Keynote Speakers

On Effective Computer Science Education in the Era of Information Technology

Jie Wu, Laura H. Carnell Professor, Director
Department of Computer and Information Sciences, Center for Networked Computing
Temple University, USA

This talk focuses on the challenges and opportunities of computer science education. We start with ACM Computing curriculum recommendations for several related computing fields, including computer engineering, computer science, information systems, and information technology. In recent years, significant technological advances have occurred in the areas of big data, AI, cybersecurity and IoT. One key challenge is how to tailor curriculum to the rapidly changing landscape of computer technology while still maintaining the core identity of computing. We elaborate on the importance of computing education to the society as a whole and discuss possible pathways to expanding computing education, including to K-12 education. Finally, we discuss how we should educate our next generation by promoting creativity and compare different education systems and their objectives.

Short Bio:
Jie Wu is the Director of the Center for Networked Computing and Laura H. Carnell professor at Temple University. He also serves as the Director of International Affairs at College of Science and Technology. He served as Chair of Department of Computer and Information Sciences from the summer of 2009 to the summer of 2016 and Associate Vice Provost for International Affairs from the fall of 2015 to the summer of 2017. Prior to joining Temple University, he was a program director at the National Science Foundation and was a distinguished professor at Florida Atlantic University. His current research interests include mobile computing and wireless net- works, routing protocols, cloud and green computing, network trust and security, and social network applications. Dr. Wu regularly publishes in scholarly journals, conference proceedings, and books. He serves on several editorial boards, including IEEE Transactions on Service Computing and the Journal of Parallel and Distributed Computing. Dr. Wu was general co-chair for IEEE MASS 2006, IEEE IPDPS 2008, IEEE ICDCS 2013, ACM MobiHoc 2014, ICPP 2016, and IEEE CNS 2016, as well as program co-chair for IEEE INFOCOM 2011 and CCF CNCC 2013. He was an IEEE Computer Society Distinguished Visitor, ACM Distinguished Speaker, and chair for the IEEE Technical Committee on Distributed Processing (TCDP). Dr. Wu is a CCF Distinguished Speaker and a Fellow of the IEEE. He is the recipient of the 2011 China Computer Federation (CCF) Overseas Outstanding Achievement Award.

Deep Learning: New Frontiers and Applications in Healthcare and Automotive Industries

Ming Dong, Professor, Co-Director
Department of Computer Science, Data Science and Business Analytics Program
College of Engineering, Wayne State University, USA

The performance of machine learning approaches is heavily dependent on the choice and quality of data representation. Deep learning is part of a broader family of machine learning methods based on learning meaningful and effective data representations, as opposed to feature engineering. Convolutional Neural Networks (CNNs) are deep learning models that generate different feature maps of the input data by using a sequence of convolutional and pooling layers before classifying them using fully connected layers. In this talk, we will present some new ideas and applications of CNN in healthcare and automotive industry applications, including CNN with attention mechanism for the detection of the functionally-important white matter pathway in human brains, Generating Synthetic CTs from Magnetic Resonance Images using Generative Adversarial Networks and deep transfer learning with generalized fisher information.

Short Bio:
Ming Dong received his B. S. degrees in electrical engineering and industrial management engineering from Shanghai Jiao Tong University, Shanghai, China, in 1995. He received his Ph. D degree in electrical engineering from University of Cincinnati in 2001. He is currently a full professor in the Department Computer Science and a co-director of the Data Science and Business Analytics program at Wayne State University (WSU). He is also the advisor on student data management in the graduate school of WSU.
Dr. Dong's areas of research include deep learning, data mining, and computer vision. His research is funded by National Science Foundation, National Institutes of Health, State of Michigan, Private Foundations (e.g., Michigan Health Endorsement Fund, Epilepsy Foundation) and Industries (e.g., APB Investment, Ford Motor Company). He has published over 100 technical articles in premium journals and conferences in his research area such as CVPR and ICCV. He is currently an associate editor of Statistical Analysis and Data Mining, the American Statistical Association (ASA) Data Science Journal (since 2017) and Journal of Smart Health (Since 2016). He served in many conference program committees and US National Science Foundation panels.

Big Data Privacy Protection

Prof. Jinjun Chen
Swinburne University of Technology, Australia

While more and more data is being hosted on cloud, privacy protection is becoming increasingly worrying to the community. How to protect personal privacy in a timely manner and in an effective way is challenging given that there is huge amount of data records, data is transferred between different domains as well as different types of data is being hosted together. Especially, data privacy needs to be protected before data can be analyzed. In this talk, we will highlight our research in this area and main challenges we are trying to address.

Short Bio:
Dr Jinjun Chen is a Professor from Swinburne University of Technology, Australia. He is Deputy Director of Swinburne Data Science Research Institute, and Director of Data Systems and Advanced Analytics. He holds a PhD in Information Technology from Swinburne University of Technology, Australia. His research interests include scalability, big data, data science, data systems, cloud computing, data privacy and security, health data analytics and related various research topics. His research results have been published in more than 140 papers in international journals and conferences, including various IEEE/ACM Transactions.
He received UTS Vice-Chancellor's Awards for Research Excellence Highly Commended (2014), UTS Vice-Chancellor's Awards for Research Excellence Finalist (2013), Swinburne Vice-Chancellor’s Research Award (ECR) (2008), IEEE Computer Society Outstanding Leadership Award (2008-2009) and (2010-2011), IEEE Computer Society Service Award (2007), Swinburne Faculty of ICT Research Thesis Excellence Award (2007). He is an Associate Editor for ACM Computing Surveys, IEEE Transactions on Big Data, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cloud Computing, as well as other journals such as Journal of Computer and System Sciences.

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