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

October 13-16, 2022
Online

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

October 13-16, 2022
Online

Program at a Glance

The following time is based on your local time, i.e.,

Please click each session for detailed information

Day 1
Your Local Time UTC+8(China) Webinar 1 Webinar 2 Webinar 3
Oct 13, 9:00-9:20 10/13, 9:00-9:20 Welcome
Session Chair: Zhexue Huang
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Oct 13, 9:20-10:20 10/13, 9:20-10:20 Keynote 1: Knowledge-Guided Machine Learning: A New Framework for Accelerating Scientific Discovery and Addressing Global Environmental Challenges
Presenter: Vipin Kumar
Session Chair: Xing Xie
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Oct 13, 10:25-12:05 10/13, 10:25-12:05 Research 1: Classification and Supervised Machine Learning
Session Chair: Xulong Zhang
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Application 1: Data Quality and AI
Session Chair: Yaodong Huang
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Oct 13, 14:00-15:20 10/13, 14:00-15:20 Research 2: Semi-supervised Machine Learning
Session Chair: Yu Zhou
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Tutorial 1: Mining of Real-world Hypergraphs: Patterns, Tools, and Generators
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Special Session 1: Computational Social Science and Complex Systems
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Oct 13, 15:30-16:50 10/13, 15:30-16:50 Research 3: Unsupervised Machine Learning for Data Analysis
Session Chair: Samer Khamasieh
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Day 2
Your Local Time UTC+8(China) Webinar 1 Webinar 2 Webinar 3
Oct 14, 9:00-10:20 10/14, 9:00-10:20 Research 4: Deep Learning Alrogrithms
Session Chair: Yaping Zhao
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Tutorial 2: Graph Neural Networks for Tabular Data Learning
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CEST (UTC+2) 14:00-17:00
Special Session 2: Practical Applications of Explainable Artificial Intelligence Methods
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Oct 14, 10:25-11:45 10/14, 10:25-11:45 Research 5: Pattern Recognition
Session Chair: Yirui Wu
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Oct 14, 14:00-15:00 10/14, 14:00-15:00 Keynote 2: Visual Domain Adaptation in the Deep Learning Era
Presenter: Gabriela Csurka
Session Chair: Laizhong Cui
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Oct 14, 15:05-16:25 10/14, 15:05-16:25 Research 6: Computer Vision and Data Visualization
Session Chair: Rushanthi Baskaran
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Application 2: Environment and Traffic Applications
Session Chair: Cho Yin Yiu
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Oct 14, 16:30-16:50 10/14, 16:30-16:50 Poster 1: Student Poster (1-4)
Session Chair: Chengliang Chai
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PhD Track
Session Chair: Philippe Fournier-Viger
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Day 3
Your Local Time UTC+8(China) Webinar 1 Webinar 2 Webinar 3
Oct 15, 9:00-10:00 10/15, 9:00-10:00 Keynote 3: Some Bad Practices in Data Analysis and Machine Learning
Presenter: Limsoon Wang
Session Chair: Longbing Cao
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Oct 15, 10:05-11:25 10/15, 10:05-11:25 Research 7: Optimization Theories and Methods
Session Chair: Hao Liao
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Application 3: Social Networks
Session Chair: Gabriel Gomes de Oliveira
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Oct 15, 11:30-12:00 10/15, 11:30-12:00 Poster 2: Student Poster (5-8) & Industry Poster
Session Chair: Siyuan Ren,
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Special Session 3: Data Science for Cyber and National Security
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Oct 15, 14:00-15:20 10/15, 14:00-15:20 Research 8: Big Data Analysis
Session Chair: Animesh Chaturvedi
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Application 4: Business and Government Analytics
Session Chair: Yaodong Huang
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Special Session 5: Private, Secure, and Trust Data Analytics
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Oct 15, 15:30-16:50 10/15, 15:30-16:50 Research 9: High Performance Computing
Session Chair: Joana Cristo Santos
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Special Session 4: Data Science and Advanced Analytics for Autonomous Vehicles
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Day 4
Your Local Time UTC+8(China) Webinar 1 Webinar 2 Webinar 3
Oct 16, 9:00-10:00 10/16, 9:00-10:00 Keynote 4: Online Learning of Data Streams with Concept Drift
Presenter: Xin Yao
Session Chair: Yi Pan
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Oct 16, 10:05-11:25 10/16, 10:05-11:25 Research 10: Advanced Data Analytics
Session Chair: Lei Zhang
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Journal Track: MLJ Special Issue on Foundations of Data Science
Session Chair: Carson Leung
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Oct 16, 11:30-12:00 10/16, 11:30-12:00 NGDS Award Session
Session Chair: Lei Zhang
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Oct 16, 14:00-15:20 10/16, 14:00-15:20 Research 11: Mathematics and Statistics for Data Sciences
Session Chair: Qin Zhang
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Application 5: Medical Applications
Session Chair: Qiwei Han
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Special Session 7: Enabled Trustworthy Recommendations
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Oct 16, 15:30-17:10 10/16, 15:30-17:10 Research 12: Data Characteristics and Explanability
Session Chair: Alessandro Leite
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Special Session 6: Data Science for Social and Behavioral Analytics
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