Rate My Professor Theo Damoulas

Professor Theo Damoulas is a Professor in Machine Learning and Statistics at the University of Warwick, with a joint appointment in the Departments of Computer Science and Statistics. He holds a PhD in Probabilistic Multiple Kernel Learning from the University of Glasgow (2009), an MSc in Informatics with Distinction from the University of Edinburgh (2004), and an MEng in Mechanical Engineering with First Class Honours from the University of Manchester (2003). His career trajectory includes Research Assistant Professor at New York University's Center for Urban Science and Progress (2013-2015), Postdoctoral Research Associate and Research Associate in Computer Science at Cornell University (2009-2013), Assistant Professor at Warwick (2015-2018), Associate Professor (2018-2021), and full Professor since 2018. He currently heads the Foundations in AI and ML Division in Computer Science, serves as a UKRI Turing AI Fellow (2021-2026) leading research on the Machine Learning Foundations of Digital Twins, is an ELLIS member, and holds a Visiting Exchange Professor position at NYU CUSP. Damoulas founded and leads the cross-departmental Warwick Machine Learning Group and previously served as deputy director of the Data-Centric Engineering programme at The Alan Turing Institute, where he led projects such as Project Odysseus and the London Air Quality project.

His research specializes in probabilistic machine learning and Bayesian statistics, focusing on integrating structured priors, spatiotemporal dependencies, dynamics, compositions, physical laws, flows, and causality, alongside advancing robust and scalable approximate inference methods. Applications include Digital Twins, Bayesian nonparametrics, and spatiotemporal causal inference in urban science, justice, and computational sustainability, with an emphasis on understanding data generating processes in socio-technical ecosystems. Damoulas has received the 2024 Wilkes Award for Best Paper from The Computer Journal, the 2022 AISTATS Best Paper Award, the 2017 ACM SIGMOD Most Reproducible Paper Award, the UKRI Turing AI Acceleration Fellowship, the 2018 Warwick Impact Fund Award, and the 2018 Turing Reproducible Research Award, among others. He has served as Associate Editor for Bayesian Analysis (2021-2023), Royal Society Open Science (2021-2023), and Data-Centric Engineering (2019-2020), contributed to numerous conference and journal reviews, and organized workshops including ICASSP 2018 on Wildlife Bioacoustics and AAAI 2015 on AI for Cities. His supervision has produced multiple award-winning PhD theses, enhancing impact in academia and industry.