Invited speakers

• Tony Jebara, Director at Netflix & Professor at Columbia University

Tony Jebara is director of machine learning at Netflix and professor on leave from Columbia University. He has published over 100 peer-reviewed papers in leading conferences and journals across machine learning, computer vision, social networks and recommendation. His work has been recognized with best paper awards from the International Conference on Machine Learning and from the Pattern Recognition Society. He is the author of the book Machine Learning: Discriminative and Generative. Jebara is the recipient of the Career award from the National Science Foundation as well as faculty awards from Google, Yahoo and IBM. He has co-founded and advised multiple startup companies in the domain of artificial intelligence. Jebara has served as general chair and program chair for the International Conference on Machine Learning. In 2006, he co-founded the NYAS Machine Learning Symposium and has served on its steering committee since then. He obtained a PhD from MIT in 2002.

• Damien Lefortier, ML Researcher/Engineer at Facebook

Damien Lefortier is a Staff Research Scientist & Tech Lead on the core Ads Ranking team at Facebook, where he currently leads the Causal Modeling team. His main research interests are in machine learning, currently focusing on causal inference for recommendation systems. Previously, Damien worked on the core Machine Learning team at Criteo, where he helped improve Criteo’s predictive algorithms for ad targeting and on the Search team at Yandex, where he focused on search quality. His research work has been published at top tier conferences, such as WWW and CIKM.

• Mounia Lalmas-Roelleke, Head of Tech Research in Personalization at Spotify

Mounia Lalmas is a Director of Research at Spotify, and the Head of Tech Research in Personalization, where she leads an interdisciplinary team of research scientists working on personalization and discovery. Mounia also holds an honorary professorship at University College London. Before that, she was a Director of Research at Yahoo, where she led a team of researchers working on advertising quality for Gemini, Yahoo native advertising platform. She also worked with various teams at Yahoo on topics related to user engagement in the context of news, search, and user generated content. Prior to this, she held a Microsoft Research/RAEng Research Chair at the School of Computing Science, University of Glasgow. Before that, she was Professor of Information Retrieval at the Department of Computer Science at Queen Mary, University of London. Her work focuses on studying user engagement in areas such as native advertising, digital media, social media, search, and now audio. She has given numerous talks and tutorials on these and related topics. She is regularly a senior programme committee member at conferences such as WSDM, WWW and SIGIR. She was co-programme chair for SIGIR 2015 and WWW 2018, and is co-programme chair for WSDM 2020. She is also the co-author of a book written as the outcome of her WWW 2013 tutorial on "Measuring user engagement".

• Craig Boutilier, Principal Scientist at Google & Professor at University of Toronto

Craig is Principal Scientist at Google, working on various aspects of decision making under uncertainty (e.g., reinforcement learning, Markov decision processes, user modeling, preference modeling and elicitation, recommender systems). He received his Ph.D. from the University of Toronto in 1992, and has held positions at the University of British Columbia, University of Toronto, CombineNet, and co-founded Granata Decision Systems. Craig was Editor-in-Chief of JAIR; Associate Editor with ACM TEAC, JAIR, JMLR, and JAAMAS; Program Chair for IJCAI-09 and UAI-2000. Boutilier is a Fellow of the Royal Society of Canada (RSC), the Association for Computing Machinery (ACM) and the Association for the Advancement of Artificial Intelligence (AAAI). He was recipient of the 2018 ACM/SIGAI Autonomous Agents Research Award and a Tier I Canada Research Chair; and has received (with great co-authors) a number of Best Paper awards including: the 2009 IJCAI-JAIR Best Paper Prize; the 2014 AIJ Prominent Paper Award; and the 2018 NeurIPS Best Paper Award.