Two Univ Fellows, Christopher MacMinn, Supernumerary Fellow in Engineering Science, and Patrick Rebeschini, Tutorial Fellow in Statistics, have been awarded the title of Professor in the University’s latest Recognition of Distinction exercise.

Christopher MacMinn, Supernumerary Fellow in Engineering Science, is now Professor of Engineering Science. Chris’s background is in mechanical engineering, with a specialization in fluid mechanics from the interdisciplinary perspectives of engineering, hydrology, and applied mathematics. He earned his SB (2005) from the Department of Mechanical Engineering at the Massachusetts Institute of Technology, after which he spent about one year in engineering consulting. In 2008, Chris returned to MIT to earn his SM and PhD in mechanical engineering, working with Professor Ruben Juanes on the fluid mechanics of geological carbon dioxide storage.

Christopher MacMinn, Supernumerary Fellow in Engineering Science

Chris then went to the Yale Climate and Energy Institute at Yale University as a Postdoctoral Fellow where he worked with Professors John Wettlaufer and Eric Dufresne on the poromechanics of soft granular materials. At Oxford, Chris leads the Poromechanics Laboratory, an interdisciplinary team of engineers, physicists, mathematicians, and earth scientists. They use mathematical modelling, numerical simulations, and high-resolution laboratory experiments to study flow, transport, and deformation in porous media and other multiphase systems for applications in subsurface science and engineering, soft materials, and biology and medicine. He serves as a member of the EPSRC Peer Review College.

Patrick Rebeschini, Tutorial Fellow in Statistics

Patrick Rebeschini, Tutorial Fellow in Statistics, has been awarded the title of Professor of Statistics and Machine Learning.

Before joining the University of Oxford, Patrick was a Lecturer in the Computer Science department at Yale University and a Postdoctoral Associate at the Yale Institute for Network Science, hosted by Sekhar Tatikonda. He holds a Ph.D. in Operations Research and Financial Engineering from Princeton University, where he worked in probability theory under the supervision of Ramon van Handel.

Patrick is interested in the investigation of fundamental principles in high-dimensional probability, statistics and optimization to design computationally efficient and statistically optimal algorithms for machine learning. In 2022 and 2023, he served as Area Chair for COLT. He is a Co-Investigator for the Imperial-Oxford StatML Centre for Doctoral Training (CDT). He is a Fellow at the Alan Turing Institute London as well as a member of the Bernoulli Society, Institute of Mathematical Statistics (IMS), and European Laboratory for Learning and Intelligent Systems (ELLIS).

Published: 3 October 2023

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