Biography: 

Dr. Md Rafiqul Islam is a postdoctoral research associate in data science at the Data Science and Machine Learning (DSMI) Lab, University of Technology Sydney (UTS), Australia. He obtained his Ph.D. in Data Analytics from the University of Technology Sydney (UTS), Australia, and his M.Sc. in Computer Science and Engineering from the Islamic University of Technology (IUT), Bangladesh. He has 8+ years of research and teaching experience in computer science. His research interests include data analytics, behavior analysis, visualization, big data, healthcare, and information system. He has published over 30 research papers in highly recognized journals and conference proceedings. Besides, he is an associate editor of the well-known Human-Centric Intelligent Systems, Frontiers in medical technology, Journal of Computer Science, and reviewer of 30 internationally well-recognized journals such as Knowledge-Based System, IEEE Access, Social Network Analysis and Mining (Springer), World Wide Web (Springer), etc. He also serves as an organizing committee member, BESC-2021, BESC-2022, PT-2022, HIS-2021, ISIC-2022, and NLPIR-2021, and a PC member in multiple international conferences.

Speech title: Robust Visual Analytics and its Applications

Abstract: We are living in the age of data science (DS), where the uses of massive amounts of data are rapidly increasing in many applications such as (i) healthcare analysis, (ii) fintech ecosystem, and (iii) tourism management, etc. The analysis of these data is grimy, irreconcilable, and complex. As a result, vast amounts of time and money are often lost. To address these data issues, new advancements such as visual Analytics blending with artificial intelligence (AI) techniques have received significant attention for solving various complex problems. Usually, it consists of two major parts such as 1) data visualization which is an emerging field in the current situation, 2) AI which adds more insights, excels at knowledge communication, and discovering strategies by applying encoding techniques to transfer abstract data into meaningful representation. It fosters the worthwhile assessment, correction, and rapid improvement of big data with meaningful interactive visualization (IV). Finally, it views as the science of analytical reasoning empowered by interactive visualizations.