CiML 2017‎ > ‎


Challenges in Machine Learning
Machine Learning Challenges as a Research Tool
Saturday December 9, 2017, Long Beach, California


Tentative list of speakers (to be confirmed)

   Ben Hamner (confirmed)
Co-founder and CTO of Kaggle 
Ben Hamner leads Kaggle's product and engineering teams. Ben is the principal architect of many of Kaggle's most advanced machine learning projects, including developing machine learning for oil exploration and GE's flight arrival prediction and optimization modeling.
Kaggle is the world leader competition platform in data science.

   Balázs Kégl (confirmed)
Research Scientist, Linear Accelerator Laboratory, CNRS-University of Paris
Balázs Kégl is a senior research scientist at CNRS and head of the Center for Data Science of the Université Paris-Saclay. Prior to joining the CNRS, he was Assistant Professor at the University of Montreal. Balázs is co-creator of RAMP (, a code-submission platform to accelerate building predictive workflows and to promote collaboration between data providers and data scientists.

    André Elisseeff  (confirmed)
André Elisseeff is a software engineer at Google Zurich working both for YouTube and in the Machine Learning Research group. He is leading the effort of organizing AutoDL a new large automatic machine learning challenge (following the AutoML series of ChaLearn).  Prior to joining Google, André was president of Nhumi technologies, a startup that revolutionized data visualization for healthcare records.  He also conducted research for 5 years at IBM Zurich and was a post-doc at the Max Plank Institute. He holds a PhD degree of ENS Lyon, France. André Elisseeff was a co-founder of the Causality Workbench  and co-organizer of several challenges on causality.

    Katja Hofmann (confirmed, if no schedule conflict with another workshop)
Katja Hofmann is a researcher at the Machine Intelligence and Perception group at Microsoft Research Cambridge. She leads the Project Malmo, which uses the popular game Minecraft as an experimentation platform for developing intelligent technology. Her long-term goal is to develop AI systems that learn to collaborate with people, to empower their users and help solve complex real-world problems.
Before joining Microsoft Research, she completed her PhD in Computer Science as part of the ILPS group at the University of Amsterdam. 

    Xavier Baro (confirmed)
Xavier Baro is Associate Professor at the Faculty of Computer Science, Multimedia, and Telecommunication in the Universitat Oberta de Catalunya (UOC). His research activities are developed at the Computer Vision Center, as part of the Barcelona Perceptual Computing Lab, and at the Internet Interdisciplinary Institute as part of the Scene Understanding and Artificial Intelligence Lab. Besides machine learning, he works on evolutionary computation, and statistical pattern recognition. Recently, he started developing algorithms for generic object recognition over huge cardinality image databases.
Xavier Baro is one of the most active developers of the Codalab platform, an open-source platform for organizing macine learning challenges and performing collaborative projects.