Keynote Speakers

The Outlook of Machine Learning in Medicine
with an Example in Radiation Cancer Therapy

Xiaodong Wu, PhD, Professor
Biophysics Research Institute of America, USA
Department of Biomedical Engineering, University of Miami
Director of International Consortium, Shanghai Proton and Heavy Ion Center

Big Data and Machine Learning have been a new phenomenon that finds its way to impact many aspects of modern informatics. The speed and degree with which this impact brought the advancement upon modern technology has been unprecedented. Parallel to other fields, the implication of Bid Data and Machine learning in medicine in terms of diagnosis and treatment has gradually gained attention and the new dynamics of development has been clearly underway. Historically, good medicine relies on two indispensible pillars, human experience and instrumentation. While industrial technology continuing to push the envelop of advancing the modern medical instrumentation, the Bid Data and Machine Learning emerged as an unexpected ¡°gift¡± that reveals unusual ability of elevating cumulative knowledge in terms of human intervention to a whole new level. In this presentation we will use radiation cancer therapy as an example to discuss how machine learning could advance the quality of cancer treatment. In the standard practice of radiation oncology, physicians and medical physicists analyze each patient¡¯s disease with respects to the type, size and anatomic location to design an ¡°optimal¡± plan that would yield most desirable outcome. In this aspect, human factor in terms of experience plays a crucial role in determining the clinical outcome. Using knowledge-based machine learning technology, cumulative human experience could be leveraged to produce an optimal plan consistently, reliably and efficiently. The development of such technology is still in its infancy and the application has not been widely available, appreciated and assimilated. It is with supreme confidence that the presenter anticipates a most optimistic future of the application of Bid Data/Machine in modern medicine.

Dr. Xiaodong Wu received his B.S. in Theoretical Physics from China¡¯s Xiamen University and obtained his M.S. in Radiological Sciences and Ph.D. in Biomedical Engineering from University of Miami. Dr. Wu joined the University of Miami in1989 as a faculty member in the Department of Radiation Oncology and became the Director of Medical Physics in 1997. He held professorship in both Radiation Oncology and Biomedical Engineering. In 2012 he founded Biophysics Research Institute of America and has been the president of the company since. Dr. Wu is widely published and is a frequent lecturer in Medical Physics worldwide. He owns many patents and a number of pending patents related to Radiation Therapy and Radiosurgery. Throughout his career, Dr. Wu led the establishment of many successful modern radiation therapy and radiosurgery centers. In the recent years he has contributed to the technical establishment of the Shanghai Proton and Heavy Ion Center is currently the center¡¯s Director of International Consortium.

Searchable Symmetric Encryption: Potential Attacks, Practical Constructions and Extensions

Dr. Jinjun Chen
University of Technology Sydney (UTS), Australia

Data outsourcing has become one of the most successful applications of cloud computing, as it significantly reduces data owners' costs on data storage and management. To prevent privacy disclosure, sensitive data has to be encrypted before outsourcing. Traditional encryption tools such as AES, however, destroy the data searchability because keyword searches cannot be performed over encrypted data. Though the above issue has been addressed by an advanced cryptographic primitive, called searchable symmetric encryption (SSE), we observe that existing SSE schemes still suffer security, efficiency or functionality flaws. In this research, we further study SSE on three aspects. Firstly, we address the search pattern leakage issue. We demonstrate that potential attacks are exist as long as an adversary with some background knowledge learns users' search pattern. We then develop a general countermeasure to transform an existing SSE scheme to a new scheme where the search pattern is hidden. Secondly, motivated by the practical phenomenon in data outsourcing scenarios that user data is distributed over multiple data sources, we propose efficient SSE constructions which allow each data source to build a local index individually and enable the storage provider to merge all local indexes into a global one. Thirdly, we extend SSE into graph encryption with support for specific graph queries. E.g., we investigate how to perform shortest distance queries on an encrypted graph.

Short Bio:
Dr Jinjun Chen is a Professor from Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia. He is the Director of Lab for Data Systems and Visual Analytics in the Global Big Data Technologies Centre at UTS. He holds a PhD in Information Technology from Swinburne University of Technology, Australia. His research interests include scalability, big data, data science, data intensive systems, cloud computing, workflow management, privacy and security, and related various research topics. His research results have been published in more than 130 papers in international journals and conferences, including ACM Transactions on Software Engineering and Methodology (TOSEM), IEEE Transactions on Software Engineering (TSE), IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Cloud Computing, IEEE Transactions on Computers (TC), IEEE Transactions on Service Computing, and IEEE TKDE.

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, JNCA. He is the Chair of IEEE Computer Society¡¯s Technical Committee on Scalable Computing (TCSC), Vice Chair of Steering Committee of Australasian Symposium on Parallel and Distributed Computing, Founder and Coordinator of IEEE TCSC Technical Area on Big Data and MapReduce, Founder and Steering Committee Co-Chair of IEEE International Conference on Big Data and Cloud Computing, IEEE International Conference on Big Data Science and Engineering, and IEEE International Conference on Data Science and Systems.

Cyber-Enabled Services and Applications in Medicine and Education

Moderator: Qun Jin, Waseda University, Japan
Panelists: Xiaodong Wu, Biophysics Research Institute of America, USA
Jinjun Chen, University of Technology Sydney, Australia
Shaozi Li, Xiamen University, China
Hong Liu, Shandong Normal University, China
Huijuan Lu, China Jiliang University, China
Xuemei Yang, Fujian University of Traditional Chinese Medicine, China

In this panel, the moderator and panelists will present their views on emerging cyber technologies, such as Ubiquitous Clouds, Big Data, Internet of Things, Smart City, Machine Learning, Personal Analytics, Virtual/Augmented Reality, and their applications in medicine and education, and discuss the opportunities, challenges, potential solutions, and promising technologies from the perspectives of their different fields.

Short Bio of the Moderator
Qun Jin is currently a tenured full professor of the Department of Human Informatics and Cognitive Sciences, Faculty of Human Sciences, Waseda University, Japan. He has been engaged extensively in research works in the fields of computer science, information systems, and social and human informatics. He seeks to exploit the rich interdependence between theory and practice in his work with interdisciplinary and integrated approaches. Dr. Jin has published more than 150 refereed papers in the world-renowned academic journals, such as ACM Transactions on Intelligent Systems and Technology, IEEE Transactions on Learning Technologies, IEEE Systems Journal, and Information Sciences (Elsevier), and international conference proceedings in the related research areas. He has served as a general chair, program chair and TPC member for numerous international conferences, and editor-in-chief, associate editor, editorial board member and guest editor for a number of scietific journals. His recent research interests cover human-centric computing, ubiquitous computing, human¨Ccomputer interaction, behavior and cognitive informatics, big data, personal analytics and individual modeling, MOOCs and learning analytics, e-learning, e-health, and computing for well-being.

1 International Conference on Information Technology in Medicine and Education