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 (sarkar@usf.edu) and Prof. Lawrence Hall (lohall@mail.usf.edu).

Seminars

Info: Friday 1-2pm at ENB118

  • Apr. 19, 2024 - Mauricio Pamplona: Unveiling Gender Effects in Gait Recognition using Conditional-Matched Bootstrap Analysis

  • Apr. 12, 2024 - Alfredo Fernandez: Stable and Robust Deep Learning By Hyperbolic Tangent Exponential Linear Unit

  • Apr. 5, 2024 - Neisarg Dave (Penn State): Building Foundations of Neurosymbolic and Verifiable AI: A stability and precision perspective

  • Mar. 29, 2024 - Hitesh Vaidya: Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps

  • Mar. 22, 2024 - Dr. Danny Silver (Acadia U): The Roles of Symbols in Neural-based AI: They are Not What You Think!

  • Mar. 8, 2024 - Cole Hill: Improving Silhouette based Gait Recognition using 3D Human Poses

  • Mar. 1, 2024 - Ramy Mounir: Learning Hierarchical Event Representations from Streaming Video

  • Fev. 23, 2024 - Gilbert Rotich: Comparative Study of Self-Supervised Pretrained Models for Land Use Classification in Satellite Images