
Day-to-Day
What I Do
After transitioning from academia, I work as an energy modeller specializing in energy market optimization.
Developing software packages to analyze and process large-scale energy market datasets, including demand, supply, plant-level, time-resolution, and interconnection flow data.
Developing an optimization model of electricity distribution across multiple sources (gas, coal, solar, wind, etc), generating long-term price forecasts and market insights.
Adding features to the market model including capacity market features, renewable energy certificate (REC) features, time granularity, and data warehouse integration.
Reliability check of new features through testing, including unit, integration, behavioural, and regression tests.
Running scenario analyses in ERCOT, European, and GB markets to evaluate the impact of technological advancements, policy shifts, and economic trends on energy prices.
Continuously enhancing models and optimizing platform data structures: "There has to be a better way."
Let's exchange ideas.
Whether it's a question, a collaboration, or just an interesting thought — I'd love to hear from you 😊.