Deep explorations in theoretical CS, AI, logic, and the foundations of intelligent systems
My foundational self-study in TCS aims to deeply understand the limits and capabilities of computation, influencing robust algorithm design and complex system analysis.
A strong focus on the theoretical underpinnings of artificial intelligence, including agent design, decision theory, and the philosophical implications of machine cognition.
Dedicated exploration of raw logic, its application in computing, and the broader philosophical questions arising from advanced technology and intelligence.
My current primary research centers on AI agent formalism and advanced decision theory. I am rigorously studying the theoretical models of intelligent agents and the computational aspects of rational choice. This involves deep dives into optimizing agent behavior in complex, dynamic environments, with a strong emphasis on foundational mathematics.
I am particularly engaged with chapters on utility theory, game theory, and multi-agent systems within the AIMA textbook, supplemented by academic papers on reinforcement learning theory and formal verification of agent behavior. My goal is to bridge theoretical robustness with practical, ethical AI design.
During my internship at Engine CX I built BRAIN. I decided to construct it from the very first agent theory principles and discrete mathematics.
I formalized BRAIN as a five tuple: ⟨S,A,P,π,R,M⟩, explicitly grounding its behavior in theoretical decision-making models and memory persistence mechanisms.
Beyond technical detail, BRAIN serves as a philosophical statement: a return to first principles as the true foundation for engineering intelligence.