Prof. Cees de Bont, PhD
Dean of School & Swire Chair Professor of Design
Alex Wong Siu Wah Gigi Wong Fook Chi
Professor in Product Design Engineering
School of Design
The Hong Kong Polytechnic University
Cees de Bont is the Dean of the School of Design at The Hong Kong Polytechnic University (PolyU), Swire Chair Professor of Design and Alex Wong Siu Wah Gigi Wong Fook Chi Professor in Product Design Engineering. Prior to his appointment at PolyU, he was the Dean of the Faculty of Industrial Design Engineering at the Delft University of Technology in the Netherlands. In the PolyU School of Design, he introduced the master program: International Design and Business Management and the executive master program: Innovation Leadership. His research interests include: design education, consumer behavior, innovation adoption, design methods and networked innovation. He published papers in the ‘Journal of Product Innovation Management, Design Studies’, ‘Journal of Retailing’, ‘Journal of Economic Psychology’, ‘International Journal of Cultural and Creative Industries’ and ‘Journal of Design, Business & Society’. Professor de Bont founded the Creative Industry Scientific Program on product-service systems (CRISP) and chaired the Dutch Innovation Centre for Electric Road Transport (D-incert). He is the Chairman of the Management Committee of the Design Institute for Social Innovation (DISI) at PolyU and a member of the Board of Directors of the Hong Kong Design Center and of PMQ, a creative design hub for design talents. Contact: firstname.lastname@example.org
Speech Title: Design as a driver for innovation
Abstract: Designers have contributed to product innovations already for many years. Apple is often mentioned as one of the companies where design made a big difference. In the School of Design of the Hong Kong Polytechnic University we have been doing research on ways to stimulate innovation, but also on ways to get the innovations better accepted by consumers. In view of the many new technologies that are now available to us, product designers can choose from a wide spectrum of attributes to innovate. We interviewed many leaders from the different fields of innovation to cluster these attributes, thereby opening up new ways to explore innovation possibilities. In order to get the innovations accepted, designers can influence the touch and feel of the products. We have investigated what are the possibilities for product designers to optimally radiate the level of newness through the aesthetic qualities of innovative products.
Steven Guan received his M.Sc. & Ph.D. from the University of North Carolina at Chapel Hill. He is currently a professor in the computer science and software engineering department at Xi'an Jiaotong-Liverpool University (XJTLU). He served the head of department position at XJTLU for 4.5 years, creating the department from scratch and now in shape. Before joining XJTLU, he was a tenured professor and chair in intelligent systems at Brunel University, UK.
Prof. Guan has worked in a prestigious R&D organization for several years, serving as a design engineer, project leader, and department manager. Later he joined the Electrical & Computer Engineering Department at National University of Singapore as an associate professor.
Prof. Guan's research interests include: machine learning, modeling, security, networking, and pseudorandom number generation. He has published extensively in these areas, with 130 journal papers and 170+ book chapters or conference papers. He has chaired and delivered keynote speech for 40+ international conferences and served in 130+ international conference committees and 20+ editorial boards.
Speech Title: Input Space Partitioning for Machine Learning
Abstract: This talk introduces an input attribute grouping method to improve the performance of learning. During training for a specific problem, the input attributes are partitioned into groups according to the degree of inter-attribute promotion or correlation that quantifies the supportive or negative interactions between attributes. After partitioning, multiple sub-networks are trained by taking each group of attributes as their respective inputs. The final classification result is obtained by integrating the results from each sub-network. Experimental results on several UCI datasets demonstrate the effectiveness of the proposed method.