Welcome
About Us
The Modern Heuristic Research Group (MHRG) develops artificial intelligence, machine learning, and computational intelligence methods for complex cyber-physical, autonomous, and human-interactive systems. Our research emphasizes trustworthy and deployable intelligence, including self-supervised learning, explainable and responsible AI, adversarial learning, generative and agentic AI, and human-supervised decision support.
Current work spans industrial and critical-infrastructure cybersecurity, smart manufacturing and cyber-physical testbeds, electric-vehicle and in-vehicle network security, smart grids and energy systems, 2D/3D computer vision, LiDAR and robotic perception, and multimodal AI for healthcare and radiotherapy. Across these domains, we study anomaly detection, resilience, adversarial robustness, structured and graph-based reasoning, intelligent sensing, and reproducible system-level evaluation.
This work builds on MHRG’s longstanding research in artificial neural networks, fuzzy logic systems, unsupervised learning, intelligent and adaptive control, human-machine interfacing, software-defined networks, robotics, visualization, energy systems, infrastructure modeling, and computational intelligence for reliability, optimization, and decision support. These established areas continue to inform the group’s newer work in trustworthy AI, autonomous systems, cybersecurity, and data-driven engineering.
MHRG works across disciplinary boundaries and collaborates with academic, government, and industry partners to move AI methods from algorithms and models toward validated systems, testbeds, and real-world applications.
What We Do
We develop and evaluate AI for secure, resilient, explainable, and human-centered cyber-physical systems across cybersecurity, autonomy, perception, energy, and healthcare.
Our Projects
Explore current and completed research projects supported by government, university, and industry partners.
Our Publications
Browse journal articles, conference papers, tutorials, book chapters, and other research outputs from MHRG.