Mike Gartrell is a senior researcher at the Criteo AI Lab in Paris, France. His recent research interests include modeling approaches for structured prediction and recommendation, including determinantal point processes, and he has published a number of papers on these topics. He is broadly interested in both theoretical and applied aspects of machine learning, including Bayesian probabilistic modeling and recommendation systems. Previously, he worked as a postdoctoral researcher at Microsoft in Herzliya, Israel. He received his PhD in 2014 from the University of Colorado Boulder.
Jennifer Gillenwater is a senior research scientist at Google Research NY. She received her PhD in 2014 from the University of Pennsylvania for thesis work focusing on approximate inference for determinantal point processes. She has also published work on submodular functions, and large-scale application of diversification techniques to the YouTube video corpus. Her other research interests include differential privacy and natural language processing.
Alex Kulesza is a senior research scientist at Google Research NY. He has studied determinantal point processes for machine learning since 2010, and published a highly-cited Foundations and Trends survey of the field in 2012. He previously co-organized tutorials on this topic at UAI 2012, CVPR 2013, and CVPR 2016. His other research interests include learning theory, online learning, differential privacy, and spectral learning.
Zelda Mariet is a graduating PhD student at MIT, where she studies the theory and application of negatively-dependent measures for machine learning model design and optimization, advised by Suvrit Sra. Zelda is broadly interested in the theoretical aspects of machine learning, and particularly in subset-selection problems for model design. Zelda is a recipient of the 2018 Google PhD Fellowship in machine learning and was previously funded by the Criteo research faculty fellowship award.