Three more Oxford academics selected to be Alan Turing Institute Fellows

The Alan Turing Institute is the national institute for data science and AI, whose purpose is to further research in these areas to tackle societal challenges. The Turing Fellowship Scheme aims to develop the data science and AI ecosystem in the UK by supporting, retaining, and developing the careers of the next generation of world-leading researchers.

Professor Mark Girolami, Chief Scientist at The Alan Turing Institute, said: ‘I’m delighted to welcome a new cohort of Turing Fellows, brought to us from across our University Network in recognition of their status as the next generation of world-leading researchers in the data sciences, AI and related fields. I’m very much looking forward to seeing the immense value they will add to our diverse and vibrant science and innovation community, including playing a critical role in the delivery of the Turing’s strategy as we strive to change the world for the better through data science and AI.’

About the new Oxford Turing Fellows:

I am honoured to be elected a Turing Fellow. This is a great opportunity to engage with like-minded experts and stake holders on aspects of AI and Sustainability. I hope it would enable a step change towards Net-Zero Computing.

Associate Professor Noa Zilberman, Department of Engineering Science.

Noa Zilberman is an Associate Professor in the Department of Engineering Science, where she leads the Computing Infrastructure Group, aiming to build sustainable, scalable and resilient computing infrastructure. Her research explores both Systems for AI and AI for systems. She has led important contributions to in-network machine learning, offloading machine learning to run within programmable network devices, and applying this to address problems within cyber-security, finance and smart environments. Alongside her work on AI, Professor Zilberman also researches how to improve ICT sustainability, particularly the effect of micro-architectures on large-scale systems’ carbon and energy efficiency. Her current efforts are focused on carbon-aware networking, to reduce the carbon footprint of the Internet.

The Turing fellowship will allow me to focus on the theoretical questions at the heart of foundation models, as well as collaborate with researchers in the Turing networks regarding algorithmic questions that arise from the focus on privacy, interpretability, fairness and truthfulness of these models.

Associate Professor Varun Kanade, Department of Computer Science

Varun Kanade is an Associate Professor in the Department of Computer Science, whose research is based in the foundations of machine learning. A particular focus is optimization algorithms for learning, as well as statistical and computational learning theory. His recent work has included deep learning with a focus on adversarial examples (malicious inputs designed to cause a machine learning model to make errors), as well as foundation models and their algorithmic properties. Professor Kanade is also interested in research that uses computer science as a lens to study the natural sciences, particularly relating to biological evolution and neuroscience.

I am delighted and honoured to be awarded a Turing Fellowship. This will enable me to interact with the multidisciplinary community at the Turing in developing novel data-centric solutions to address important societal challenges.

Associate Professor Xiaowen Dong, Department of Engineering Science

Xiaowen Dong is an Associate Professor in the Department of Engineering Science, whose research interests lie at the intersection of signal processing, machine learning, and network science. A key focus is developing approaches to use graphs as mathematical tools to model relationships and structures within complex, real-world datasets. Through applying these graph-based data processing techniques, his work has generated new insights towards understanding human social interactions and collective behaviour, as well as the implications for building sustainable social and urban communities.