Institute for Artificial Intelligence

About Us

The Institute for Artificial Intelligence + X is a university wide research and education center for Artificial Intelligence. It has a focus on collaboration across disciplines.

The goal is to establish a world-class academic research and development (R&D) center at the University of South Florida to conduct externally-funded research in Artificial Intelligence (AI) and associated areas (X = Healthcare, Medicine, Biology, Cybersecurity, Finance, Business, Manufacturing, Transportation), using a transdisciplinary approach across Neuroscience, Cognitive Science, and Computer Science, and work with industry to transition them into products that benefit humanity in an ethical and responsible manner.

There are faculty from many departments associated with the center including Computer Science and Engineering, Electrical Engineering, Industrial Engineering, Integrated Biology, Genomics, Psychology, Public Health, Medicine, and Information Systems Decision Sciences.

The Institute is co-directed by Prof. Sudeep Sarkar ( and Prof. Lawrence Hall (


Info: Friday 1-2pm at ENB118

Full schedule: Spring23 Fall22 Spring22 Fall21 Spring21

  • Apr. 21, 2023 - Mohammad Noroozi: An AI-Assisted Systematic Literature Review of the Impact of Vehicle Automation on Energy Consumption

  • Apr. 7, 2023 - Imran Hossain: Explainable Artificial Intelligence (XAI)

  • Fev. 17, 2023 - John Licato: ChatGPT, Cheating, and Chaos: What All Educators Need to Know, and What's Next

  • Fev. 3, 2023 - Nikolai Fetisov: Survival Time Prediction from Unannotated Lung Cancer Histopathology Images

  • Jan. 27, 2023 - Yili Ren, FSU: Intelligent Wireless Systems: from Human Sensing to Object Detection

  • Jan. 20, 2023 - Harlin Lee, UCLA: Learning with graphs and networks: from theory and methods to applications in social science

  • Dec. 2, 2022 - Julia Woodward: The Disconnect Between Children's Expectations and Interactions With Intelligent Systems

  • Nov. 18, 2022 - Tyree Lewis: Brain Authentication: A P300 Approach