Mark Girolami

Mark Girolami is a Computational Statistician having ten years experience as a Chartered Engineer within IBM. In March 2019 he was elected to the Sir Kirby Laing Professorship of Civil Engineering (1965) within the Department of Engineering at the University of Cambridge where he also holds the Royal Academy of Engineering Research Chair in Data Centric Engineering. Girolami takes up the Sir Kirby Laing Chair upon the retirement of Professor Lord Robert Mair. Professor Girolami is a fellow of Christ’s College Cambridge.

Prior to joining the University of Cambridge Professor Girolami held the Chair of Statistics in the Department of Mathematics at Imperial College London. He was one of the original founding Executive Directors of the Alan Turing Institute the UK’s national institute for Data Science and Artificial Intelligence, after which he was appointed as Strategic Programme Director at Turing, where he established and led the Lloyd’s Register Foundation Programme on Data Centric Engineering. Since October 2021 he serves as the Chief Scientist of the Alan Turing Institute.

Professor Girolami is an elected fellow of the Royal Society of Edinburgh, he was an EPSRC Advanced Research Fellow (2007-2012), an EPSRC Established Career Research Fellow (2012-2018), and a recipient of a Royal Society Wolfson Research Merit Award.

He delivered the IMS Medallion Lecture at the Joint Statistical Meeting 2017, and the Bernoulli Society Forum Lecture at the European Meeting of Statisticians 2017.

In 2020 Professor Girolami delivered the BCS and IET Turing Talk in London, Manchester, and Belfast.

Professor Girolami currently serves as the Editor-in-Chief of Statistics and Computing and the new open access journal Data Centric Engineering published by Cambridge University Press.

Narges Razavian

Narges is an assistant professor in the Departments of Population Health and Radiology conducting research in the Center for Healthcare Innovation and Delivery Science (CHIDS), and a member of its Predictive Analytics Unit.

Her lab's research is focused on the intersection of machine learning, artificial intelligence, and medicine. Using millions of records in the Electronic Health Records database at NYU Langone, as well as hundreds of thousands of imaging and millions of genomic data points, they focus on a number of important topics, including but not limited to: prediction of upcoming preventable conditions and events using machine learning and data science, discovery of disease subtypes using radiology and pathology imaging and electronic records, discovery of existing but undiagnosed medical conditions using electronic health records, and, the discovery of biomarkers and factors associated with important outcomes, etc. 

Gitta Kutyniok

Gitta Kutyniok completed her Diploma in Mathematics and Computer Science in 1996 at the Universität Paderborn in Germany. She was then employed as a Scientific Assistant and in 2000 received her Ph.D. degree in the area of time-frequency analysis from the same university. In 2001, she spent one term as a Visiting Assistant Professor at the Georgia Institute of Technology. After having returned to Germany, she accepted a position as a Scientific Assistant at the Justus-Liebig-Universität Giessen. In 2004, she was awarded a Research Fellowship by the DFG-German Research Foundation, with which she spend one year at Washington University in St. Louis and at the Georgia Institute of Technology. She then returned to Germany, completed her Habilitation in Mathematics in 2006 and received her venia legendi. In 2007 and 2008, being awarded one of the highly competitive “Heisenberg Fellowships” by the DFG-German Research Foundation, she spent half a year at each, Princeton University, Stanford University, and Yale University. After returning to Germany in October 2008, she became a full professor for Applied Analysis at the Universität Osnabrück. Gitta Kutyniok was awarded various prizes for both her teaching and research, among which were the “Weierstrass Prize for outstanding teaching of the Universität Paderborn” in 1998, the “Research Prize of the Universität Paderborn” in 2003 as well as the “Prize of the University Gießen” in 2006. Just recently, in 2007, she received the prestigious “von Kaven Prize” awarded annually by the DFG-German Research Foundation. 

Since 2007, she is an Associate Editor for the Journal of Wavelet Theory and Applications, and since 2009, she is a Corresponding Editor for Acta Applicandae Mathematicae. She was a panelist for the NSF in 2008 and serves as a reviewer for the NSF, GIF, NWO, WWTF as well as for over 30 journals. 

Her research interests include the areas of applied harmonic analysis, numerical analysis, and approximation theory, in particular, sparse approximations, compressed sensing, geometric multiscale analysis, sampling theory, time-frequency analysis, and frame theory with applications in signal and image processing.

Aasa Feragen

Aasa Feragen is a rogue mathematician who has worked at the intersection of machine learning and medical imaging since 2009. She is also a professor at the Technical University of Denmark. Her MSc and PhD in mathematics are both from the University of Helsinki, following which she held postdocs at the University of Copenhagen, and the MPI for Intelligent Systems in Tübingen. 

While interpretability and explainability have been at the heart of Aasa's research from the start, her more recent interests include uncertainty modelling and algorithmic fairness. Her work ranges from quantification and communication of uncertainty in brain imaging, via mathematical modelling for algorithmic fairness in medicine, to developing explainable AI algorithms for clinical use.