Lydia Y. Chen is a Professor in the Department of Computer Science at the University of Neuchatel in Switzerland and Delft University of Technology in the Netherlands. Prior to joining TU Delft, she was a research staff member at the IBM Research Zurich Lab from 2007 to 2018. She holds a PhD from Pennsylvania State University and a BA from National Taiwan University. Her research interests are distributed/federated machine learning systems, generative AI, and dependability and privacy enhancing technologies. More specifically, her work focuses on developing machine learning and stochastic models, and applying these techniques to application domains, such as data centers, edge systems, semi-conductor and material science.

She has published more than 100 papers in peer-reviewed journals, including IEEE Transactions on Distributed Systems and IEEE Transactions on Service Computing, and in conference proceedings, including ICML, ICDE, MobiCom, DSN, and EUROSYS. She was a co-recipient of the best paper awards at CCGrid’15 and eEnergy’15. She received TU Delft technology fellowship in 2018. She was program co-chair for IEEE IC2E 21, IEEE ICAC 2019, Middleware Industry track 2018, track vice-chair for ICDCS 2018, and DIAS 2017. She serves on the editorial boards of IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Service Computing and IEEE Transactions on Network and Service Management. She is an IEEE senior member.

Michael Kamp is Associate Professor for Machine Learning and Artificial Intelligence at TU Dortmund University and a faculty member of the Lamarr Institute for Machine Learning and Artificial Intelligence. His research spans the theoretical foundations of deep learning, causal representation learning, and trustworthy machine learning, with a particular focus on federated learning and privacy-preserving optimization. He develops machine learning methods that are not only mathematically rigorous but also designed to meet the demands of high-stakes real-world applications, particularly in healthcare and medicine. He is also affiliated with the Institut für KI in der Medizin (IKIM) at the University Medicine Essen, where he previously led the research group Trustworthy Machine Learning. He continues to collaborate closely with IKIM on cutting-edge medical AI research at the intersection of clinical practice, data privacy, and reliable machine learning. Earlier in his career, Michael was a postdoctoral researcher at the CISPA Helmholtz Center for Information Security in the Exploratory Data Analysis group of Jilles Vreeken (2021), and from 2019 to 2021 a postdoctoral fellow at Data Science & AI Department at Monash University and the Monash Data Futures Institute, where he remained an associated research fellow until 2024. Prior to that, he spent nearly a decade at Fraunhofer IAIS as data scientist, where he led the institute’s contributions to the EU project DiSIEM and headed a small applied research team working at the interface of academic research and industrial deployment. He also advised and trained corporate partners such as Volkswagen and DHL on data-driven innovation. Michael Kamp received his doctorate from the University of Bonn, where he taught graduate seminars and supervised numerous theses. Prior to entering academia, he worked for over a decade as a professional software developer. He is a member of the editorial board of the Springer journal Machine Learning and a member of the ELLIS society.