Computability & Complexity Theory
Computability, computational complexity, real-time computation, continuous models of computing, and the computability and complexity of real functions.
·Theoretical Computer Science ·Molecular Programming ·Unconventional Computing ·Artificial Intelligence ·Optimization ·Scientific Discovery
Bridging Theoretical Computer Science, Molecular Programming, Analog Computation,
Theoretical AI, Optimization, and AI-Driven Discovery.
I am a PhD Candidate in Computer Science at Iowa State University working at the intersection of theoretical computer science, molecular programming, unconventional computing, machine learning, and scientific discovery. My research explores how computation emerges across discrete, continuous, biological, and physical systems—from chemical reaction networks and analog models of computation to AI-driven optimization and data-driven science.
I enjoy building bridges between theory and practice, using mathematical foundations to understand computation while developing computational tools that address challenges in science, engineering, and emerging technologies.
Areas of expertise and interest
Core technical directions
AI-Machine Learning
AI-Deep Learning
AI-Learning Optimization
AI-Metaheuristic Optimization
Molecular Programming
Quantum Computing
Information Theory
Molecular Memory
Theory of Computation
Analog Computing
Dynamical Systems
Cryptography
Reversible Computing
Profile
Research-centered academic portfolio
Molecular & Analog Computing
Molecular programming, chemical reaction networks, analog computing, dynamical systems, and chaos theory.
Optimization & AI
Hybrid heuristics, learning-rate adaptation, medical imaging, deep learning, and interpretable ML for materials science.
Academic Community
IEEE, ACM, IEEE-HKN, reviewing, conference service, student leadership, and outreach.