Invited speakers

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Percy Liang, Stanford, USA : Coopetitions

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Dr. Percy Liang is assistant professor at Stanford University. As part of his Ph.D. he developed a platform to benchmark machine learning algorithms (mlcomp.org). He is presently co-developing with Microsoft a new powerful platform to exchange data and code , and code-develop machine learning algorithms (codalab.org). He will share with us the new possibilities that this platform offers to organize the next generation of machine learning challenges: coopetitions. Coopetitions permit participants both to collaborate and compete.

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Olga Russakovsky, Stanford University, USA : The ImageNet project

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Olga Russakovsky is a PhD student working on computer vision with Prof. Fei-Fei Li on the ImageNet project, a large-scale image database organized according to the WordNet hierarchy, in which each node of the hierarchy is depicted by hundreds of thousands of images. Several challenges were organized with ImageNet data since 2010.

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Rinat Sergeev, Harvard, USA : The NASA tournament lab

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Dr. Rinat Sergeev Data Scientist and Chief Scientific Advisor, at the Harvard-NASA Tournament Lab. Over the past few years, they organizes a number of data science challenges for NASA, making use of the TopCoder platform. He will share with us his experience in crowdsourcing data science problems.

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Gabor Melis, Consultant Franz Inc, Hungary : A serial winner

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Gabor Melis is a Hungarian mathematician, computer scientist, software developer, consultant, and R&D engineer with main focus on machine learning, data mining, and Lisp with expertise in C, C++ and Java. Gábor Melis is author of the Hex-program Six, three times Gold medal winner at Computer Olympiads. In November 2010, Gábor won the Google AI Challenge organized by the University of Waterloo with his Planet Wars bot Bocsimackó written in Lisp. He recently won the Higgs ML challenge that will be discussed at the 2014 HEPML NIPS workshop.

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Tim Salimans, Algoritmica, The Nethelands : A brilliant data scientist

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Dr. Tim Salimans is founding partner and data scientist at predictive analytics consulting firm Algoritmica, with a PhD in computational Econometrics and a strong academic background in Machine Learning. He is a 4-times prize winner and 6-times 10% competition in Kaggle competitions. He recently ranked second in the Higgs ML challenge that will be discussed at the 2014 HEPML NIPS workshop. He will share with us his experience as data scientist and his expectations from upcoming competitions.

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Michele Sebag, LRI, France : Pascal challenges

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Dr. Michele Sebag is senior scientist at the CNRS and co-head of project TAO, INRIA Saclay, France. She has been heading the challenge program of the Pascal European network of excellence, which has launched many high impact challenges, including challenges in text mining, brain computer interfaces, and computer vision (Pascal VOC challenges). She will share her vision on where challenges in machine learning should head.

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Gustavo Stolovitzky, IBM, USA : DREAM challenges

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Dr. Gustavo Stolovitzky is a research staff member in the IBM Computational Biology Center and the manager of the IBM Functional Genomics and Systems Biology Group. Gustavo has had an active role in organizing the systems biology community. He founded and leads the DREAM project, an international effort that nucleates thousands of participants to address important problems of validation of systems biology methods and models. He also co-organizes the RECOMB Systems and Regulatory Genomics and DREAM challenge conference series, which has nucleated more than a thousand attendees over the past 5 years.

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Ben Hamner, Kaggle, USA : The Kaggle platform

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Ben leads Kaggle's data science and development teams. Kaggle is a very successful competition platform. Over the past few years, Kaggle has demonstrated that challenges can be run as a service to solve real world problems and the Kaggle community is now the largest data science workforce. Ben is the principal architect of many of Kaggle's most advanced machine learning projects including current work in Eagle Ford and GE's flight arrival prediction and optimization modeling.