Welcome

The DSAIG seminar series aim to bring together faculty members and students across different disciplines within the University to discuss research topics that are related to Data Science and its applications. It serves as a forum to foster interdisciplinary research and to uncover new possibilities for research collaboration in the Data Science field.

Monthly seminars will be organized with focuses on specific research topics related to Data Science and its applications, and given by renowned experts of the field with a broad range of academic backgrounds including Business, Engineering, Science, and Social Science.

For enquiries, please contact us at cb-dsaig@cityu.edu.hk.


Next Seminar

Prof. Jun WANG

Next Seminar

Neurodynamics-based Parallel Data Selection in the Era of Big Data

By Prof. Jun WANG

Department of Computer Science
City University of Hong Kong

Date
1 March 2018 (Thursday)
Time
12:30pm-2:00pm
Venue
Room 14-221, 14/F, Lau Ming Wai Academic Building, City University of Hong Kong
Language
English
Registration
Register Here

(A light sandwich lunch will be provided starting from 12:00noon. Please confirm your attendance.)

 

Abstract
In the present information era, huge amount of data to be processed daily. In contrast of conventional sequential data processing techniques, parallel data processing approaches can expedite the processes and more efficiently deal with big data. In the last few decades, neural computation emerged as a popular area for parallel and distributed data processing. The data processing applications of neural computation included, but not limited to, data sorting, data selection, data mining, data fusion, and data reconciliation. In this talk, neurodynamic approaches to parallel data processing will be introduced, reviewed, and compared. In particular, my talk will compare several mathematical problem formulations of well-known multiple winners-take-all problem and present several recurrent neural networks with reducing model complexity. Finally, the best one with the simplest model complexity and maximum computational efficiency will be highlighted. Analytical and Monte Carlo simulation results will be shown to demonstrate the computing characteristics and performance of the continuous-time and discrete-time models. The applications to parallel sorting, rank-order filtering, and data retrieval will be also discussed.

Biography
Jun Wang is the Chair Professor Computational Intelligence in the Department of Computer Science at City University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and the Chinese University of Hong Kong. He also held various short-term visiting positions at USAF Armstrong Laboratory, RIKEN Brain Science Institute, Dalian University of Technology, Huazhong University of Science and Technology, and Shanghai Jiao Tong University (Changjiang Chair Professor). He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology and his Ph.D. degree in systems engineering from Case Western Reserve University. His current research interests include neural networks and their applications. He published about 200 journal papers, 15 book chapters, 11 edited books, and numerous conference papers in these areas. He is the Editor-in-Chief of the IEEE Transactions on Cybernetics. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009), IEEE Transactions on Cybernetics and its predecessor (2003-2013), and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a member of the editorial board of Neural Networks (2012-2014), editorial advisory board of International Journal of Neural Systems (2006-2013. He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence, and a Program Chair of the IEEE International Conference on Systems, Man, and Cybernetics (2012). He has been an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012, 2014-2016). In addition, he served as President of Asia Pacific Neural Network Assembly (APNNA) in 2006 and many organizations such as IEEE Fellow Committee; IEEE Computational Intelligence Society Awards Committee; IEEE Systems, Man, and Cybernetics Society Board of Governors, He is an IEEE Fellow, IAPR Fellow, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011, Neural Networks Pioneer Award from IEEE Computational Intelligence Society (2014), among others.


Forthcoming

Prof. Jun WANG
Neurodynamics-based Parallel Data Selection in the Era of Big Data
By Prof. Jun WANG (CS)
In the present information era, huge amount of data to be processed daily. In contrast of conventional sequential data processing techniques, parallel data ... [ more ]
 
By Zhenzhen WANG
A Lightening Introduction to Parallel Computing: Using Apache Spark as an Example
By Zhenzhen WANG
Traditionally, many problems are so large and/or complex that it is impossible to solve them on a single computer, especially given limited computer ... [ more ]
 
Dr. Geoffrey TSO
Development of two consumer satisfaction and confidence indexes
By Dr. Geoffrey TSO (MS)
We shall cover the development of two consumer indexes with local impacts in Hong Kong. The Hong Kong Consumer Satisfaction Index (HKCSI) is a leading performance indicator for HK businesses, measuring ... [ more ]
 
Dr. Hailiang CHEN
Vertical Integration, Movie Screening, and Theatrical Performance
By Dr. Hailiang CHEN (IS)
In Mainland China, vertical integration between producers/distributors and exhibitors is quite common in the motion picture industry. For instance, a producer or distributor can have a significant equity interest in an exhibitor, and vice versa ... [ more ]
 
Dr. Lishuai LI
Characterize Operations in Air Transportation Systems Based on Large-scale Operational Data
By Dr. Lishuai LI (SEEM)
Many operations in air transportation systems, such as flying an airplane or air traffic management, are performed by human following specific procedures. Standards of these operations are multifaceted depending on pilot, air traffic controller ... [ more ]
 
Dr. LI Xi
One picture is worth 253 characters – Using photo mining to understand the role of a camera in online word of mouth
By Dr. LI Xi (MKT)
The last few years have witnessed an explosive growth in the number of photos attached to reviews in online communities. In this paper, using two datasets from TripAdvisor and Yelp, we empirically ... [ more ]
 
 

Previous

Dr. Junhui WANG
A Smooth Collaborative Recommender System
By Dr. Junhui WANG (MA)
In recent years, there has been a growing demand to develop efficient recommender systems which track users' preferences and recommend potential items of interest to users. In this talk, I will present a smooth ... [ more ]
 
Dr. LI Xin
Collaboration Process Pattern & Teamwork Performance: A Graph Mining-Based Methodology
By Dr. LI Xin (IS)
It is well documented in management literature that characteristics of collaboration processes strongly influence team ... [ more ]
 
Prof. Frank CHEN & Dr. Eman LEUNG
A Data-driven Approach to the Integrated Elderly Care
By Prof. Frank CHEN & Dr. Eman LEUNG (MS)
The global trend today is to shift the care burden away from the acute care and long-term care, which are costly and afford a poor quality of life, to community care and primary care, which are more conducive ... [ more ]
 
Dr. Huazhong ZHAO
Status Leaders, Hubs and their Social Influence
By Dr. Huazhong ZHAO (MKT)
The prevalence of internet technology and social media has provided marketers new opportunities to identify influencers and utilize their social influence to affect consumer decisions. We introduce status leaders ... [ more ]
 
Dr. Kevin SUN
Mining Triage Notes to Predict Emergency Department Admissions
By Dr. Kevin SUN (MS)
Emergency department (ED) overcrowding is a worldwide problem that undermines hospitals’ ability to provide timely care to patients who need it urgently. One of the ... [ more ]
 
Dr. Qingpeng ZHANG
Prediction of chronic disease occurrence using EHR data: A pilot study in Hong Kong
By Dr. Qingpeng ZHANG (SEEM)
Chronic diseases are major causes of morbidity and mortality worldwide. Many chronic diseases are preventable through effective disease management and prevention programs. In addition, some ... [ more ]
 
Prof. Jonathan ZHU
Mining Time Information in Digital Data
By Prof. Jonathan ZHU (COM)
Time is the single common denominator across all types of digital data from the internet, mobile networks, and IoT (internet of things). However, the rich information embedded in timestamps of digital data has often been taken for granted. As such, this gold ... [ more ]
 
Prof. Jeff HONG
Understanding Demand Uncertainty
By Prof. Jeff HONG (EF/MS)
Poisson processes are often used to model arrival (demand) processes in many business and engineering fields. In this talk, I will show you the results from a sequence of studies that we have done trying to understand the uncertainty behaviors in demand processes. The examples include phone calls ... [ more ]
 
Prof. Leon ZHAO
Blockchain as Trust Platform for Cross-boundary Dataflow
By Prof. Leon ZHAO (IS)
In the era of big data, more and more companies have become digitized. Further, under globalization initiatives such as the One-Belt One Road effort, business processes are being automated nowadays cross companies, leading to cross-boundary data flow. However, conventional information ... [ more ]
 
 

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