Cheng Zhang is a senior researcher at the All Data AI 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.
Professor Carlsson is one of the most renowned mathematicians in the world. He has been a professor of mathematics at Stanford University since 1991. He is known for his work on the Segal conjecture and applied algebraic topology, especially topological data analysis. He pioneered the applied use of topology to solve complex real-world problems. He is also the co-founder and president of the predictive technology company Ayasdi.
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.
Reza 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.
Mihaela van der Schaar
Prof. van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence, and Medicine at the University of Cambridge, a Turing Faculty Fellow at The Alan Turing Institute in London, where she leads the effort on ML and AI for personalised medicine. Her research expertise spans signal and image processing, communication networks, network science, multimedia, game theory, distributed systems and machine learning. Her current research focus is on ML, AI and operations research for medicine.
Andrea Vedaldi is Associate Professor of Engineering Science at the University of Oxford, UK where he has been co-director of the Visual Geometry Group (VGG), one of the internationally-leading groups in computer vision, since 2012. 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).
Partha Maji is a Principal Research Scientist at Arm's Machine Learning Research Lab based in Cambridge, where he drives the core research on the efficient implementation of probabilistic machine learning on the resource-constrained devices. Partha spent a decade in the semiconductor industry as a CPU subsystem architect and ASIC design engineer. He has extensive experience with the end-to-end chip design process through multiple tape-outs of low-power chips at 65/40/28/22nm deep-submicron CMOS process technology. His current research interests lie in multiple disciplines that bridge the topics of machine learning, mobile/embedded systems, computer architecture and hardware implementation.
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.
Audrey is an Assistant Professor in the Computer Science and Software Engineering department, and the Electrical Engineering and Computer Engineering department, at Université Laval. She is also affiliated with Mila — Quebec AI Institute through a Canada CIFAR AI Chair. She is interested in developing reinforcement learning algorithms that learn by interacting with their environment and leveraging their power in real-world applications. Her research therefore focuses both on analysing such algorithms in order to provide guarantees on their performance and ensuring that they can safely be deployed on the field.
Alona is an Assistant Professor in the Computing Science and Psychology Departments at the University of Alberta. She uses machine learning to analyse brain images collected while people read, which allows her to study how humans represent the meaning they encounter in text, and how they combine words to understand higher-order meaning. Alona also studies how computers learn to represent meaning when trained on text or images. Alona has discovered interesting connections between meaning representations in computer models and those in the human brain. Those connections serve to advance both our understanding of the brain, and the state of the art in machine learning.
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.
Pier Palamara is associate professor at the University of Oxford's Department of Statistics and a group leader at the Wellcome Centre for Human Genetics. His research group develops statistical models and machine learning algorithms for the analysis of large genomic data sets. Applications include using genomic data to reconstruct past human demographic and evolutionary events and studying the architecture of complex heritable traits and diseases in data sets of heterogeneous ancestry.
Moez Draief is Chief Scientist and Vice President in Data Science and Engineering at Capgemini. His research focus is on graph analysis and the study of dynamics on networks such as epidemics. He was also undertaken work in on line learning. He leads numerous projects related to the Corona virus health crisis for a variety of European governments, among which StopCovid, the French government contract tracing application. Prior to Capgemini, he was professor at Imperial College London and Chief Scientist at Huawei Technologies.
Shiva is the Director of Research Infrastructure at 23andMe where she works between researchers and engineers to power the world class research at 23andMe. Her role is focused on building data products for data scientists and researchers. She was previously the Director of Data Science at Zymergen Inc., a molecular technology company in the Bay Area focused on generating new chemicals. Prior to Zymergen, she was the CEO of BioSymetrics Inc. a biomedical machine learning startup in New York, where she was initially the Chief Product Officer. She has a PhD (DPhil) in Computational Biophysics from the University of Oxford and a HBSc. in Computer Science and Human Biology from the University of Toronto.
Alix Lacoste, PhD, is the Vice President of Data Science at BenevolentAI, an AI healthcare company that is transforming the way medicines are discovered, designed and developed. She leads a team that grows analytical, visual and machine learning tools for finding new ways to treat diseases. Previously at IBM Watson Health, Alix led AI-assisted research projects with academic and pharma partners. Alix holds a PhD in Molecular and Cellular Biology from Harvard University. She has presented her work in both biomedical and machine learning journals and conferences.
Nicola is a senior solution architect at NVIDIA for deep learning in healthcare and an active member of the medical imaging research community (e.g. Area Chair for MICCAI and IPCAI 2020). Throughout her studies and professional career, she has been working in the intersection of mathematics, medicine and computer science. In particular, she investigates real-time machine learning approaches for computer-assisted surgical interventions and federated learning for digital health. She holds a PhD from the Technical University of Munich, published various peer reviewed papers and was honored with the MICCAI Young Scientist Award.
Kayhan is an Assistant Professor of the Department of Biomedical Informatics and Intelligent Systems Program with secondary appointments in the Computer Science and Electrical Engineering Department at the University of Pittsburgh and an adjunct faculty in the Machine Learning Department at the Carnegie Mellon University. His research is at the intersection of medical vision, machine learning, and bioinformatics. His group develops efficient human-explainable machine learning methods to analyze and understand medical images along with genetic data and other electrical health records such as clinical reports. The main focus of his group is on learning from weak and noisy data as well as Explainable-AI. This research is supported by NIH and NSF as well as industry-sponsored funding.
Mona is the founder and CEO of AI for Global Goals, which aspires to connect AI talents to the industries and companies whose missions are aligned with the UN's Sustainable Development Goals (SDGs). Her main interest is in training and upskilling AI scientists by organising educational events such as Oxford ML Summer schools.
She has a DPhil in Biomedical Engineering from the University of Oxford, and has years of experience in the medical devices industry, and deep tech venture capital.