Andreas Fischer received the M.S. and Ph.D. degrees in Computer Science from the University of Bern, Switzerland, in 2008 and 2012, respectively. Afterward, he conducted postdoctoral research in Montreal, Canada, at the Centre for Pattern Recognition and Machine Intelligence (CENPARMI) and at Polytechnique Montreal. Currently, he is Full Professor at the University of Applied Sciences and Arts Western Switzerland (HES-SO, HEIA Fribourg) and Lecturer at the University of Fribourg, Switzerland. Andreas Fischer’s research interests include pattern recognition, deep learning, graph-based methods, document analysis, and handwriting recognition. He has published over 100 peer-reviewed articles in international journals and conference proceedings on these topics. Andreas Fischer is a member of the governing board of the International Association of Pattern Recognition (IAPR), where he represents Switzerland, and Chair of the IAPR technical committee on reading systems (TC11).

Benoit Gaüzère is an Associate Professor in Machine Learning, conducting research within the LITIS lab and teaching at INSA Rouen Normandie. His work focuses on graph data representation learning and developing machine learning methods for graphs. His recent research explores learning edit costs for graph edit distance, improving pooling layers in Graph Neural Networks (GNNs), and addressing the preimage problem in graph-based models. He actively applies these methods to chemoinformatics. He is involved in multiple research projects, including FAMOUS (fair prediction models, with a focus on graphs), CodeGNN (advancing GNNs by improving convolution, pooling and GNNs on temporal graphs), and AGAC (applying graph-based ML to chemistry). His publications and implementations are available on Google Scholar, HAL, and GitHub.