REPRESENTATION LEARNING & STATISTICAL ML
Yoshua Bengio is a Full Professor in the Department of Computer Science and Operations Research at Université de Montreal, as well as the Founder and Scientific Director of Mila and the Scientific Director of IVADO. Considered one of the world’s leaders in artificial intelligence and deep learning, he is the recipient of the 2018 A.M. Turing Award with Geoff Hinton and Yann LeCun, known as the Nobel prize of computing. He is a Fellow of both the Royal Society of London and Canada, an Officer of the Order of Canada, and a Canada CIFAR AI Chair.
Photo courtesy of Maryse Boyce
Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and Head of Graph Learning Research at Twitter. He also heads ML research in Project CETI, a TED Audacious Prize-winning collaboration aimed at understanding the communication of sperm whales. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019). He has previously served as Principal Engineer at Intel Perceptual Computing and was one of the key developers of the Intel RealSense technology.
Andrea Vedaldi is a Professor of Computer Vision and Machine Learning and a co-lead of the VGG group at the Engineering Science department of the University of Oxford. His research focuses on computer vision methods to understand the content of images automatically, with applications to organising and searching vast image and video libraries and recognising faces and text in images and videos. Andrea also is a research scientist, at Facebook AI Research (FAIR).
Silvia Chiappa is a Senior Staff Research Scientist at DeepMind, where she is working on causal reasoning and ethical aspects of machine learning.
Her research interests are based around Bayesian and causal reasoning, graphical models, variational inference, ML fairness and bias, and time-series models.
Before joining DeepMind, Silvia was a post-doctoral researcher in the Empirical Inference Department at the Max-Planck Institute for Intelligent Systems and in the Machine Intelligence and Perception Group at Microsoft Research Cambridge, and a Marie-Curie fellow in the Statistical Laboratory at the University of Cambridge.
Ali Eslami is a staff research scientist at Google DeepMind working on problems related to artificial intelligence. Prior to that, he was a post-doctoral researcher at Microsoft Research in Cambridge. He did his PhD in the School of Informatics at the University of Edinburgh, during which he was also a visiting researcher in the Visual Geometry Group at the University of Oxford. His research is focused on figuring out how we can get computers to learn with less human supervision.
Robin Evans is an Associate Professor of Statistics at the University of Oxford, and a fellow of Jesus College. His research interests include multivariate and graphical models, latent variable models, and causal inference methods. His work has been applied to systems biology, quantum information theory, and the social sciences. Previous postings include the University of Cambridge, and the University of Washington where he did his PhD. He has collaborated with psychologists on the characteristics of substance abuse, and most recently on the causal relationships between paranoia and social media. He has also worked with a large, multinational data analytics company on data fusion problems.
Cheng Zhang is a Principal Researcher at the Machine intelligence group at Microsoft Research Cambridge (MSRC), UK. Currently, she leads the Minimum Data AI project in MSRC. She is interested in both machine learning theory, including Bayesian deep learning, approximate inference, causality, and Bayesian experimental design for sequential decision making, as well as various machine learning applications with business and social impact. Before joining Microsoft Research, she was at Disney Research Pittsburgh located at Carnegie Mellon University.
James Hensman is currently a Principal Scientist at Amazon.
He was previously the Director of Research at PROWLER.io, where he works on Bayesian machine learning approaches for automated decision making systems.
He was also a lecturer at Lancaster University and a fellow of the UK Medical Research Council in biostatistics.
Melanie Mitchell is the Davis Professor at the Santa Fe Institute. Her current research focuses on conceptual abstraction, analogy-making, and visual recognition in artificial intelligence systems. Melanie is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award and was named by Amazon.com as one of the ten best science books of 2009. Her latest book is Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux).
NATURAL LANGUAGE PROCESSING (NLP)
Rada Mihalcea is the Janice M. Jenkins Collegiate Professor of Computer Science and Engineering at the University of Michigan and the Director of the Michigan Artificial Intelligence Lab. Her research interests are in computational linguistics, with a focus on lexical semantics, multilingual natural language processing, and computational social sciences. She serves or has served on the editorial boards of the Journals of Computational Linguistics, Language Resources and Evaluations, Natural Language Engineering, Journal of Artificial Intelligence Research, IEEE Transactions on Affective Computing, and Transactions of the Association for Computational Linguistics.
Sebastian Ruder is a research scientist in the Language team at DeepMind, London, U.K. He completed his Ph.D. in Natural Language Processing and Deep Learning at the Insight Research Centre for Data Analytics, while working as a research scientist at Dublin-based text analytics startup AYLIEN. Previously, he studied Computational Linguistics at the University of Heidelberg, Germany and at Trinity College, Dublin. He is interested in transfer learning for NLP and cross-lingual learning as well as making ML and NLP more accessible.
Andreas Vlachos is a senior lecturer at the Natural Language and Information Processing group at the Department of Computer Science and Technology at the University of Cambridge. His current projects include dialogue modelling, automated fact checking and imitation learning. Andreas has also worked on semantic parsing, natural language generation and summarisation, language modelling, information extraction, active learning, clustering and biomedical text mining.
Luke Zettlemoyer is a Professor in the Paul G. Allen School of
Computer Science & Engineering at the University of Washington, and a Research Scientist at Facebook. His research focuses on empirical methods for natural language semantics, and involves designing machine learning algorithms, introducing new tasks and datasets, and, most recently, studying how to best develop self-supervision signals for pre-training. Honors include multiple paper awards, a PECASE award, and an Allen Distinguished Investigator Award. Luke received his PhD from MIT and was a postdoc at the University of Edinburgh.
Yue Zhang is currently an associate professor at Westlake University. Before joining Westlake in 2018, he worked as an assistant professor at Singapore University of Technology and Desgin and as a research assocaite at University of Cambridge. Yue Zhang received his PhD degree from University of Oxford in 2009 and his BEng degree from Tsinghua University, China in 2003. Yue Zhang's research interest lies in fundamental algorithm for NLP, syntax, semantics, information extraction, sentiment, text generation, machine translation and dialogue systems. He serves as the action editor for Transactions of Association of Computational Linguistics (TACL), and area chairs of ACL, EMNLP, COLING and NAACL.
Yulan He is a Professor in Natural Language Processing at the University of Warwick. She is also a Turing AI Fellow funded by the UK Research and Innovation (UKRI). Her research focuses on the integration of machine learning and natural language processing for text understanding. Current research topics include question-answering, sentiment analysis and opinion mining, topic/event extraction from text, clinical text mining, and social media analytics. Yulan received her PhD from the University of Cambridge.
Pengfei Liu is a postdoc at the Language Technologies Institute of Carnegie Mellon University and serves as a co-lecturer in the CMU Natural Language Processing course. His research topics currently focus on information extraction, text generation, and NLP system evaluation. He serves as area chairs of NAACL, EMNLP, NeurIPS and recently leads the development of a review robot and an AI product for AI system diagnostics. Honors include multiple doctoral dissertation awards and scholarships.
ML in HEALTHCARE
Reza Khorshidi is currently the Chief Scientist at AIG, and Investigator (in machine learning and medicine) at Deep Medicine program of The University of Oxford. Reza's current research at Oxford is focused on probabilistic machine learning, deep sequence models, biomedical informatics, and population health.
Reza’s team at AIG (i.e., Investments AI) is a group of scientists, engineers, designers, product managers, and digital strategy experts, focused on building AI-first products in FinTech.
Jens Rittscher is Professor of Engineering Science at the University of Oxford with his appointment held jointly between the Institute of Biomedical Engineering and the Nuffield Department of Medicine. He is a group leader at the Big Data Institute and affiliated to the Ludwig Institute of Cancer Research and the Wellcome Centre as an adjunct member. Previously, he was a senior research scientist and manager at GE Global Research (Niskyauna, NY, USA). His research interests lie in enabling biomedical imaging through the development of new algorithms and novel computational platforms, with a current focus to improve mechanistic understanding of cancer and patient care through quantitative analysis of image data. He is a co-director of the Oxford EPSRC Centre for Doctoral Training in Health Data Science.
Narges Razavian is an assistant professor at New York University Langone Health, Center for Healthcare Innovation and Delivery Sciences. Her research lab focuses on various applications of Machine Learning and AI for medicine with a clinical translation outlook. Her main focus and collaborations include representation learning and classifications using Electronic Health Records, Medical Images, and Clinical Notes. She is involved in a diverse set of medical collaborations around the early detection of diseases such as dementia, lung cancer, pancreatic cancer, diabetes, among others. Before NYU Langone, she was a postdoc at the CILVR lab at NYU Courant CS department. She received her PhD at CMU Computational Biology group.
Russ Greiner is a Professor in Computing Science and the founding Scientific Director of the Alberta Machine Intelligence Institute. He was elected a Fellow of the AAAI, has been awarded a McCalla Professorship and a Killam Annual Professorship; and in 2021, became a CIFAR AI Chair. For his mentoring, he received a 2020 FGSR Great Supervisor Award. He has published over 300 refereed papers, most in the areas of machine learning and recently medical informatics, including 5 that have been awarded Best Paper prizes. The main foci of his current work are bio- and medical- informatics; learning and using effective probabilistic models and formal foundations of learnability.
Kazem is a cardiologist, epidemiologist and health services researcher with interest in prevention and management of chronic diseases. He has led the design, coordination and reporting of multi-centre randomized trials, collaborative meta-analyses and large-scale observational studies that have investigated the burden, causes and management of cardiovascular diseases. He is the Director of the Oxford Martin programme on Deep Medicine which is using some of the largest and most complex biomedical datasets that have ever been collected to generate insights into complex disease patterns, risk trajectories and treatment effects.
M Jorge Cardoso is Senior Lecturer in Artificial Medical Intelligence at King’s College London, where he leads a research portfolio on big data analytics, quantitative neuro-radiology and value-based healthcare. Jorge is also the CTO of the new London Medical Imaging and AI Centre for Value-based Healthcare. He has more than 12 years expertise in advanced image analysis, neuroimaging, big data, and artificial intelligence. Jorge also co-leads the development of Project MONAI (monai.io), a deep-learning platform for artificial intelligence in healthcare.
Lea Goetz is a GSK.ai fellow whose interest in AI stems from an early fascination with neural circuits and how their computations give rise to intelligent behaviour. She joins the GSK.ai Fellows Programme to pursue research on the representations, learning algorithms and network architectures that support artificial intelligence. Lea wants to contribute her unique perspective and skill set to realise the enormous potential of intelligent systems in the biological sciences, target discovery and healthcare.
Lea received her PhD in computational neuroscience from University College London, where she focused on dendrites of single neurons as a biological substrate for the implementation of sparse input representations and learning algorithms - the basis for hierarchical representations in the visual cortex.
AI FOR GOOD
Thomas Dietterich is Distinguished Professor Emeritus in the School of Electrical Engineering and Computer Science at Oregon State University. He is one of the pioneers of the field of Machine Learning and has authored more than 220 refereed publications and two books. His current research topics include robust AI, robust human-AI systems, and applications in sustainability.
He is a former President of the Association for the Advancement of Artificial Intelligence, and the founding president of the International Machine Learning Society.
David Rolnick is Assistant Professor and Canada CIFAR AI Chair in the School of Computer Science at McGill University and at the Mila Quebec AI Institute, where his work focuses on deep learning theory and on applications of machine learning to climate change. He is co-founder and chair of Climate Change AI and serves as scientific co-director of Sustainability in the Digital Age. Dr. Rolnick is a former NSF Mathematical Sciences Postdoctoral Research Fellow, NSF Graduate Research Fellow, and Fulbright Scholar. He received his Ph.D. in Applied Mathematics from MIT.
Adam Wierman is a Professor in the Department of Computing and Mathematical Sciences (CMS) at the California Institute of Technology. He has been a faculty at Caltech since 2007. Adam’s research strives to make the networked systems that govern our world sustainable and resilient. He develops new mathematical tools in machine learning, optimization, control, and economics and applies these tools to design new algorithms and markets that can be deployed in data centers, the electricity grid, transportation systems, and beyond. He is best known for his work spearheading the design of algorithms for sustainable data centers and is a recipient of multiple awards, including the ACM SIGMETRICS Rising Star award, and the IEEE Communications Society William R. Bennett Prize.
Daniele Magazzeni is a Research Director at J.P. Morgan AI Research and he is the Head of the firmwide Explainable AI Center of Excellence. His main research interests are in AI Planning and ML for efficient resource allocation and processes optimisation, and Explainable AI.
Daniele is the current President of the International Conference on Automated Planning and Scheduling (ICAPS).
Daniele is Associate Professor (on leave) at King’s College London, where he was Co-Director of the UK Center for Doctoral Training in Safe and Trusted AI, and Head of the Human-AI-Teaming Lab. He is a frequent tutorial and keynote speaker at AI Conferences.
Renyuan Xu is currently a Hooke Research Fellow in the Mathematical Institute at the University of Oxford. She completed her Ph.D. degree in Operations Research from UC Berkeley in 2019. She will join the Epstein Department of Industrial Systems Engineering at the University of Southern California as a Gabilan Assistant Professor in Fall 2021. Her research interests lie at the intersection of machine learning, stochastic control, game theory, and mathematical finance.
Naren Ramakrishnan is the Thomas L. Phillips Professor of Engineering and director of the Sanghani Center for AI and Analytics at Virginia Tech. His research interests include data science and applied ML with applications to computational epidemiology, finance, and urban analytics. His work has been featured in the NIH outreach publication Biomedical Computation Review, the National Science Foundation's Discoveries series, Wall Street Journal, Newsweek, Smithsonian Magazine, Popular Science, and Chronicle of Higher Education. His team's recent research received the COVID-19 Symptom Data Challenge award.
Deniz Gündüz is a Professor of Information Processing in the Electrical and Electronic Engineering Department at Imperial College London, UK, and a part-time faculty member at University of Modena and Reggio Emilia, Italy. His research spans information theory, machine learning, wireless communications and privacy. He is particularly interested in how machine learning can be used to design post-5G wireless communication networks, and how those networks can enable distributed machine learning applications. He is a Distinguished Lecturer of the IEEE Information Theory Society.
Dimitris Vlitas is an Associate Director for Accenture UKI. He is a leading expert in bringing advanced machine learning into practical applications for business.
Helping UKI Accenture clients to fully embark on their AI transformation and create competitive advantage specific to their industry sector. AI transformation involves systematic execution of multiple valuable AI projects, appropriate processes in place to identify and select valuable AI projects and company’s strategy broadly aligned to AI powered landscape.
Dimitris is also a visiting Professor in Mathematics and Machine Learning at the University of Toronto. He is widely published and has helped research positions at Cornell University, University of Toronto and Fields Institute.
Jacob Abernethy is an Associate Professor in Computer Science at Georgia Tech. In 2011 he completed his PhD in Computer Science at the University of California at Berkeley, and then spent nearly two years as a Simons postdoctoral fellow at the CIS department at UPenn, working with Michael Kearns. Abernethy's primary interest is in Machine Learning, with a particular focus in sequential decision making, online learning, online algorithms and adversarial learning models.
Haitham Ammar leads the reinforcement learning team at Huawei technologies Research & Development UK and is an Honorary lecturer at UCL. Prior to Huawei, Haitham led the reinforcement learning and tuneable AI team at PROWLER.io (now SecondMind), where he contributed numerously to their technology in finance and logistics.
His primary research interests lie in the field of statistical ML and AI, focusing on lifelong learning, multitask learning, knowledge transfer, and RL. His research also spans different areas of control theory and nonlinear dynamical systems, as well as social networks and distributed optimization.
Oana Cocarascu is a Lecturer in Artificial Intelligence at King's College London. Her work is on applied research, specifically on how AI can be deployed to support real world applications.
She received her PhD from Imperial College London, where she worked at the intersection of natural language processing and machine learning for argument mining. She also worked on the automatic extraction of argumentation frameworks from data to provide user-centric explanations in a variety of settings. Application areas span recommender systems, explainable classifiers, as well as safe and trusted AI systems.
Luo Mai is a Lecturer (Assistant Professor) at the University of Edinburgh, where he leads the Large-scale AI Systems Group. He has a track record of publishing papers in top system conferences such as OSDI, NSDI, VLDB and USENIX ATC. He leads the designs of several popular open-source AI systems, including TensorLayer which won the best open-source software award in ACM Multimedia 2017. Before joining Edinburgh, he was a research associate at Imperial College London and a visiting researcher at Microsoft Research. Luo holds a PhD degree from Imperial College London, and his PhD study was supported by a Google PhD Fellowship.
Yikuan Li is a DPhil student in Deep Medicine programme, Nuffield Department of Women’s & Reproductive Health, University of Oxford. His research interests include representation learning for electronic health records, Bayesian deep learning, and interpretable AI. Prior to move to Oxford, he worked as a machine learning engineer in Huawei. He obtained his Master of Science in the Department of Electrical and Electronic Engineering in University College London. And before that, he graduated from University of Electronic Science and Technology of China majored in Electronic Engineering.