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Jure Leskovec is an Associate Professor of Computer Science at Stanford University, and investigator at Chan Zuckerberg Biohub. His general research area is applied machine learning for large interconnected systems focusing on modeling complex, richly-labeled relational structures, graphs, and networks for systems at all scales, from interactions of proteins in a cell to interactions between humans in a society. Applications include commonsense reasoning, recommender systems, computational social science, and computational biology with an emphasis on drug discovery.
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Michael Bronstein is a Professor at ICL, Department of Computing. He has served as a professor at USI Lugano, Switzerland since 2010 and held visiting positions at Stanford, Harvard, MIT, TUM, and Tel Aviv University. Michael received his PhD with distinction from the Technion (Israel Institute of Technology) in 2007. His main expertise is in theoretical and computational geometric methods for machine learning and data science, and his research encompasses a broad spectrum of applications ranging from computer vision and pattern recognition to geometry processing, computer graphics, and biomedicine.
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Stephan Günnemann is a professor for Data Analytics and Machine Learning at Technical University of Munich and an acting director of the Munich Data Science Institute. He conducts research in the area of machine learning and data analytics. His main research focuses on how to make machine learning techniques reliable, thus, enabling their safe and robust use in various application domains. He is particularly interested in studying machine learning methods targeting complex data domains such as graphs/networks and temporal data.
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Pan Li is an Assistant Professor at Purdue University. Before that, he was a postdoc in the SNAP group at Stanford, where he worked with Prof. Jure Leskovec. His research focuses on computational and machine learning methods for data analysis, especially graph or network structured data. This combines interests in graph theory, machine learning, computational science and statistics.
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Xia Hu is an Associate Professor in Computer Science at Rice University. He is currently directing the DATA Lab, with his students and collaborators, they strive to develop automated and interpretable data mining and machine learning algorithms with theoretical properties to better discover actionable patterns from large-scale, networked, dynamic and sparse data. Their research is directly motivated by, and contributes to, applications in social informatics, health informatics and information security. Their work has been featured in Various News Media, such as MIT Tech Review, ACM TechNews, New Scientist, Fast Company, Economic Times. Their research is generously supported by federal agencies such as DARPA (XAI, D3M and NGS2) and NSF (CAREER, III, SaTC, CRII, S&AS), and industrial sponsors such as Adobe, Apple, Alibaba, Google, LinkedIn and JP Morgan. He was the General Co-Chair for WSDM 2020.
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Renjie Liao is a Visiting Faculty Researcher at Google Brain, working with Geoffrey Hinton and David Fleet. He's also affiliated with Vector Institute. He will join UBC ECE and CS (associated) as an assistant professor in Jan. 2022. He's interested in machine learning and its interplay with vision, self-driving, language, and other areas. In the long run, He is keen to build learning systems that excel at integrating perception and reasoning. Recently, He's been focusing on deep learning for structures, e.g., graphs.
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