ADVANCES IN FUNDAMENTAL AI/ML RESEARCH
A limited number of seats is available for the audience of this math-heavy, expert-level workshop. If you are interested in participating, please submit a nomination form [here]. You need to be a knowledgeable and active researcher in the field. Please submit a short justification.
Speakers, Panelists and Moderators
KLAUS-ROBERT MÜLLERHead of the Intelligent Data Analysis group, Professor, Co-ChairTU BerlinKlaus-Robert Müller received the Diploma degree in mathematical physics in 1989 and the Ph.D. in theoretical computer science in 1992, both from University of Karlsruhe, Germany. From 1992 to 1994 he worked as a Postdoctoral fellow at GMD FIRST, in Berlin where he started to built up the intelligent data analysis (IDA) group. From 1994 to 1995 he was a European Community STP Research Fellow at University of Tokyo in Prof. Amari's Lab. From 1995 until 2008 he was head of department of the IDA group at GMD FIRST (since 2001 Fraunhofer FIRST) in Berlin and since 1999 he holds a joint associate Professor position of GMD and University of Potsdam. In 2003 he became a full professor at University of Potsdam, in 2006 he became chair of the machine learning department at TU Berlin. He has been lecturing at Humboldt University, Technical University Berlin and University of Potsdam. In 1999 he received the annual national prize for pattern recognition (Olympus Prize) awarded by the German pattern recognition society DAGM, in 2006 the SEL Alcatel communication award and in 2014 he was granted the Science Prize of Berlin awarded by the Governing Mayor of Berlin and in 2017 he received the Vodafone Innovations Award. Since 2012 he is Member of the German National Academy of Sciences Leopoldina and he holds a distinguished professorship at Korea University in Seoul. In 2017 he was elected member of the Berlin Brandenburg Academy of Sciences and also external scientific member of the Max Planck Society. For 5 years he was director of the Bernstein Center for Neurotechnology, from 2014 he became co-director of the Berlin Center for Big Data and from 2018 simultaneously director of the Berlin Machine Learning Center. He serves in the editorial boards of Computational Statistics, IEEE Transactions on Biomedical Engineering, Journal of Machine Learning Research and in program and organization committees of various international conferences. In 2019 he became ISI Highly Cited Researcher. His research interest is in the field of machine learning, deep learning and data analysis covering a wide range of theory and numerous scientific (Physics, Chemistry and Neuroscience) and industrial applications. GS > 69000, h 114. His research areas include statistical learning theory for neural networks, support vector machines and ensemble learning techniques. He contributed to the field of signal processing working on time-series analysis, statistical denoising methods and blind source separation. His present application interests are expanded to the analysis of biomedical data, most recently to brain computer interfacing, genomic data analysis, computational chemistry and atomistic simulations.
KAMALIKA CHAUDHURIAssociate Professor Computer Science and Engineering; Co-ChairKamalika Chaudhuri received a Bachelor's of Technology degree in Computer Science and Engineering in 2002 from the Indian Institute of Technology, Kanpur, and a PhD in Computer Science from UC Berkeley in 2007. After a stint as a postdoctoral researcher at the Information Theory and Applications Center at UC San Diego, she joined the CSE department at UCSD as an assistant professor in 2010. She is a recipient of the NSF CAREER Award in 2013, and a Hellman Faculty Fellowship in 2012. Chaudhuri's research is on the design and analysis of machine-learning algorithms and their applications. In particular, her interests lie in clustering, online learning, and privacy-preserving machine-learning, and applications of machine-learning and algorithms to practical problems in other areas.
MASASHI SUGIYAMADirector; Co-ChairRIKEN Center for Advanced Intelligence ProjectMasashi Sugiyama was born in Osaka, Japan, in 1974. He received the degrees of Bachelor of Engineering, Master of Engineering, and Doctor of Engineering in Computer Science from Tokyo Institute of Technology, Japan in 1997, 1999, and 2001, respectively. In 2001, he was appointed Assistant Professor in the same institute, and he was promoted to Associate Professor in 2003. He moved to the University of Tokyo as Professor in 2014. Since 2016, he has concurrently served as Director of RIKEN Center for Advanced Intelligence Project. He received an Alexander von Humboldt Foundation Research Fellowship and researched at Fraunhofer Institute, Berlin, Germany, from 2003 to 2004. In 2006, he received European Commission Program Erasmus Mundus Scholarship and researched at the University of Edinburgh, Edinburgh, UK. He received the Faculty Award from IBM in 2007 for his contribution to machine learning under non-stationarity, the Nagao Special Researcher Award from the Information Processing Society of Japan in 2011 and the Young Scientists' Prize for the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology Japan in 2014 for his contribution to the density-ratio paradigm of machine learning, and the Japan Society for the Promotion of Science Award and the Japan Academy Medal in 2017 for his series of machine learning research. His research interests include theories and algorithms of machine learning and data mining, and a wide range of applications such as signal processing, image processing, and robot control.
ULRIKE VON LUXBURGProfessor, Department of Computer ScienceUniversity of TuebingenI am a professor for computer science, with research focus on the theory of machine learning. I am also a fellow at the Max Planck Institute for Intelligent Systems. My research focus is on theoretical questions about unsupervised machine learning, in particular the statistical analysis of algorithms on random graphs and ordinal data analysis. I am coordinating the research cluster Machine learning: New Perspectives for Science. In the city of Tübingen, and also in the wider context of Germany, there is an ongoing discussion about research in artificial intelligence and its impact on future society. I find this discussion important and actively participate(d) in quite a number of past events.
ZHI-HUA ZHOUProfessor of Computer ScienceNanjing University
Zhi-Hua Zhou received his B.Sc., M.Sc. and Ph.D. degrees in computer science from Nanjing University, China, in 1996, 1998 and 2000, respectively, all with the highest honor. He joined the Department of Computer Science & Technology of Nanjing University as an Assistant Professor in 2001, and at present he is a Professor, Head of the Department of Computer Science and Technology, Dean of the School of Artificial Intelligence, Standing Deputy Director of the National Key Lab for Novel Software Technology, and Founding Director of LAMDA (the Institute of Machine Learning and Data Mining) at Nanjing University.
He has wide research interests, mainly including machine learning, data mining, pattern recognition and artificial intelligence. He authored the book "Ensemble Methods: Foundations and Algorithms (2012)", "Evolutionary Learning: Advances in Theories and Algorihtms (2019)", and "Machine Learning (in Chinese) (2016)", published more than 200 papers in top-tier international journals/conferences, and holds 20+ patents. He recived various awards/honors such as the National Natural Science Award of China (2013), the IEEE Computer Society Edward J. McCluskey Technical Achievement Award (2019), the ACML Distinguished Contribution Award (2019), the PAKDD Distinguished Contribution Award (2016), the IEEE ICDM Outstanding Service Award (2016), the IEEE CIS Outstanding Early Career Award (2013), the Microsoft Professorship Award (2006), and fourteen international paper/competition awards. He is a foreign member of the Academy of Europe, and Fellow of the ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, CCF, and CAAI.
He serves as the Editor-in-Chief of Frontiers of Computer Science, Associate Editor-in-Chief of the Science China Information Science, Action Editor or Associate Editor of Machine Learning, IEEE Trans. Pattern Analysis and Machine Intelligence, ACM Trans. Knowledge Discovery from Data, etc. He served as Associate Editor-in-Chief of the Chinese Science Bulletin (2008-2014), Associate Editor of the IEEE Trans. Knowledge and Data Engineering (2008-2012), IEEE Trans. Neural Networks and Learning Systems (2014-2017), ACM Trans. Intelligent Systems and Technology (2009-2017), Neural Networks (2014-2016), etc. He founded ACML (Asian Conference on Machine Learning). He serves/ed as Steering Committee vice chair of PRICAI, Steering Committee member of ICDM and PAKDD, Trustee of IJCAI (2018-2023), Advisory Committee member of IJCAI (2015-2016), Program Chair of AAAI 2019, ICDM 2015, IJCAI 2015 Machine Learning Track, etc., General Chair of ICDM 2016, PAKDD (2014, 2019), etc., Workshop Chair of ICDM 2014, KDD (2012, 2016), etc., Tutorial Chair of KDD 2013, CIKM 2014, etc., and Area Chair of various conferences including NeurIPS, ICML, AAAI, IJCAI, KDD, etc. He will serve as Program Chair for IJCAI 2021.
He is/was the Chair of the IEEE CIS Data Mining Technical Committee (2015-2016), Vice Chair of the IEEE Nanjing Section (2011-), Founding Chair of the IEEE Computer Society Nanjing Chapter (2008-2017), member of the IEEE CS Fellow Evaluation Committee (2015), Chair of the CCF-AI (2012-2019), Chair of the CAAI-ML (2006-2015), Vice President of the China Association of Artificial Intelligence (2019-), President of the Jiangsu Association of Artificial Intelligence (2017-), and President of the Jiangsu Computer Society (2019-).
Zhi-Hua Zhou's URL is at http://cs.nju.edu.cn/zhouzh/
ANIL JAIN University Distinguished Professor Dept. of Computer Science & EngineeringMichigan State UniversityANIL JAINUniversity Distinguished Professor Dept. of Computer Science & EngineeringMichigan State UniversityAnil K. Jain (PhD, 1973, Ohio State University; B. Tech., IIT Kanpur) is a University Distinguished Professor at Michigan State University where he conducts research in pattern recognition, machine learning, computer vision, and biometrics recognition. He was a member of the United States Defense Science Board and Forensics Science Standards Board. His prizes include Guggenheim, Humboldt, Fulbright, and King-Sun Fu Prize. For advancing pattern recognition, Jain was awarded Doctor Honoris Causa by Universidad Autónoma de Madrid. He was Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence and is a Fellow of ACM, IEEE, AAAS, and SPIE. Jain has been assigned 8 U.S. and Korean patents and is active in technology transfer for which he was elected to the National Academy of Inventors. Jain is a member of the U.S. National Academy of Engineering (NAE), foreign member of the Indian National Academy of Engineering (INAE), a member of The World Academy of Science (TWAS) and a foreign member of the Chinese Academy of Sciences (CAS). His list of publications is available at Google Scholar.
ANATOLE VON LILIENFELDProfessor, Department of ChemistryUniversity of BaselAnatole von Lilienfeld develops methods for the design of new chemicals through exploration of compound space using quantum mechanics, super computers, and machine learning. He is also interested in pseudopotentials, van der Waals forces, and nuclear quantum effects. As of Dec 2014, Anatole serves as a member of the Editorial Board of Nature’s Scientific Data. Since 2013, Anatole has been a Swiss National Science Foundation Assistant Professor in the Institute of Physical Chemistry at the University of Basel. Prior to that he has been member of scientific staff at the Argonne National Laboratory’s Leadership Computing Facility in Illinois which hosts MIRA, an IBM BlueGene/Q computer with nearly 0.8M compute cores—one of the world’s largest supercomputers accessible to open science and research. In spring 2011 he chaired the 3 months program, “Navigating Chemical Compound Space for Materials and Bio Design”, at the Institute for Pure and Applied Mathematics, UCLA, California. From 2007 to 2010 he was a Distinguished Harry S. Truman Fellow at Sandia National Laboratories, New Mexico. Anatole carried out postdoctoral research at the Max-Planck Institute for Polymer Research (2007) and at New York University (2006). He received a Ph.D. in computational chemistry from EPF Lausanne in 2005. He studied chemistry at ETH Zurich, the University of Cambridge (UK), Ecole de Chimie, Polymers,et Materiaux (ECPM) in Strasbourg, and at the University of Leipzig. Anatole descends from Baltic German refugees, he was born in Minnesota (1976), and grew up in Germany.
FLORENCE D'ALCHÉ-BUCProfessorTélécom ParisTechResearch scientist at Philips Research Lab from 1990 to 1993 as PhD student from 1993 to 1995 as project leader Associate professor at Université de Paris VI from 1995 to 2004 Full professor at University of Evry from 2004 to october 2014 Co-head of the IBISC lab from 2010 to 2011. Head of AMIS group from 2004 to 2013 Head of AROBAS group (formelly AMIS) from 2013 to 2014 Visiting INRIA (LRI, Université Paris XI) from 2011 to 2013 Specialties: Statistical Machine learning, structured output data, kernel methods dynamical systems, network inference, applications to systems biology and personalized medecine
BEEN KIMSenior Research ScientistGoogle BrainI am interested in designing high-performance machine learning methods that make sense to humans. My current focus is building interpretability method for already-trained models (e.g., high performance neural networks). In particular, I believe the language of explanations should include higher-level, human-friendly concepts.
- 09:00 - 10:15
- Part 1
- Speakers and moderators coming soon
- 10:15 - 11:00
- Coffee break
- 11:00 - 12:15
- Part 2
- Speakers and moderators coming soon
- 12:15 - 14:00
- Lunch break
- 14:00 - 15:15
- Part 3
- Speakers and moderators coming soon
- 15:15 - 16:00
- Coffee break
- 16:00 - 17:15
- Part 4
- Speakers and moderators coming soon