AIR Center Research Thrusts
Main AIR Center Focus Research Areas
Fundamental AI Models
Aims to develop next-generation architectures, learning algorithms and training paradigms that enable more capable and generalizable AI systems. It emphasizes creating models that are explainable, safe, secure, fair, transparent and accountable, ensuring they behave reliably across diverse tasks and real-world conditions. This thrust advances both the theoretical foundations and practical methods needed for trustworthy, high-impact AI.
Autonomous Systems
Led by Dr. Tingjun Lei
Focuses on developing intelligent agents that can perceive, reason and act safely and reliably in complex, dynamic environments. This includes advancing adaptive decision-making, robust perception and human-AI collaboration to enable trustworthy autonomy across domains such as robotics, transportation and defense.
Cyber Security
Led by Dr. Sicong
Aims to develop intelligent, adaptive security systems that leverage AI models to detect, predict and mitigate sophisticated cyber threats. It also advances techniques for making AI itself safe and robust against adversarial attacks to ensure trustworthy, resilient deployment.
Biology and Medicine
Led by Dr. Bo Liang
Focuses on developing AI models to learn patterns from large-scale biological and clinical data—such as genomes, medical images and electronic health records—to improve diagnosis, predict disease risk and guide personalized treatments. Another major area under this thrust is applying generative AI to design novel drugs, proteins and therapeutics much faster than traditional laboratory methods.
Aerospace and Aviation
Led by Dr. Jielun Zhang
Aims to enhance aerospace and aviation by enabling intelligent flight control, autonomous navigation and real-time decision support that improve safety and efficiency. It also accelerates aircraft design and maintenance through simulation, optimization and predictive diagnostics.
Quantum AI
Led by Dr. Jobayer Hossain
Focuses on advancing quantum algorithms, hardware and error-corrected systems capable of tackling problems beyond the reach of classical computers, while also leveraging quantum AI to enhance quantum control, optimize circuit design and accelerate progress toward practical quantum advantage.
Physics Informed AI
Dr. Diego Fregolent Mendes de Oliveira
Focuses on integrating quantum computing with artificial intelligence to enhance data processing, optimization and learning capabilities. It explores how quantum algorithms and quantum hardware can accelerate machine learning, solve complex problems and analyze large datasets beyond classical computing limits.
Chemistry Informatics
Led by Dr. Nagababu Andraju
Aims to data-science methods and AI models to chemical data to store, analyze, and model molecular information. It supports projects such as drug discovery, materials design, and reaction prediction by using databases, algorithms, and machine learning to understand chemical structures.
Education
Aims to use AI to better understand how people learn, interact and make decisions, enabling more effective, personalized and equitable educational tools and social interventions. It investigates human–AI collaboration, learning analytics, behavioral modeling and sociotechnical impacts to ensure AI systems support—not replace—human expertise. This thrust also emphasizes fairness, accessibility and cultural sensitivity so that AI innovations benefit diverse learners and communities.
AIR Priority Initiatives
- Developing state-of-the-art AI-enabled autonomous systems to enhance capabilities in navigation safety and automatic decision-making.
- Developing AI techniques to identify security vulnerabilities, enhance detection techniques and implement robust countermeasures against cyber-attacks across various platforms and networks.
- Developing AI-enabled systems for inspecting, monitoring and maintaining critical infrastructure.
- Utilizing AI to improve diagnosis and prognosis of diseases such as Alzheimer’s and cancer, aiming to enhance patient care and treatment.
- Applying AI methods to strengthen the understanding and solve complex problems in physics, chemistry and geology.
- Develop intelligent learning systems and technologies to transform educational methodologies, personalize learning experiences and enhance educational outcomes.