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The 9th IEEE International Conference on Data Science and Advanced Analytics

October 13-16, 2022
Hybrid (Virtual and Onsite)
Shenzhen, China

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The 9th IEEE International Conference on
Data Science and Advanced Analytics

October 13-16, 2022
Hybrid (Virtual and Onsite)
Shenzhen, China

Keynotes

Keynote Speaker 1

Professor Xin Yao

Chair Professor of Computer Science at the Southern University of Science and Technology, Shenzhen, China.

Title: TBD

Abstract: ​ TBD

Biography:

Xin Yao is a Chair Professor of Computer Science at the Southern University of Science and Technology, Shenzhen, China, and a part-time Professor of Computer Science at the University of Birmingham, UK. His major research interests include evolutionary computation, ensemble learning and search-based software engineering. His work won the 2001 IEEE Donald G. Fink Prize Paper Award; 2010, 2015 and 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards; 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist); 2011 IEEE Transactions on Neural Networks Outstanding Paper Award; and many other best paper awards. He received a prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013. He was recently selected to receive the 2020 IEEE Frank Rosenblatt Award.

Keynote Speaker 2

Professor Vipin Kumar

Professor, William Norris Endowed Chair in the Department of Computer Science and Engineering, University of Minnesota, USA.

Title: TBD

Abstract: ​ TBD

Biography:

Vipin Kumar is a Regents Professor at the University of Minnesota, where he holds the William Norris Endowed Chair in the Department of Computer Science and Engineering. Kumar received the B.E. degree in Electronics & Communication Engineering from Indian Institute of Technology Roorkee (formerly, University of Roorkee), India, in 1977, the M.E. degree in Electronics Engineering from Philips International Institute, Eindhoven, Netherlands, in 1979, and the Ph.D. degree in Computer Science from University of Maryland, College Park, in 1982. He also served as the Head of the Computer Science and Engineering Department from 2005 to 2015 and the Director of Army High Performance Computing Research Center (AHPCRC) from 1998 to 2005. 

Kumar's current research interests span data mining, high-performance computing, and their applications in Climate/Ecosystems and health care. His research has resulted in the development of the concept of isoefficiency metric for evaluating the scalability of parallel algorithms, as well as highly efficient parallel algorithms and software for sparse matrix factorization (PSPASES) and graph partitioning (METIS, ParMetis, hMetis). He has authored over 300 research articles, and has coedited or coauthored 10 books including two text books "Introduction to Parallel Computing" and "Introduction to Data Mining", that are used world-wide and have been translated into many languages. Kumar's current major research focus is on bringing the power of big data and machine learning to understand the impact of human induced changes on the Earth and its environment. Kumar served as the Lead PI of a 5-year, $10 Million project, "Understanding Climate Change - A Data Driven Approach", funded by the NSF's Expeditions in Computing program that is aimed at pushing the boundaries of computer science research. 

Kumar has served as chair/co-chair for many international conferences in the area of data mining, big data, and high performance computing, including 25th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), 2015 IEEE International Conference on Big Data, IEEE International Conference on Data Mining (2002), and International Parallel and Distributed Processing Symposium (2001). Kumar co-founded SIAM International Conference on Data Mining and served as a founding co-editor-in-chief of Journal of Statistical Analysis and Data Mining (an official journal of the American Statistical Association). Currently, Kumar serves on the steering committees of the SIAM International Conference on Data Mining and the IEEE International Conference on Data Mining, and is series editor for the Data Mining and Knowledge Discovery Book Series published by CRC Press/Chapman Hall. 

Kumar has been elected a Fellow of the American Association for Advancement for Science (AAAS), Association for Computing Machinery (ACM), Institute of Electrical and Electronics Engineers (IEEE), and Society for Industrial and Applied Mathematics (SIAM). He received the Distinguished Alumnus Award from the Indian Institute of Technology (IIT) Roorkee (2013), the Distinguished Alumnus Award from the Computer Science Department, University of Maryland College Park (2009), and IEEE Computer Society's Technical Achievement Award (2005). Kumar's foundational research in data mining and high performance computing has been honored by the ACM SIGKDD 2012 Innovation Award, which is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD), and the 2016 IEEE Computer Society Sidney Fernbach Award, one of IEEE Computer Society's highest awards in high-performance computing. 

Keynote Speaker 3

 

Professor Limsoon Wong

Chair Professor in the School of Computing at the National University of Singapore.

Title: 

Some bad practices in data analysis and machine learning

Abstract: ​

With the democratization of data analysis and machine learning through many easy-to-use platforms, many lay analysts are now involved in analyzing data to hopefully produce actionable insight, as well as developing tools for modelling their data. Unlike professional statisticians who have the benefits of  many years of rigorous training and many years of practising and perfecting the art of data analysis, lay analysts (like me, a computer scientist and logician) have rather ad hoc training. As a result, we have developed some bad data analysis habits, and some of us have even irresponsibly propagated these. In this talk, I will explain and bring attention to a few of these bad habits (including misusing principal component analysis as a dimension reduction tool, misunderstanding correlation as association, and mistreating accuracy as a one-dimensional performance measure), as well as discuss some impact of these bad habits (e.g., self-perpetuation of biased datasets.)

Biography:

Limsoon Wong is Kwan-Im-Thong-Hood-Cho-Temple Chair Professor in the School of Computing at the National University of Singapore (NUS). He was also a professor (now honorary) of pathology in the Yong Loo Lin School of Medicine at NUS. Limsoon is a Fellow of the ACM, named in 2013 for his contributions to database theory and computational biology. His other recent awards in these two fields include the 2003 FEER Asian Innovation Gold Award for his work on treatment optimization of childhood leukemias and the ICDT 2014 Test of Time Award for his work on naturally embedded query languages.

Keynote Speaker 4

Dr. Gabriela Csurka

Principal Scientist at NAVER LABS Europe, France

Title:

 Visual Domain Adaptation in the Deep Learning Era

Abstract: ​

As computer vision systems are being deployed in mission critical applications whose predictions have real-world impact, but where real-world testing data statistics differ significantly from lab-collected training data, domain adaptation (DA) is gaining an increasing societal importance. The aim of this talk is to give an overview of visual domain adaptation methods, starting with a brief introduction and recall of traditional domain adaptation algorithms proposed before the deep learning era.  Then, I will provide an overview of the main trends in deep domain adaptation and I will discuss how to handle situations that depart form the classic domain adaptation setting such as multi-domain learning, domain generalization, test-time adaptation or source-free domain adaptation. During the talk, I will discuss different DA application scenarios such as autonomous driving, visual localization, biomedical imaging, biometry and surveillance.

Biography:

Gabriela Csurka is a Principal Scientist at NAVER LABS Europe, France. Her main research interests are in computer vision for image understanding, 3D reconstruction, visual localization  as well as domain adaptation and transfer learning. She has contributed to more than 100 scientific communications, many in major CV conferences and journals. Concerning domain adaptation, in addition to related publications,  she has given several invited talks and organized a related tutorial at ECCV’20. In 2017 she edited a Springer book entitled Domain Adaptation for Computer Vision Applications and recently co-authored a Morgan & Clayton book entitled Visual Domain Adaptation in the Deep Learning Era which is under publication.