Speakers

Karim Beguir (InstaDeep)

Karim is the co-founder and CEO of InstaDeep, a startup specialised in industrial applications of Deep learning algorithms. A graduate of France’s Ecole Polytechnique and former Program Fellow at NYU’s Courant Institute, Karim has a passion for applied mathematics. His goal is to democratize AI and make it accessible to a wider audience.

Rama Cont (CNRS & Imperial College London)

Rama CONT is Professor of Mathematics at Imperial College London, Director of the CFM-Imperial Institute of Quantitative Finance, CNRS Senior Research Scientist at Laboratoire de Probabilites, Statistiques & Modelisation (Sorbonne Universite) and Fellow of the Society for Industrial and Applied Mathematics (SIAM). His research focuses on stochastic analysis, stochastic processes and mathematical modelling in finance. Prof. Cont received the Louis Bachelier Prize by the French Academy of Sciences in 2010 and the Royal Society Award for Excellence in Interdisciplinary Research in 2017 for his research on systemic risk modelling.

David Hand (Winton Capital Management/ Imperial College London)

Professor David Hand is Senior Research Investigator and Emeritus Professor of Mathematics at Imperial College, London, where he formerly held the Chair in Statistics. He is also Chief Scientific Advisor to Winton Capital Management. He is a Fellow of the British Academy, and an Honorary Fellow of the Institute of Actuaries, and has served (twice) as President of the Royal Statistical Society. He is a non-executive director of the UK Statistics Authority, and is Chair of the Board of the UK Administrative Data Research Network. He has published 300 scientific papers and 28 books, including Principles of Data Mining, Information Generation, Measurement Theory and Practice, The Improbability Principle, and The Wellbeing of Nations. In 2002 he was awarded the Guy Medal of the Royal Statistical Society, and in 2012 he and his research group won the Credit Collections and Risk Award for Contributions to the Credit Industry. He was awarded the George Box Medal in 2016. In 2013 he was made OBE for services to research and innovation.

Stephen Hardiman (Capital Fund Management)

Stephen is Senior research manager at CFM, working on algorithmic execution. He studied theoretical physics at Trinity College (Dublin, Ireland). He has a keen interest in machine-learning and its application to high frequency financial data.

Gary Kazantsev (Bloomberg)

Gary is the Head of Machine Learning Engineering at Bloomberg. Prior to joining Bloomberg in 2007, Gary earned degrees in physics, mathematics, and computer science at Boston University. He is engaged in advisory roles with start ups in FinTech and machine learning space and has worked at a variety of technology and academic organizations over the last 20 years. He is a member of the KDD Data Science + Journalism workshop program committee and a co-organizer of the annual Machine Learning in Finance conference at Columbia University.

Florent Krzakala (Ecole Normale Supérieure)

Florent Krzakala is Professor at Sorbonne Université (Paris VI) and École Normale Supérieure in Paris. Trained in statistical physics, Krzakala studies complex assemblies of simple elementary components, in the context of disordered systems, combinatorial optimization, satisfaction and coloring problems, coding and information theory, statistical inference, machine learning and compressed sensing.

Stéphane Mallat (Ecole Normale Supérieure / Collège de France)

Stéphane Mallat is an internationally recognized expert in signal and image processing, known for his pioneering work on the use of wavelets in signal processing. He holds the Chair of Data Science at Collège de France and is a member of the French Academy of sciences. He received a Ph.D. from the University of Pennsylvania, in 1988. He was then Professor at the Courant Institute of Mathematical Sciences, until 1994. In 1995, he became Professor in Applied Mathematics at Ecole Polytechnique, Paris and Department Chair in 2001. From 2001 to 2007 he was co-founder and CEO of a semiconductor start-up company. In 2012 he joined the Computer Science Department of Ecole Normale Supérieure, in Paris. His research interests include learning, signal processing, and harmonic analysis. He is an IEEE Fellow and an EUSIPCO Fellow. In 1997, he received the Outstanding Achievement Award from the SPIE Society and was a plenary lecturer at the International Congress of Mathematicians in 1998. He has received the 2004 European IST Grand prize, the 2004 INIST-CNRS prize for most cited French researcher in engineering and computer science, the 2007 EADS grand prize of the French Academy of Sciences, the 2013 Innovation medal of the CNRS, and the 2015 IEEE Signal Processing Best sustaining paper award.

Amir Sani (Imperial College London)

Amir Sani is a research fellow at the CFM-Imperial Institute of Quantitative Finance. A former proprietary trader with extensive experience in mortgage finance, he received a MSc in Machine Learning from UCL and a PhD in Machine Learning from INRIA (France). His research focuses on sequential resource allocation problems in financial market agent-based models and complex systems, with specific emphasis on sampling, surrogate modeling and policy approximation.

Csaba Szepesvári (Google DeepMind & University of Alberta)

Prof. Csaba Szepesvári is the team-lead for the "Foundations" team at Google Deepmind, currently on leave from the University of Alberta, where he is a Professor of Computing Science. His research focuses on reinforcement learning and principled approaches to AI. He is the co-inventor of UCT, a widely successful Monte-Carlo tree search algorithm, which ignited much work in AI, contributing significantly to the leap in performance of computer Go programs and eventually leading to Deepmind's AlphaGo. This work on UCT won the 2016 Test-of-time Award at ECML/PKDD where it was originally published in 2006.