After leading engineering organizations in Silicon Valley across speech recognition, GPS and location technologies, and large-scale data systems, I returned to hands-on research and engineering. Today my work combines AI, machine learning, computational neuroscience, and data science to solve technically challenging problems at the intersection of statistical inference, complex systems, and real-world decision making.
I work comfortably across industry and academic research, building practical systems while exploring new computational methods for understanding complex data..
Developing statistical and computational methods for analyzing chronic neural recordings, with an emphasis on robust neural representations for intracranial brain-computer interfaces. Recent work investigates how functional connectivity methods and representation learning can improve the robustness of chronic brain-computer interfaces.
AI for Energy Systems
Applying agentic AI and machine learning to distributed energy resources, virtual power plants, and energy management systems. Current interests include semantic modeling, optimization, and operational decision support for complex energy infrastructure.
Applied AI Systems
Designing AI systems that integrate statistical learning, semantic models, and autonomous agents to solve problems involving heterogeneous data, physical systems, and human decision making..
Research Philosophy
The problems that interest me share common characteristics:
- high-dimensional, noisy, time-dependent data
- heterogeneous data sources and semantic structure
- complex interactions between physical systems and human decisions
- opportunities to combine principled statistical methods with modern AI techniques
Rather than applying AI indiscriminately, I prefer to start with the structure of the underlying problem and build models that are both scientifically grounded and practically useful.
Working Style
- Hypothesis-driven
- Data-driven
- Hands-on prototyping
- Iterative model development
- Close collaboration with domain experts
- Fractional AI, machine learning, and technical strategy engagements
Recent Work
Scientific Reports 2026 publication on chronically stable speech brain-computer interfaces
Poster presentation at the 2026 NIH BRAIN Initiative Conference
Agentic AI for semantic understanding of energy microgrids (submitted)
AI strategy and product roadmap consulting for intelligent energy systems
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