Keynote Speakers

Prof. Tanveer Syeda-Mahmood
IEEE Fellow, IBM Fellow, IBM Research - Almaden
Distinguished Fellow, AIMI Center, Stanford University, USA

Dr. Tanveer Syeda-Mahmood is an IBM Fellow and the Chief Scientist/overall lead for the global Medical Sieve Radiology Grand Challenge project in IBM Research. As a worldwide expert in artificial Intelligence for medical imaging and clinical decision support, she is leading the company's future in cognitive health and helping define new IBM products through her groups research in biomedical imaging, computer vision, deep learning, knowledge and reasoning.

Dr. Tanveer Syeda-Mahmood graduated with a Ph.D from the MIT Artificial Intelligence Lab in 1993. Prior to coming to IBM, Dr. Syeda-Mahmood led the image indexing program at Xerox Research and was one of the early originators of the field of content-based image and video retrieval. Over the past 30 years, her research interests have been in a variety of areas relating to artificial intelligence ranging from computer vision, image and video databases, to recent applications in medical image analysis, healthcare informatics and clinical decision support. She has over 250 refereed publications and over 100 filed patents. Dr. Syeda-Mahmood has chaired numerous conferences and workshops over the years at forums such as IEEE CVPR, ICCV, ACM, and MICCAI including MICCAI 2016 (Industrial Chair), IEEE HISB 2011 (General Chair), and IEEE CVPR 2008 (Program Chair).

Dr. Syeda-Mahmood is a Fellow of IEEE. She is also a member of IBM Academy of Technology. Dr. Syeda-Mahmood was declared Master Inventor in 2011. She is the recipient of key awards including IBM Corporate Award 2015, Best of IBM Award 2015, 2016 and several outstanding innovation awards. In 2016, she received the highest technical honor at IBM and was conferred the title of IBM Fellow.

Speech Title: Chest X-Ray Report Generation Through Fine-Grained Label Learning

Abstract: Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals. However, the quality of reports generated by current automated approaches is not yet clinically acceptable as they cannot ensure the correct detection of a broad spectrum of radiographic findings nor describe them accurately in terms of laterality, anatomical location, severity, etc. In this work, we present a domain-aware automatic chest X-ray radiology report generation algorithm that learns fine-grained description of findings from images and uses their pattern of occurrences to retrieve and customize similar reports from a large report database. We also develop an automatic labeling algorithm for assigning such descriptors to images and build a novel deep learning network that recognizes both coarse and fine-grained descriptions of findings. The resulting report generation algorithm significantly outperforms the state of the art using established metric.

Prof. Changsheng Xu, Chinese Academy of Sciences, China

ACM Distinguished ScientistIEEE Fellow, and IAPR Fellow

Changsheng Xu is a professor of Institute of Automation, Chinese Academy of Sciences. His research interests include multimedia content analysis/indexing/retrieval, pattern recognition and computer vision. He has hold 50+ granted/pending patents and published over 400 refereed research papers including 100+ IEEE/ACM Trans. papers in these areas. Prof. Xu serves as Editor-in-Chief of Multimedia Systems Journal and Associate Editor of ACM Trans. on Multimedia Computing, Communications and Applications. He received the Best Paper Awards of ACM Multimedia 2016, 2016 ACM Trans. on Multimedia Computing, Communications and Applications and 2017 IEEE Multimedia. He served as Associate Editor of IEEE Transactions on Multimedia and Program Chair of ACM Multimedia 2009. He has served as associate editor, guest editor, general chair, program chair, area/track chair and TPC member for over 20 IEEE and ACM prestigious multimedia journals, conferences and workshops. He is an ACM Distinguished Scientist, IEEE Fellow, and IAPR Fellow.

Speech Title: Connecting Isolated Social Multimedia Big Data
Abstract: The explosion of social media has led to various Online Social Networking (OSN) services. Today's typical netizens are using a multitude of OSN services. Exploring the user-contributed cross-OSN heterogeneous data is critical to connect between the separated data islands and facilitate value mining from big social multimedia. From the perspective of data fusion, understanding the association among cross-OSN data is fundamental to advanced social media analysis and applications. From the perspective of user modeling, exploiting the available user data on different OSNs contributes to an integrated online user profile and thus improved customized social media services. This talk will introduce a user-centric research paradigm for cross-OSN mining and applications and some pilot works along two basic tasks: (1) From users: cross-OSN association mining and (2) For users: cross-OSN user modeling.

Prof. Jianfei Cai 

IEEE Fellow, Monash University, Australia

Jianfei is a Professor at Faculty of IT, Monash University, where he currently serves as the Head for the Data Science & AI Department. Before that, he was a full professor, a cluster deputy director of Data Science & AI Research center (DSAIR), Head of Visual and Interactive Computing Division and Head of Computer Communications Division in Nanyang Technological University (NTU). His major research interests include computer vision and multimedia. He has published more than 250+ technical papers in international conferences and journals, and has successfully trained 20+ PhD students. Many of them joined leading IT companies such as Facebook, Apple, Amazon, NVIDIA and Adobe or become faculty members in reputable universities. He is a co-recipient of paper awards in ACCV, ICCM, IEEE ICIP and MMSP. He has served as an Associate Editor for IEEE T-IP, T-MM, and T-CSVT as well as serving as Area Chair for ICCV, ECCV, CVPR, IJCAI, ACM Multimedia, ICME and ICIP. He was the Chair of IEEE CAS VSPC-TC during 2016-2018. He had also served as the leading TPC Chair for IEEE ICME 2012 and the best paper award committee chair & co-chair for IEEE T-MM 2020 & 2019. He is a Fellow of IEEE.

Speech Title: Bridging Vision and Language for Image Captioning
Abstract: As human beings, we can use our vision capabilities and language to perceive the world around us and to communicate with each other. While it seems to be easy for human beings to accomplish a wide variety of tasks that combine the two modalities, it is quite challenging for machines because it requires the model to understand both images and language, especially how they relate to each other. In this talk, I will discuss a few of my group’s recent works to bridge images and natural language via leveraging language inductive bias for the application of image captioning. I will also touch the future directions along this line.

Prof. Hong Shen (沈鸿教授), Sun Yat-sen University, China

Hong Shen is a specially-appointed and endowed Professor in Sun Yat-sen University, China. He is also a tenured Professor of Computer Science in the University of Adelaide, Australia.  He received the B.Eng. degree from Beijing University of Science and Technology, M.Eng. degree from University of Science and Technology of China, Ph.Lic. and Ph.D. degrees from Abo Akademi University, Finland, all in Computer Science. He was Professor and Chair of the Computer Networks Laboratory in Japan Advanced Institute of Science and Technology (JAIST) during 2001-2006, and Professor of Compute Science at Griffith University, Australia, where he taught 9 years since 1992. With main research interests in parallel and distributed computing, algorithms, data mining, privacy-preserving computing and high performance networks, he has led numerous research centres and projects in different countries. He has published more than 400 papers including over 100 papers in international journals such as a variety of IEEE and ACM transactions. Prof. Shen received many honours/awards including National Endowed Expert of China and “100 Talents” of Chinese Academy of Sciences. He has served on the editorial boards of several major international journals and chaired numerous conferences.

Speech Title: The Power of Differential Privacy for Secure Data Sharing
Abstract: In the era of cloud computing with the evolving demand of big data processing, privacy-preserving computing (PPC) has arisen as an effective way to achieve secure distributed computing and information sharing which serves as the base for realization of widespread Smart City and e-Society. PPC requires to develop a computation paradigm for solving a given problem that takes privacy-protected data as input and produces an output that is utilizable to the public yet secure against privacy attacks. There is a rich literature on the topic and numerous advancements have appeared in the past decade with the focus on improved security against various privacy attacks in the cloud computing environment. In these PPC paradigms, the demand of security assurance against emerging privacy attacks makes the task of maintaining output's utility to public become ever more challenging. In the first part of this talk, I will first introduce the problem of privacy-preserving computing, its research challenges in cloud big data computing, then give a taxonomy on data protection techniques categorized on the security levels of data publishing, with the focus on differential privacy as an effective method to combat inference attacks, and provide an overview on our contributions in privacy-preserving computing. In the second part, to show the power of differential privacy for secure data sharing, I will give two examples of our work of applying differential privacy to achieve privacy-preserving recommendation and data clustering against inference attacks. As concluding remarks, I will further illustrate the application of differential privacy in obtaining privacy-preserving solutions for some statistical and combinatorial optimization problems.