speakers
Challenges in Machine Learning:
Machine Learning Challenges as a Research Tool
Saturday December 9, 2017, Long Beach, California
NIPS 2017 workshops. Long Beach Convention Center
[Home][Schedule][Committee][Organizers]
Tentative list of speakers (to be confirmed)
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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.
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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 (www.ramp.studio), a code-submission platform to accelerate building predictive workflows and to promote collaboration between data providers and data scientists.
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Olivier Bouquet in director of machine learning research at Google Zurich. He is interested in advancing the understanding of human and computer intelligence and developing applications of this research in various industrial or business domains. He is presently preparing a challenge on automatic machine learning geared towards Google applications of deep learning. Prior joining Google, Olivier was director of research at Pertinence (Paris) and post-doc at the Max Plank institute. He holds and engineering degree and a PhD in computer science from Ecole Polytechnique (Paris).
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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.
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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.
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Sharada Prasanna Mohanty is a Doctoral Student at École polytechnique fédérale de Lausanne, in the lab of Marcel Salathé. His research focusses on automated diagnosis of diseases, measuring nutritional intake in large populations, and a range of problems at the intersection of Machine Learning and Digital Health. As a strong believer in OpenSource and reproducible science, he also spends his time helping shape CrowdAI, an open data science challenge platform, which connects researchers from various domains with a community of machine learning enthusiasts; leading to novel and creative solutions to numerous problems in Science. Prior to starting his PhD, Mohanty was a part of a small team of researchers at the Theoretical Physics Department, CERN, who ran the CERN Public Computing Challenge.