Lecturers

Short biography

Wojciech Samek is a professor at the Technical University of Berlin and is jointly heading the AI Department at Fraunhofer Heinrich Hertz Institute. Hestudied computer science in Berlin and Edinburgh, was a visiting researcher at the NASA Ames Research Center, Mountain View, USA, and received the Ph.D. degree with distinction from TU Berlin in 2014. He then founded the Machine Learning Group at Fraunhofer HHI, which he headed until 2020. Dr. Samek is Fellow at BIFOLD - Berlin Institute for the Foundation of Learning and Data and associated faculty at the ELLIS Unit Berlin and the DFG Graduate School BIOQIC. Furthermore, he is a senior editor of IEEE TNNLS, an editorial board member of PLoS ONE and Pattern Recognition, and an elected member of the IEEE MLSP Technical Committee. He is recipient of multiple best paper awards, including the 2020 Pattern Recognition Best Paper Award, and part of the expert group developing the ISO/IEC MPEG-17 NNR standard. He is the leading editor of the Springer book "Explainable AI: Interpreting, Explaining and Visualizing Deep Learning" (2019), co-editor of the open access Springer book “xxAI – Beyond explainable AI” (2022), and organizer of various special sessions, workshops and tutorials on topics such as explainable AI, neural network compression, and federated learning. Dr. Samek has co-authored more than 150 peer-reviewed journal and conference papers; some of them listed by Thomson Reuters as "Highly Cited Papers" (i.e., top 1%) in the field of Engineering.

Associate professor at the Computer Science Department of the University of Pisa

Short biography

Anna Monreale is an associate professor at the Computer Science Department of the University of Pisa and a member of the Knowledge Discovery and Data Mining Laboratory (KDD-Lab), a joint research group with the Information Science and Technology Institute of the National Research Council in Pisa. She has been a visiting student at Department of Computer Science of the Stevens Institute of Technology (Hoboken, NewJersey, USA) (2010). Her research interests include big data analytics, social networks and the privacy issues raising in mining these kinds of social and human sensitive data. In particular, she is interested in the evaluation of privacy risks during analytical processes and in the design of privacy-by-design technologies in the era of big data. She earned her Ph.D. in computer science from the University of Pisa in June 2011 and her dissertation was about privacy-by-design in data mining.

Researcher with the KDD Lab, ISTI-CNR, Italy

Short biography

Salvatore Rinzivillo is a Researcher with the KDD Lab, ISTI-CNR, Italy. His main research interests include explainable AI, data mining, and visual analytics for spatial, spatiotemporal, and mobility data. He has participated in a number of prestigious European projects related to complex systems, society and explainable artificial intelligence.

Short biography

Sofia Triantafyllou is an Assistant Professor in the Department of Mathematics and Applied Mathematics in the University of Crete. She has studied Applied Mathematics at NTUA and has a PhD in Computer Science from the University of Crete. Before joining the University of Crete, she was an Assistant Professor in the University of Pittsburgh. Her research involves causal discovery and inference from multiple sources of data, and applications of causal inference.

PhD candidate in Computer Science at TU Darmstadt

Short biography

Matej is a Ph.D. Candidate in Computer Science under Prof. Kristian Kersting at TU Darmstadt. His research involves neural causal inference, and role of causality in XAI and Machine Learning.

Short biography

Rita Nogueira completed her academic course at the Instituto de Engenharia da Universidade do Porto (ISEP) with a master's degree in Computer Systems. In 2017 she enrolled the doctoral program in computer science at Faculdade de Ciências da Universidade do Porto. Her research focuses on causality, specifically on understanding how we can retrieve causal relationships from observational data more efficiently, and its applications in real-world solutions.

Head, Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia

Short biography

Sašo Džeroski is head of the Department of Knowledge Technologies at the Jozef Stefan Institute (Ljubljana, Slovenia) and a full professor at the Jozef Stefan International Postgraduate School. He is also a visiting professor at the European Space Agency (Frascati, Italy). His department develops artificial intelligence methods for machine learning and decision support and uses them to solve practical problems from agriculture and environmental sciences, medicine and life sciences, and space operations/Earth observation. His own research focuses on machine learning from complex data and in the presence of domain knowledge, also in the case of learning models of dynamical systems.

Short biography

Anna Hedström is currently pursuing her PhD at the Machine Learning Group at Technische Universität Berlin, where she is a part of the independent research group, Understandable Machine Intelligence Lab (UMI Lab), which focuses on eXplainable AI topics. AH received her MSc degree in Machine Learning at the Royal Institute of Technology (KTH) and studied Engineering for her bachelor's at University College London (UCL). Her current research interests include XAI, Deep Learning and in particular, Evaluation of XAI methods. Previously, AH held teaching- and research assistantship positions and worked in ML-related positions in companies such as Klarna, Bosch Software Innovations, BCG and other ML start-ups. AH is a co-organiser of Women in ML and DS (WiMLDS) meet-ups in Berlin.

Short biography

Christopher J. Anders is pursuing his Ph.D. since 2018 at Machine Learning Group at Technische Universität Berlin, and in the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He received his M.Sc. in 2018 in computer science at Technische Universität Berlin, with a focus on machine learning and computer security. His research topics include (adversarial-/) explainable machine learning, software for machine learning and deep neural networks.