About
I like brains, bots, math, and the mysterious machinery of mind and matter! I work in AI, think about intelligence, and occasionally hope to be right about something. I didn't quite make the cut for a neuroscientist, mathematician, or computer scientist, so I compromised to work at the intersection of all three.
At Dell Technologies, I lead AI software initiatives globally for the Global Technology Office, integrating efforts across Dell’s various engineering, product, and presales teams, as well as open and close source partners. I keep one foot in research, thinking about how models might one day be capable of executive function, and the other in emerging technologies, thinking about the next wave of disruptions shaping the future of AI. I enjoy collaborating closely with large enterprises and Dell’s most select customers on technical engagements, thought leadership, and advanced AI solution design. I've driven hundreds of millions in customer opportunities to operationalizing AI and machine learning at enterprise scale.
Previously, I worked at the intersection of Computational Cognitive Neuroscience and Artificial General Intelligence, exploring how insights from the brain might guide the next generation of AI. My research examined the limits of existing AI tools while developing neuromorphic-inspired theories and deep learning models aimed at higher-order cognition and intelligence. These brain-inspired approaches offered new ways to tackle problems where narrow algorithms fall short and helped shed light on fundamental questions about building systems that can truly reason and learn at a deeper level.
Things I like to think about (in no order of importance):
• Neural and deep learning models of complex cognition, decision-making, and higher-order cognition
• Reinforcement learning models, with a focus on hierarchical learning, compositional generalization, rewards, and semantic learning
• Goal-oriented learning, thinking, reasoning, and generalization
• AI alignment, explainable AI, AI ethics, human-in the loop, and human machine teaming
• Generation and deployment of optimal decision making models and strategies (model building, risk, bias, effort, resource engagement, motivated reasoning, model based vs. model free/system 1 vs. system 2, and other cost benefit analysis)
• Internal model building and representation learning for embodied, interactive agents using geometric algebra and combinatorial topology to organize, understand, and optimize abstractions in high-dimensional combinatorial space
• Chaotic systems and attractor landscapes, shaped by energy functions and supporting formulation of graph-structured memory and associations for short and long-term learning, associative recall, and planning
• Refinement of deep learning architectures and algorithms through integration of complex systems theory, enabling advanced mathematical frameworks for AGI
* Disclaimer - All thoughts and views are my own.
* Disclaimer - Personal artwork is subject to copywrite.
* Disclaimer - Website images were generated by Sora