Intro
After leading engineering and data science teams in areas such as automatic speech recognition, GPS and location-based services, and large-scale data systems, I am now back hands-on tackling complex, high-impact technical problems.
My work spans applied AI/ML and data science in domains where data is messy, systems are heterogeneous, and decisions have real economic consequence such as found in energy systems, financial markets, and neurotech.
Current Focus
A primary area of focus is energy systems and markets:
- Energy microgrid dynamics in virtual power plants (VPP)
- Demand response and pricing dynamics in energy-as-a-service (EaaS)
- Electricity pricing and auction dynamics (e.g., ERCOT)
These systems sit at the intersection of physical infrastructure, economic and
financial incentives, and real-time decision-making.
The central question:
How can applied AI and agentic systems improve the economics and
operational efficiency of distributed energy resources?
Mission
Apply AI/ML and data science to solve technically challenging problems
with real-world impact, particularly in systems where:
- data is high-dimensional, noisy, and time-dependent
- control decisions interact with economic incentives
- outcomes matter at scale (cost, efficiency, human and social impact)
Work Style
- Respect the Data
- Hypothesis-driven
- Iterative Hands-on MVP Development
- Fractional AI/Data Science collaborations
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