At the School of Computing and Augmented Intelligence, part of the Ira A. Fulton Schools of Engineering, our faculty team is working on cutting-edge artificial intelligence, or AI, research. They are leading the discussion on the impact of this new technology and guiding the next generation of engineering students toward successful careers in an exciting, emerging field.
With more than two dozen faculty members actively engaged in artificial intelligence instruction, research and development, our team oversees a program ranked No. 21 in artificial intelligence by U.S. News and World Report.
The artwork for this post was generated using ECLIPSE, an AI-art creation tool created by Maitreya Patel, a doctoral student working under the supervision of Associate Professor Yezhou Yang in ASU’s Active Perception Group lab. The team will present their paper and demonstrate the tool at the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 in Seattle in June.
Our leadership voices
Ross Maciejewski, Director, School of Computing and Augmented Intelligence
As an expert on spatiotemporal analysis and data visualization, Ross Maciejewski has been tapped for his expertise in creating visual analytics for homeland security, public health, social media and law enforcement. He is emerging as an important voice on the development of ethical artificial intelligence.
AI research interests:
Explainable AI
Fairness in AI
Human-computer interaction
Baoxin Li, Associate Director, School of Computing and Augmented Intelligence
A Fulton Exemplar Faculty member, past winner of the SHARP Labs’ Inventor of the Year Award and holder of sixteen U.S. patents, Baoxin Li is an expert currently researching how to harness the power of artificial and augmented intelligence to create assistive technologies for the visually impaired.
AI research interests:
Visual computing and machine learning
Human-centered computing
Subbarao Kambhampati, AAAI Fellow, Past AAAI President
A national thought leader on artificial intelligence, Subbarao Kambhampati is frequently called to be a leading voice on the societal impacts on the advances in AI by local and national media outlets, as well as to address policy makers such as the Arizona Supreme Court. He writes an AI column for The Hill, is a past trustee of International Joint Conference on Artificial Intelligence and is a founding board member of Partnership on AI.
AI research interests:
Human-AI interaction
AI planning and decision making
Reinforcement learning
Cooperative AI systems
Reasoning and planning with large language models
Huan Liu, AAAI Fellow
A Regents Professor and Fellow of the ACM, AAAS, and IEEE, Huan Liu is a past winner of the ASU President’s Award for Innovation. He works to investigate interdisciplinary problems that arise in many real-world, data-intensive applications with high-dimensional data of disparate forms such as social media. His well-cited published works include books, book chapters, encyclopedia entries as well as conference and journal papers.
AI research interests:
Augmented intelligence
Social computing
Social media analysis
Teresa Wu
The Fulton Schools associate dean for global engagement, Teresa Wu also serves as the founding co-director of the ASU-Mayo Center for Innovative Imaging and a health solutions ambassador for the College of Health Solutions. Much of her recent work focuses on deep machine learning techniques for MRI scans in the hope of improving the early detection and effective treatment of diseases like Alzheimer’s.
AI research interests:
AI in medical imaging
Deep learning for medical imaging
Chitta Baral
A past associate editor of both the Journal of AI Research and the AI Journal, Chitta Baral is a recognized authority on knowledge representation. He is the author of the book, Knowledge Representation, Reasoning and Declarative Problem Solving, published by Cambridge University Press. He is also the past president of KR Inc., a not-for-profit scientific foundation that fosters research and communication on knowledge representation and reasoning.
AI research interests:
Natural language processing
Generative AI
Machine learning
Autonomous agents
AI in medicine
Stephanie Forrest
The founding director of ASU’s Biodesign Center for Biocomputing, Security and Society and a Fellow of the IEEE, Stephanie Forrest is a past ACM/AAAI Allen Newell Award winner and a recipient of the Presidential Young Investigator Award. She is currently researching machine learning inspired single molecule biomarker detection systems under a grant from the National Science Foundation.
AI research interests:
Machine learning for biodesign
Bio-inspired computing
Automated software repair
Our faculty with a focus on AI research
Ajay Bansal
Knowledge representation and reasoning
Automated reasoning
Machine learning
Heni Ben Amor
Machine learning
Human-robot interaction
Deep learning
YooJung Choi
Trustworthy AI and machine learning
Robustness, fairness and explainability in AI
Hasan Davulcu
Sociocultural modeling and persuasive AI
Social Media and Web Mining
Yanjie Fu
Spatial-temporal AI
Graph learning
Reinforcement learning
Data-centric AI
Adaptive and interactive machine learning
Nakul Gopalan
Reinforcement learning
Natural language processing
Rakibul Hasan
Applied machine learning
Hannah Kerner
Use of AI and NASA satellite data to develop Large Earth Models
Machine learning
Hokeun Kim
AI on edge devices
Secure federated learning
AI and cyber-physical systems
Kookjin Lee
Scientific machine learning
Deep learning for computational physics and applied mathematics
Deep learning for dynamical systems and spatiotemporal process modeling
Rong Pan
Human-AI Systems in a manufacturing environments
Paulo Shakarian
Neurosymbolic AI
Military applications of AI
Metacognitive AI
Yan Shoshitaishvili
AI for cybersecurity education
Aviral Shrivastava
Machine learning accelerator frameworks
Intelligent transportation and autonomous vehicles
Siddharth Srivastava
Planning and learning for reliable autonomous agents
Safe and reliable AI systems
Safety assessment of AI systems
Hua Wei
Trustworthy reinforcement learning
Multi-agent reinforcement learning
Machine learning
Yezhou “YZ” Yang
Image generative AI
Secure generative AI
Visual reasoning
Concept learning
Cognitive robotics
Yingzhen Yang
Deep learning and statistical machine learning
Optimization for machine learning
Theoretical deep learning and applications to computer vision, data mining and medical imaging
Yu Zhang
Planning and automated reasoning
Reinforcement learning
Multi-agent systems
Jia Zou
Data integration using large language models
Machine learning systems
Database systems
Applying deep learning to database systems
Federated data management and data integration