CiML2019‎ > ‎


Workshop chairs:

Adriënne Mendrik (Netherlands eScience Center)
Wei-Wei Tu (4Paradigm Inc. and ChaLearn)
Isabelle Guyon (UPSud/INRIA, U. Paris-Saclay and ChaLearn) 
Evelyne Viegas (Microsoft Research)

Workshop co-organizer:

Ming Li (Nanjing University, China)
Xiawei Guo (4Paradigm Inc.)
Zhen Xu (4Paradigm Inc.)
Zhengying Liu (INRIA, U. Paris-Saclay)

Bios of the organizers:

Adriënne Mendrik is manager and researcher at the Netherlands eScience Center (Amsterdam, the Netherlands). She has extensive experience with designing benchmarks and challenges for measuring algorithm performance in biomedical image analysis, and has organized various workshops and tutorials on this topic at international conferences (IEEE ISBI, IEEE eScience, MICCAI). She is currently leading the development of the EYRA benchmark platform, a platform to set-up benchmarks to test algorithm performance for challenges in a wide range of scientific disciplines (from astronomy to humanities and social sciences). Her research focuses on machine learning in medical image analysis and benchmark and challenge design.

Wei-Wei Tu is principle machine learning architect at the 4Paradigm Inc., Beijing, China. He got master degree from Department of Computer Science at Nanjing University, China. He was major in machine learning. After graduation, he worked as a senior engineer for two and a half years at China’s biggest search engine, Baidu. At Baidu, he built Baidu’s first distributed GBDT training system running on hundreds of machines at 2012, deployed Baidu’s first large scale deep learning based click-through rate prediction system at 2013, and co-designed Baidu’s first distributed machine learning computation framework at 2014. One of his work won “Baidu Million Dollar Highest Prize” at 2015. At 4Paradigm Inc., Wei-Wei Tu designed and developed the distributed machine learning computation framework, and led his team developed many large scale distributed machine learning algorithms supporting thousands of billions parameters and hundreds of billions instances. He is now leading his team to build business AutoML systems at 4Paradigm Inc.. He is data competition chair of PAKDD2018 and PAKDD 2019, chair of AutoML workshop at PRICAI2018, one of main organizers of NeurIPS2018 AutoML Challenge, main organizer of KDD Cup 2019 AutoML Challenge, one of the organizers of NeurIPS 2019 AutoDL competition.

Isabelle Guyon is chaired professor in “big data” at the Université Paris-Saclay, and specializes in statistical data analysis, pattern recognition, machine learning, and causal discovery. Prior to joining Paris-Saclay, she worked as an independent consultant and was a researcher at AT&T Bell Laboratories, where she pioneered applications of neural networks to pen computer interfaces (with collaborators including Yann LeCun and Yoshua Bengio) and co-invented Support Vector Machines (SVM) with Bernhard Boser and Vladimir Vapnik. She is the primary inventor of SVM-RFE, a variable selection technique based on SVM, and co-authored a seminal paper on feature selection that received thousands of citations. Since 2003, she has organized many challenges in machine learning and causal discovery, supported by the EU network Pascal2, NSF, and DARPA, with prizes sponsored by Microsoft, Google, Facebook, Amazon, Disney Research, and Texas Instrument. Guyon holds a Ph.D. in Physical Sciences from the University Pierre and Marie Curie, Paris, France. She is president of ChaLearn, a nonprofit dedicated to organizing challenges. She is an action editor of the Journal of Machine Learning Research and general chair of the NIPS 2017 conference.

Evelyne Viegas is Senior Director of Global Research Engagement at Microsoft Research. In her current role, she leads initiatives in the areas of Artificial Intelligence, Computing Systems, Experiences and AI and Society, working in partnership with the academic research community, and business groups, worldwide. She develops signature programs via research collaborations to drive open innovation. Prior to her present role, Evelyne worked as a Technical Lead at Microsoft delivering Natural Language Processing components to Office and Windows. Before Microsoft, and after completing her Ph.D. in France, she worked as a Principal Investigator at the Computing Research Laboratory in New Mexico on an ontology-based Machine Translation project. Evelyne serves on international editorial, program and award committees.

Ming Li is a Professor in the Department of Computer Science, Nanjing University, China. He received my B. Sc. and Ph. D. degrees in computer science from Department of Computer Science and Technology, Nanjing University, China, in 2003 and 2008, respectively. He joined Department of Computer Science and Technology of Nanjing University in 2008. His major research interests include machine learning and data mining, especially on software mining. major research interests include machine learning and data mining, especially on software mining. He has served as the area chair of IEEE ICDM, senior PC member of the premium conferences in artificial intelligence such as IJCAI and AAAI, and PC members for other premium conferences such as KDD, NIPS, ICML, etc., and the chair of the International Workshop on Software Mining. He has served as the associate editor (junior) for Frontiers of Computer Science and editorial board member for International Journal of Data Warehousing and Mining. He is the executive board member of ACM SIGKDD China Chapter. He has been granted various awards including the Excellent Youth Award from NSFC, the New Century Excellent Talents program of the Education Ministry of China, the CCF Distinguished Doctoral Dissertation Award, and Microsoft Fellowship Award, etc.