Keynote Speakers

Professor at the University of Amsterdam, the Netherlands

Bio

Theo Gevers is a professor of computer vision at the University of Amsterdam. He is director of the Computer Vision Lab and co-director of the Atlas Lab (UvA-TomTom) and Delta Lab (UvA-Bosch) in Amsterdam. His research area is artificial intelligence with the focus on computer vision and deep learning, and in particular image processing, 3D (object) understanding and human-behavior analysis with industrial and societal applications. He is the co-founder of 3DUniversum, Scanm B.V. (Sold in 2020), and Sightcorp (Sold in 2022).

Abstract for presentation

Coloring (generative) AI

Professor at Georgia Institute of Technology, US

Bio

Prof. AlRegib is currently the John and Marilu McCarty Chair Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. His group is the Omni Lab for Intelligent Visual Engineering and Science (OLIVES) at Georgia Tech. In 2012, he was named the Director of Georgia Tech’s Center for Energy and Geo Processing (CeGP). He is the director of the Center for Signal and Information Processing (CSIP). He also served as the Director of Georgia Tech’s Initiatives and Programs in MENA between 2015 and 2018. He has authored and co-authored more than 300 articles in international journals and conference proceedings. He has been issued several U.S. patents and invention disclosures. He is a Fellow of the IEEE.

Prof. AlRegib received the ECE Outstanding Graduate Teaching Award in 2001 and both the CSIP Research and the CSIP Service Awards in 2003. In 2008, he received the ECE Outstanding Junior Faculty Member Award. In 2017, he received the 2017 Denning Faculty Award for Global Engagement. Prof. AlRegib has provided services and consultation to several firms, companies, and international educational and R&D organizations. He has been a witness expert in several patents’ infringement cases.

His research group is working on projects related to machine learning, image and video processing and understanding, subsurface imaging, perception, and healthcare intelligence. The primary applications of the research span from Autonomous Vehicles to Portable AI-based Ophthalmology and Eye Exam and from Microscopic Imaging to Seismic Interpretation. The group has produced a number of large-scale datasets; examples can be found at https://alregib.ece.gatech.edu/.

Abstract for presentation

Visual Explanations in AI

Visual explanations have traditionally acted as rationales used to justify the decisions made by machine learning systems. With the advent of large-scale neural networks, the role of visual explanations has been to shed interpretability on opaque models. We view this role as the process for the network to answer the question `Why P?’, where P is a trained network’s prediction. Recently however, with increasingly capable models, the role of explainability has expanded. Neural networks are asked to justify `What if?’ counterfactual and `Why P, rather than Q?’ contrastive question modalities that the network did not explicitly train to answer. This allows explanations to act as reasons to make further prediction. The talk provides a principled and rational overview of Explainability within machine learning and justifies them as reasons to make decisions. Such a reasoning framework allows for robust machine learning as well as trustworthy AI to be accepted in everyday lives. Applications like robust recognition, image quality assessment, visual saliency, anomaly detection, out-of-distribution detection, adversarial image detection, seismic interpretation, semantic segmentation, introspection, and machine teaching among others will be briefly discussed.