Keynote Speaker I
Prof. Yang Kuang
School of Mathematical and Statistical Sciences, Arizona State University, USA.
Bio.: Yang Kuang is the author or co-author of more than 150 refereed publications and books in mathematical biology and medicine. His most recent book is "Introduction to Mathematical Oncology" (2016). He founded and continues to edit the journal of Mathematical Biosciences and Engineering. He has done extensive work in applying delay differential equations to models of biology and medicine. His research interests are mostly data or hypothesis inspired modeling efforts. His models often incorporate the chemical heterogeneity which is ubiquitous and highlight the importance of resource quantity and quality dynamics. Recently, he focuses on the formulation of scientifically well-grounded and computationally tractable mathematical models to describe the rich and intriguing dynamics of various within-host diseases and their treatments. Some of these models are being tested in clinical trials on late-stage prostate cancer treatment.
Keynote Speaker II
Professor Hai Jin, IEEE Fellow, CCF Fellow
School of Computer Science and Technology, Huazhong University of Science and Technology, China
Bio.: Hai Jin is a Professor of Computer Science and Engineering at the Huazhong University of Science and Technology (HUST) in China. He is now the Dean of School of Computer Science and Technology at HUST. He received his Ph.D. in computer engineering from HUST in 1994. In 1996, he was awarded the German Academic Exchange Service (DAAD) fellowship for visiting the Technical University of Chemnitz in Germany. He worked for the University of Hong Kong between 1998 and 2000 and participated in the HKU Cluster project. He worked as a visiting scholar at the University of Southern California between 1999 and 2000. He is the chief scientist of the largest grid computing project, ChinaGrid, in China. Dr. Jin is a senior member of IEEE and a member of ACM. He is a member of the Grid Forum Steering Group (GFSG). His research interests include computer architecture, cluster computing, and grid computing, virtualization technology, peer-to-peer computing, network storage, network security.
Keynote Speaker III
Professor Amir H Gandomi
The University of Technology Sydney, Australia
Talk Title:EVOLUTIONARY (BIG) DATA MINING
Abstract: Evolutionary computation (EC) has been widely used during the last two decades and has remained a highly-researched topic, especially for complex real-world problems. The EC techniques are a subset of artificial intelligence, but they are slightly different from the classical methods in the sense that the intelligence of EC comes from biological systems or nature in general. The efficiency of EC is due to their significant ability to imitate the best features of nature which have evolved by natural selection over millions of years. The central theme of this presentation is about EC techniques and their application to civil structures and infrastructures. On this basis, the presentation I about an evolutionary approach called genetic programming for data mining. Applied evolutionary computing in data mining field will be presented, and then their new advances will be mentioned such as big data mining. Here, some of my studies on big data mining and modeling using EC and genetic programming, in particular, will be presented. As a case study, EC application in one structural health monitoring problem, inverse identification, will be introduced. And then, the application of EC for response modeling of a new structural system under seismic loads will be explained in detail to demonstrate the applicability of these algorithms on a complex real-world problem.
Bio.: Amir H. Gandomi is a Professor of Data Science at the Faculty of Engineering & Information Technology, University of Technology Sydney. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at the School of Business, Stevens Institute of Technology, USA and a distinguished research fellow in BEACON center, Michigan State University, USA. Prof. Gandomi has published over one hundred and seventy journal papers and seven books which collectively have been cited more than 15,000 times (H-index = 57). He has been named as one of the most influential scientific mind and Highly Cited Researcher (top 1%) for three consecutive years, 2017 to 2019. He also ranked 18th in GP bibliography among more than 12,000 researchers. He has served as associate editor, editor and guest editor in several prestigious journals such as AE of SWEVO, IEEE TBD, and IEEE IoTJ. Prof Gandomi is active in delivering keynote and invited talks. His research interests are global optimisation and (big) data mining using machine learning and evolutionary computations in particular.