Theory of Computing
Computability and complexity theory, discrete and continuous models of computation, real-time computability, and the complexity of computing real functions.
Research
Theory of Computing, Analog Computing, Molecular Programming, Theoretical and Applied AI
Research
Research Areas and Interests
My research spans theoretical computer science, unconventional computation, molecular programming, analog computation, AI-driven optimization, and scientific machine learning.
Molecular Programming
Formal models for molecular-scale computation, including chemical reaction networks, programmable chemistry, reaction dynamics, and computation in biochemical systems.
DNA and Molecular Memory
Nucleic-acid-based data storage, extended genetic alphabets, molecular memory models, and constraint-based encoding for reliable information storage.
Analog Computing
Computation over continuous-state models, real functions, dynamical systems, analog complexity, and general-purpose analog computation.
AI / ML / Deep Learning Optimization
Hybrid heuristics, adaptive learning-rate optimization, sustainable computing, meta-learning, and hyperparameter optimization for machine learning systems.
Applied AI and Metaheuristics
AI for materials science, medical imaging, scientific data-driven discovery, VLSI global routing, and metaheuristic optimization for applied computational problems.
Quantum Computing
Quantum computing fundamentals, continuous-variable quantum computing, quantum complexity theory, and quantum information.
Cryptography
Analog cryptography, post-quantum cryptography, hybrid encryption schemes, and security models for emerging computational paradigms.
Publications