Bio: Jian Pei is a Professor in the School of Computing Science at Simon Fraser University. He is a renown leading researcher in the general areas of data science, big data, data mining, and database systems. His expertise is in developing effective and efficient data analysis techniques for novel data intensive applications, and transferring his research results to products and business practice. He is recognized as a Fellow of the Royal Society of Canada (Canada's national academy), the Canadian Academy of Engineering, the Association of Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). He is one of the most cited authors in data mining, database systems, and information retrieval. Since 2000, he has published one textbook, two monographs and over 300 research papers in refereed journals and conferences, which have been cited extensively by others. His research has generated remarkable impact substantially beyond academia. For example, his algorithms have been adopted by industry in production and popular open source software suites. Jian Pei also demonstrated outstanding professional leadership in many academic organizations and activities. He was the editor-in-chief of the IEEE Transactions of Knowledge and Data Engineering (TKDE) in 2013-16, the chair of the ACM Special Interest Group on Knowledge Discovery in Data (SIGKDD) in 2017-2021, and a general co-chair or program committee co-chair of many premier conferences. He maintains a wide spectrum of industry relations with both global and local industry partners. He is an active consultant and coach for industry. He received many prestigious awards, including the 2017 ACM SIGKDD Innovation Award, the 2015 ACM SIGKDD Service Award, the 2014 IEEE ICDM Research Contributions Award, the British Columbia Innovation Council 2005 Young Innovator Award, an NSERC 2008 Discovery Accelerator Supplements Award, an IBM Faculty Award (2006), a KDD Best Application Paper Award (2008), an ICDE Influential Paper Award (2018), a PAKDD Best Paper Award (2014), and a PAKDD Most Influential Paper Award (2009).

Bio: Duen Horng (Polo) Chau is an Associate Professor of Computing at Georgia Tech. He co-directs Georgia Tech's MS Analytics program. He is the Director of Industry Relations of The Institute for Data Engineering and Science (IDEaS), and the Associate Director of Corporate Relations of The Center for Machine Learning. His research group bridges machine learning and visualization to synthesize scalable interactive tools for making sense of massive datasets, interpreting complex AI models, and solving real world problems in cybersecurity, human-centered AI, graph visualization and mining, and social good. His Ph.D. in Machine Learning from Carnegie Mellon University won CMU's Computer Science Dissertation Award, Honorable Mention. He received awards and grants from NSF, NIH, NASA, DARPA, Intel (Intel Outstanding Researcher), Google, Facebook, NVIDIA, Bosch, Amazon, Microsoft, Cisco, Symantec, eBay, Yahoo, LexisNexis; Raytheon Faculty Fellowship; Edenfield Faculty Fellowship; Outstanding Junior Faculty Award; The Lester Endowment Award; Symantec fellowship (twice); IEEE VIS'20 Best Poster Research Award, Honorable Mention; ACM TiiS 2018 Best Paper, Honorable Mention, Best student papers at SDM'14 and KDD'16 (runner-up); Best demo at SIGMOD'17 (runner-up); Chinese CHI'18 Best paper. His research led to open-sourced or deployed technologies by Intel (for ISTC-ARSA: ShapeShifter, SHIELD, ADAGIO, MLsploit), Google (GAN Lab), Facebook (ActiVis), Symantec (Polonium, AESOP protect 120M people from malware), and Atlanta Fire Rescue Department. His security and fraud detection research made headlines. He is a steering committee member of ACM IUI conference, IUI’15 co-chair, and IUI’19 program co-chair. He is an Associate Editor for IEEE TVCG. He was publicity chair for ACM KDD'14 and ACM WSDM'16 He co-organized the popular IDEA workshop (at KDD) that catalyzes cross-pollination across HCI and data mining.