Invited Speakers2019



Assoc. Prof. Gurdal Ertek,
United Arab Emirates University, UAE

Dr. Gurdal Ertek is an Associate Professor of Business Administration at United Arab Emirates University (UAEU), Al Ain, UAE. His research areas include artificial intelligence, data science, supply chain management, and project management. His work experience and international collaborations include industry, academic, and government organizations in UAE, Kuwait, USA, UK, Netherlands, Bosnia, Turkey, China, Singapore, and Australia. He has completed projects with a variety of industries, including information technology, energy, manufacturing, retail, and automotive industries. Dr. Ertek has 25+ years experience in data science, artificial intelligence, business analytics, and IT fields, and has served as a reviewer for more than 50 R&D and IT projects submitted to TÜBİTAK (Turkish National Science Foundation) and other R&D agencies.

Speech Title--“25 Years of Data Science Projects: Lessons Learned

Abstract--Big data, artificial intelligence, data mining, business analytics. These are the terms and concepts we hear almost every day. But how are these concepts related to each other? How are they alike and how do they differ? In this presentation, the answers to these questions will be explored, through examples of projects conducted in different industries. The commonality in data science projects is that the ultimate objective is analyzing data for obtaining actionable insights. Lessons learned from 25 years of data science projects and practical tips for data science project management will also be shared.


Assoc. Prof. Manhua Liu,
Shanghai Jiao Tong University, China


Dr. Manhua Liu is currently an Associate Professor with the Artificial Intelligence institute, Shanghai Jiao Tong University. She received the Ph.D. degree from Nanyang Technological University, Singapore, in 2008. Her research interests include multi-modality brain image computing and analysis, biometrics, and machine learning and their applications to normal early brain development and disorders. She has published more than 60 SCI/EI papers in journals and proceedings of international conferences. As the PI, Dr. Liu has also successfully collaborated on 3 NSFC projects and National Key National Key Research and Development Program sponsored projects.

Speech Title--Brain image computing and analysis based on deep learning

Abstract--Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to brain diseases. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD). Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish disease subjects from other groups. This talk will present our research works on application of deep learning in brain image computing and analysis including the hippocampus segmentation and classification using structural MRIs and multimodal image classification for brain disease diagnosis.