[01/03/24]The Bayes Centre Announces Latest Turing Fellows Appointments

Francesco Tudisco studied Computer Science and Mathematics at the University of Rome (Italy) and obtained a PhD in Computational Mathematics from the same University in December 2015. He then moved as a postdoc to Saarbruecken (Germany) and as a Marie Curie Individual Fellow at the University of Strathclyde soon afterwards. Before joining the University of Edinburgh as Reader in Machine Learning, he was Associate Professor of Numerical Analysis at the Gran Sasso Science Institute graduate school in L'Aquila (Italy).

Francesco’s research interests lie at the intersection between spectral theory, numerical analysis, network science and machine learning. His recent work includes the use of model-order reduction techniques from matrix and tensor differential equations to design efficient training algorithms for deep learning models, the numerical stability of deep neural networks and neural PDEs, the analysis of neural networks in the infinite width and infinite depth limits, nonlinear spectral theory with applications to machine learning on graphs, and the use of physics-informed and physics-inspired deep learning models in scientific simulations.

Giulia De Togni is an interdisciplinary social scientist specializing in Science and Technology Studies, holding degrees in Social Anthropology (PhD, MSc), Japanese Studies (MSt, MPhil), and Legal Studies (BA). Recently awarded a Chancellor’s Fellowship at CMVM/Usher, she started this new position in January 2024, after having worked at Usher since 2019 on the Wellcome Trust-funded project “AI and Health,” and since 2022 as Principal Investigator on her Wellcome Trust Fellowship “Caring Machines.” For her Chancellor’s Fellowship, Giulia will continue her work on Responsible AI and Robotics Innovation in Healthcare which aims to enable different stakeholders to become part of the co-production of healthcare technologies to inform and shape innovation together.

Giulia is a Visiting Researcher at Edinburgh Centre for Robotics/Bayes; a Postdoctoral Affiliate of EFI; and she closely collaborates with teams of roboticists at the School of Engineering, Heriot-Watt University, National Robotarium, Alan Turing Institute, and Bristol Robotics Laboratory. Internationally, Giulia is PI of a project funded by JST (Japan’s Science and Technology Agency); closely collaborates with AIST (Japan’s National Institute of Advanced Industrial Science and Technology) and RIKEN (the largest research institute in Japan, with 3,000+ members). She is also a member of the INNOVCARE group (École des Hautes Études en Sciences Sociales); closely collaborates with roboticists based at KAIST (Korea Advanced Institute of Science and Technology); and is a Postdoctoral Affiliate of the IIT (Italian Institute of Technology).

Sara Wade is a Reader in Statistics and Data Science in the School of Mathematics, University of Edinburgh. Her research interests include statistics, machine learning, Bayesian analysis, with a focus on flexible methodology and efficient inference for complex data. She was co-Director of the Centre for Statistics (2019-2022), on the board of the International Society of Bayesian Analysis (ISBA) 2022-2025 and has served as Area Chair for Women in Machine Learning (WiML) workshops. Her work has been recognized by prizes including Best Doctoral Thesis by the Italian Statistical Society, Lindley Prize by ISBA, and Young Biometrician Award by the International Biometric Society.

Before joining the School of Mathematics, she was a Harrison Early Career Assistant Professor at the Department of Statistics, University of Warwick (2015-2018). She was a postdoctoral researcher in Machine Learning at the Computational and Biological Learning Laboratory, University of Cambridge working with Prof. Zoubin Ghahramani. She earned her PhD in Statistics in 2013 from Bocconi University, under the supervision of Prof. Sonia Petrone and Prof. Stephen Walker.