About

Dr. Rodrigue Rizk is currently a professor and the Computer Science Graduate Program Director at the University of South Dakota. He also serves as the Chair of the IEEE Siouxland Section, and the Vice Director of Engineering at the Artificial Intelligence Research Lab. His interdisciplinary work bridges cognitive science, neural computation, and artificial intelligence.

Research Vision

Dr. Rizk’s research lies at the intersection of machine learning, cognitive science, and quantum computation, aiming to uncover the principles that make intelligent systems adaptive, explainable, and self-improving.

His work develops brain-inspired architectures and learning algorithms that autonomously discover abstractions, goals, and reasoning strategies, bridging neuroscience and explainable AI to advance our understanding of learning itself.

At the heart of this effort lies a central question:
“How can an intelligent system continually refine not just what it knows, but the very process by which it learns?”

Current efforts explore world models, lifelong learning, and quantum-enhanced intelligence, integrating ideas from deep learning, reinforcement learning, and quantum information theory to design the next generation of explainable self-evolving intelligent agents.

Research Interests

AI and Deep Learning

Deep Learning
  • Deep Learning and Neural Networks
  • Foundation Models, Small/Large Language Models (LLMs), MLLMs, and Agentic AI
  • Meta-Learning
  • Physical-Inspired Computing and Physical AI
  • Brain-inspired computing and neural architectures
  • Cognitive Modeling and Computational Neuroscience
  • Explainable artificial intelligence (XAI)
  • Multi-Modal and Cross-Modal Learning
  • Bioinformatics and pattern recognition
  • Adaptive intelligent systems and decision support

Reinforcement Learning

Reinforcement Learning
  • Deep Reinforcement Learning
  • Imitation Learning and Apprenticeship Learning
  • Planning under Uncertainty
  • Multi-Agent Systems
  • Game Theory and Theory of Mind
  • Digital Twins, Autonomous vehicles, and self-driving cars

Quantum Computing

Quantum Computing
  • Quantum Computing and Quantum Machine Learning
  • Quantum Algorithms
  • Quantum Simulation
  • Quantum Hardware and Qubit Technologies
  • Quantum Error Correction and Fault Tolerance
  • Hybrid Quantum-Classical Algorithms
  • Quantum Cryptography and Communication
  • Emerging Technologies

Artificial Intelligence Research Lab

Our lab is building a collaborative environment that explores the theoretical, algorithmic, and cognitive foundations of autonomous learning. Prospective students and collaborators with interests in combining rigorous theory with creative system design are encouraged to reach out.

If you are driven by questions about how intelligence can understand and improve itself, and if you enjoy blending mathematical insight, algorithmic innovation, and imagination, you will find a natural home in this research effort.

Explore Research