Keynote Speaker I


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.

Keynote Speaker II

Professor Hai Jin,  IEEE Fellow, CCF Fellow

School of Computer Science and Technology, Huazhong University of Science and Technology, China


Talk Title:

Abstract: 

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

Prof. Yang Kuang

School of Mathematical and Statistical Sciences, Arizona State University, USA.



Talk Title: Existence and Implications of Traveling Wave Solutions in Reaction Diffusion Models of Brain Cancer Growth

Abstract: Glioblastoma multiforme (GBM) is an aggressive brain cancer that is extremely fatal.  It is characterized by aggressive proliferation and fast migration, which contributes to the difficulty of treatment.  Based on the so-called go or grow hypothesis, existing models of GBM growth often include two separate equations to model proliferation or migration processes.  Motivated by an in vitro experiment data set of GBM growth, we formulate, validate, simulate, study and compare two plausible models of GBM growth. We propose first a single equation which uses density dependent diffusion to capture the behavior of both proliferation and migration. We analyze the model to determine the existence of traveling wave solutions. To prove the viability of the density-dependent diffusion function chosen, we compare our model with the in vitro experimental data. Our second model is build on the Go or Grow hypothesis since glioma cells tend to exhibit a dichotomous behavior: a cell either primarily proliferates or primarily migrates. For this model, different solution types are examined via approximate solution of traveling wave equations and we determine conditions for various wave front forms.

Bio.: Yang Kuang is a professor of mathematics at Arizona State University (ASU) since 1988. He received his B.Sc from the University of Science and Technology of China in 1984 and his Ph.D degree in mathematics in 1988 from the University of Alberta. Dr. Kuang is the author of more than 180 refereed journal publications and 16 books (including 11 special issues) and the founder and editor of Mathematical Biosciences and Engineering. He has directed 23 Ph.D dissertations in mathematical and computational biology and several major (funding exceeding $1m) multi-disciplinary research projects in US. He is well  known for his efforts in developing practical theories to the study of delay differential equation models and models incorporating resource quality in biology and medicine. His recent research interests focus on the formulation and validation of scientifically well-grounded and computationally tractable mathematical models to describe the rich and intriguing dynamics of various within-host diseases and their treatments. These models have the potential to speed up much needed personalized medicine development.


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