Speakers

Richard Wilson received the B.A. degree in physics from the University of Oxford, and a PhD in computer science from the University of York in 1996. He is currently a Professor with the Department of Computer Science, University of York where he is head of the AI research group. He has authored more than 300 articles in journals, edited books, and refereed conferences with more than 6000 citations and an h-index of 38. He has chaired BMVC and SSPR and was associate editor of Pattern Recognition for a number of years. His research interests include structural pattern recognition, graph methods for computer vision, and machine learning with graphs. Professor Wilson is also a Fellow of the International Association for Pattern Recognition (IAPR).

Emanuele Rodolà is a Full Professor of Computer Science at Sapienza University of Rome, where he leads the GLADIA group focusing on Geometry, Learning, Audio, and Applied AI. His work in this area is funded by an ERC Starting Grant and a Google Research Award. Before his current position, he served as an Assistant and then Associate Professor at Sapienza (2017-2020), a postdoc at USI Lugano (2016-2017), an Alexander von Humboldt Fellow at TU Munich (2013-2016), and a JSPS Research Fellow at The University of Tokyo (2013), in addition to visiting periods at Tel Aviv University, Technion, Ecole Polytechnique, and Stanford. He is a fellow of ELLIS and the Young Academy of Europe. Prof. Rodolà has received several awards for his research, he has been active in the academic community, serving on program committees and as an area chair for leading conferences in computer vision, machine learning, and graphics (CVPR, ICCV, ICLR, NeurIPS, etc.). His current research mainly focuses on neural model merging, representation learning, ML for audio, and deep multimodal learning, and has published around 150 papers in these areas.

Nils M. Kriege is associate professor and leader of the work group "Machine Learning with Graphs" at the Faculty of Computer Science at the University of Vienna. He received his PhD from the TU Dortmund University in 2015, was a visiting researcher at the University of York, and held an interim professorship for Algorithm Engineering at the TU Dortmund University. In 2019 he was awarded a WWTF Vienna Research Group for the project "Algorithmic Data Science for Computational Drug Discovery" and joined the University of Vienna in 2020. His research focuses on developing methods for data mining and machine learning with graphs by solving problems at the boundaries of machine learning, graph theory, and algorithmics. He contributes techniques to the broad topics of graph embedding, graph matching, and graph search in large databases. His ambition is to develop methods that are useful for solving concrete problems in real-world applications, especially in computational drug discovery.