Marco Gori

sample-image Marco Gori received the Ph.D. degree from University of Bologna, working partly at McGill University, Montréal. He is full professor of computer science at the University of Siena, head of the Siena Artificial Intelligence Lab, and co-founder of Questit. He was Chairman of the Italian Chapter of the IEEE Computational Intelligence Society, and President of the Italian Association for Artificial Intelligence. He is a fellow of the IEEE, EurAI, and IAPR.
He is author of “Machine Learning: A Constraint-Based Approach,” Morgan Kaufmann, 560 pp., 2018.


Olivia Guest

sample-image Olivia Guest is a computational modeler in cognitive science and neuroscience. She has experience creating, replicating, and evaluating computational accounts for categorisation, conceptual representation, and semantic memory. She has modelled data from healthy adults, patient groups, infants, and animals. More broadly, she maintains active interests in applying computational modelling to other research areas such as politics and sociology — both theoretically and in an applied way.
She is a research scientist at the Research Centre on Interactive Media, Smart Systems and Emerging Technologies (RISE), Cyprus, and an affiliated researcher at Prof. Bradley C. Love's lab in the Department of Experimental Psychology, University College London (UCL), UK. She holds degrees in Computer Science (BSc, University of York), Cognitive and Decision Sciences (MSc, UCL) and Psychology (PhD, Birkbeck). She worked at the University of Oxford and UCL before joining RISE.


Yang-Hui He

sample-image Professor Yang-Hui He is a mathematical physicist working on the interface between geometry, number theory and quantum field theory/string theory. Recently, he helped introduce machine-learning into the field of pure mathematics by using AI to help uncover new patterns and raise new conjectures (cf. interview by Science [Vol 365, July, 2019] and by New Scientist [Dec 9 Issue, 2019]). Yang studied at Princeton University, where he received his Bachelor of Arts in Physics, with a Certificate in Applied Mathematics and a Certificate in Engineering, Summa cum Laude (Highest Honours, Phi-Beta-Kappa). He then obtained a Certificate in Advanced Mathematics (Tripos) at the University of Cambridge, with Distinction. He went on to receive his PhD in theoretical and mathematical physics from MIT. Yang continued with postdoctoral work in the University of Pennsylvania before joining University of Oxford as the FitzJames Fellow in Mathematics and then the UK STFC Advanced Fellow in theoretical physics. Yang joined City in 2010 as Reader. He is currently Professor of Mathematics and Senior Tutor for Research for Mathematics. He concurrently holds the Chang-Jiang Chair Professorship at NanKai University, China and jointly remains a Tutor and Lecturer at Merton College, Oxford where he taught since 2005.


Ernesto Jiménez Ruiz

sample-image Ernesto Jiménez Ruiz is a Lecturer in Artificial Intelligence at City, University of London affiliated to the Research Centers for Machine Learning and Artificial Intelligence. He is also a researcher in the Centre for Scalable Data Access (SIRIUS) at the University of Oslo, Norway. He previously held a Senior Research Associate position at The Alan Turing Institute in London (UK) and a Research Assistant position at the University of Oxford. His home university (Universitat Jaume I, Castellon, Spain) awarded a “Premio extraordinario de doctorado” (roughly translated as a Extraordinary Doctoral Award) to his doctoral thesis (Engineering category 2010-2011). His research has covered several areas, including bio-medical information processing and integration, ontology reuse, ontology versioning and evolution, ontology alignment. His current research interests focus on the application of Semantic Technology to Data Science workflows and the combination of Knowledge Representation and Machine Learning techniques.


Mehdi Keramati

sample-image Dr. Keramati is a multi-disciplinary scientist with his background in Computer Engineering (BSc), Economics (MSc), and Mathematical Neuroscience (PhD). He held two postdoctoral positions in Computational Neuroscience (UCL) and Computational Psychiatry (UCL). He has been a lecturer at the Department of Psychology at City since 2018. His research focuses on Planning, and Decision Making, Neuroeconomics, and Behavioral Economics. More broadly, he maintains an active interest in applying machine learning and artificial intelligence methods to to understanding human and social decision making processes by using a mixture of theoretical/computational and experimental techniques.


Niklas Kokkola

sample-image Niklas holds a BSc. in Computer Science & AI, and a PhD in Computer Science focused on mathematical models of associative learning, both from City, University of London. He is experienced in the theory and implementation of machine learning and general mathematical algorithms in multiple areas, including behavioural psychology, medical imaging, genetic sequencing, and pharmacological domains. He is currently working on his own machine learning start-up, and prior to that worked as a Senior Machine Learning Engineer at Lifebit in London. There, he worked on projects ranging from cloud recommendation systems, biomedical machine vision pipelines, and deep-learning based research tools for drug repurposing and vaccine optimisation.
He is an affiliated member of the Centre for Computational and Animal Learning Research and Artificial Intelligence Research Centre (CitAI).

Brad Love

sample-image Brad Love is Professor of Cognitive and Decision Sciences at UCL and an inaugural Faculty Fellow at the Alan Turing Institute, the UK's national institute for data science. He is also an APS, ELLIS, and Wolfson fellow. His work lies at the intersection of Neuroscience, Experimental Psychology, and Machine Learning. He has made contributions in linking models of cognition to brain function, and to using large real-world datasets to understand human behaviour. His current focus is making deep learning approaches more consistent with human behavioural and brain measures to increase the robustness of models and make them more faithful accounts of human cognition. For example, work in his lab seeks to incorporate top-down, goal-directed attentional mechanisms into convolutional networks to improve model correspondence with human behaviour and brain activity. Another project aims to interface brain measures with networks to better understand how brain regions correspond to parts of networks and to pave the way for better brain machine interfaces.


Shuhui Li

sample-image Dr. Shuhui Li received his B.S. and M.S. degrees in Electrical Engineering respectively from Southwest Jiaotong University in Chengdu, China and Ph.D. degree in Electrical Engineering from Texas Tech University. From 1988 to 1995, he was with the School of Electrical Engineering at Southwest Jiaotong University, where his research interests were in the areas of modelling and simulation of large dynamic systems, dynamic process simulation of electrified railways, power electronics, power systems, and power system harmonics. From 1995 to 1999, Dr. Li involved into the research areas of renewable energies, neural networks, and applications of massively parallel processing. He was with the Department of Electrical Engineering and Computer Science at Texas A&M University - Kingsville from 1999 to 2006. He joined the Department of Electrical and Computer Engineering at The University of Alabama in 2006. Dr. Li is a member of IEEE and a member of ASME.