Martin Pál, Google Research: "Making Money on Ad Exchange"
Martin Pál graduated from Comenius University in Slovakia and Cornell University (PhD '04). He held a postdoc at Rutgers and Bell Labs in '04/'05, and has been working as an engineer at Google since then. Martin's interests include approximation algorithms, combinatorial optimization, auctions and game theory.
Olivier Koch, Criteo: "New challenges for scalable machine learning in online advertising"
Olivier Koch is Engineering Program Manager at Criteo, where he focuses on large-scale prediction and recommendation algorithms.
Olivier's background is in computer vision and artificial intelligence, with 12 years of experience in academia and industry. He has published work at ICCV, CVPR and ICRA. Olivier holds an engineering degree from ENSTA-Paristech and a PhD degree in Computer Science from Massachusetts Institute of Technology.
Chinmay Karande, Facebook: "A theoretical model for budget effects in A/B testing"
Chinmay Karande is an Engineering Manager at Facebook, leading the "Market Dynamics" team. The team applies concepts from auction theory, algorithms and distributed systems to a diverse set of use cases. Chinmay completed his BTech in Computer Science & Engineering at IIT Bombay, and PhD in Algorithms, Combinatorics and Optimization at Georgia Tech. His focus area was algorithms and game theory.
John Langford, Microsoft Research: “A Multiworld Testing Decision Service”
John Langford is a machine learning research scientist and is the principal developer of Vowpal Wabbit. John works at Microsoft Research New York, of which he was one of the founding members, and was previously affiliated with Yahoo! Research, Toyota Technological Institute, and IBM's Watson Research Center. He studied Physics and Computer Science at the California Institute of Technology, earning a double bachelor's degree in 1997, and received his Ph.D. in Computer Science from Carnegie Mellon University in 2002.
Edoardo (Edo) Airoldi, Harvard
Edoardo Airoldi is associate professor of statistics at Harvard. He is also an associate faculty member at The Broad Institute of MIT and Harvard. He currently leads the Harvard laboratory for applied statistical methodology and data science. His research is at the intersection of statistical machine learning and computational social science.
Tony Ezzat, Nanigans, Inc
Tony Ezzat is the VP of Optimization at Nanigans. He leads a team of data scientists, who are responsible for building predictive lifetime value modeling algorithms and platform. Tony has 15 years of experience specializing in analytics, machine learning, optimization, and information retrieval. Formerly, Tony was the Director of Analytics at KAYAK and Principal Research Scientist at Mitsubishi Electric Research Labs in Boston. Tony graduated with BS, MS, and PhD degrees, all in Computer Science from MIT.