Title : Computational Anatomy Models for Medical Image Understanding
Speaker : Prof. Yen-Wei Chen

Summary

In recent years, medical imaging, including computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound imaging, has made remarkable progress. For example, multi-detector row CT scanners (MDCT) can acquire whole body CT images in about 20 seconds with approximately 0.5 mm resolution, while open configuration magnetic resonance scanners (Open-MR) can be used as an image navigation system for minimally invasive treatments. Such medical imaging systems have opened up completely new research fields of computer aided diagnosis (CAD) and computer aided surgery (CAS). Automatic medical image understanding is one of most fundamental techniques for CAD and CAS. In this keynote speech, I will describe a statistical learning framework for medical image understanding with computational anatomy models. The computation anatomy model, which can capture the organ variability of its position, shape and voxel intensity (texture) from a training set, is constructed and used as a prior knowledge for automatic medical image understanding.

Biography

Yen-Wei Chen received the B.E. degree in 1985 from Kobe Univ., Kobe, Japan, the M.E. degree in 1987, and the D.E. degree in 1990, both from Osaka Univ., Osaka, Japan. He was a research fellow with the Institute of Laser Technology, Osaka, from 1991 to 1994,. From Oct. 1994 to Mar. 2004, he was an associate Professor and a professor with the Department of Electrical and Electronic Engineering, Univ. of the Ryukyus, Okinawa, Japan. He is currently a professor and vice dean with the college of Information Science and Engineering, Ritsumeikan University, Kyoto, Japan. He was a visiting professor with the Oxford University, Oxford, UK in 2003 and a visiting professor with Pennsylvania State University, USA in 2010.
His research interests include medical image analysis, pattern recognition and computational intelligence. He has published more than 300 research papers in a number of leading journals and leading conferences including IEEE Trans. Image Processing, IEEE Trans. SMC, Pattern Recognition, Signal Processing, Neurocomputing and MICCAI, ICPR, ICIP. He has received many distinguished awards including Watanabe Mitsuhiko Award, Outstanding Chinese Oversea Scholar Fund of Chinese Academy of Science.
Dr. Chen is an Editor-in-Chief of Advances in Multimedia Technology, associate Editors of International Journal of Image and Graphics (IJIG), International Journal of Image Processing (IJIP), and Journal of Convergence Information Technology (JCIT), and a member of Editorial Board for a number of academic journals. He also served as a guest editor of an issue/volume of 4 international journals including Information, International Journal of Computation Intelligence and Applications. He was invited to serve as a general chair or a program chair of several international conference including IIHMSP2009, SEDM2010 and ICCIT2011.

Title : Data Deluge in Business: Is it a Technical or Management Issue?
Speaker : Dr. Ping-Yu Hsu

Abstract and Title of the Speech

Trying to keep floating in the age of data deluge, organizations and IT professionals have tried to come up with ever more complex methodology or models to make sense of what has happened and predict what will happen. Operating these methodologies mainly rely on IT professionals. However, as the tide rises faster than the increase of capable IT professionals, the risk of organizations being inundated by data is getting even higher. The speech therefore argues that to solve the issue, approaches to motivate and help industry people to get involved is needed.

The phenomena should be parallel to what has happened to Web2.0, in which all passive readers are encouraged to actively participate in the development of Internet content. Passive BI information consumers should be motivated, empowered, and properly equipped to participate in report creation and model generation.

In this speech, a model to show how industry people may be motivated to create reports is discussed. The author also shares a viewpoint that a meta data navigation system may be needed to help users find the right data to perform mining.

Biography

Dr. Ping-Yu Hsu graduated from the CSIE department of National Taiwan University in 1987, got his master degree from the Computer Science Department of New York University in 1991, and Ph.D. degree from the Computer Science Department of UCLA in 1995. He is a professor in the Business Administration department of National Central University in Taiwan and the secretary-in-chief of the Chinese ERP association.

In 2000, he cofounded the ERP Center in National Central University. The center is the first Cloud ERP education center Hosting SAP ERP training servers for 26 universities in Taiwan. In 2002, he cofounded the Chinese ERP Society which issuing more than 15, 000 certificates per year. He coauthored the best selling ERP text book in Taiwan and is also one of the authors of a popular BI text book.

His research interest focuses in the business data related applications, including Data mining, Business Intelligence, Data Warehousing, and Enterprise System implementation. He has published more than 100 Journal and conference articles. His papers have been published in IEEE Transactions, Information Systems, Information Sciences, and various other journals.

Title : "Some Overlooked Issues on Contemporary Information Technology Research"

Editorial Affiliations
- Journal of Intelligent & Fuzzy Systems, Member of Advisory Board, John Wiley Publishers
- Policy Analysis and Information Systems, Area Editor, Plenum Publishing Corp., Fuzzy Mathematics, Editorial Board
- Advisory Editor, The Journal of Fuzzy Mathematics, International Fuzzy Mathematics Institute, Los Angeles, California, USA, since 1991.
- Bulletin Pour Les Sous Enserbles Flous et leur Applications Correspondents Etrangers
- Editor-in-Chief, Information Sciences?An International Journal, North Holland, Elsevier Publishing Company
- Three Journals: (i) Computer Theory & Informatics, (ii) Intelligent Systems, (iii) Applications.
- Member, Advisory Editorial Board, Fuzzy Optimization and Decision-Making, Editor-in-Chief, Shu-Cherng Fang, North Carolina State University, Raleigh, NC, USA (Kluwer Academic Publishers) 2000 to Present.

Professional Background
1960-62 Instructor in Electrical Engineering Dept., University of New Brunswick.
1962-65 Graduate and Research Assistant, E.E. Dept., Ohio State University.
1965-68 Member of Technical Staff, Bell Laboratories, Inc., Holmdel, N.J.,
(Communication Systems Research, Operations Research and Modern Control Theory).
1968-70 Assistant Professor of Electrical Engineering, Duke University
1970-75 Associate Professor of Electrical Engineering, Duke University
1974-75 Visiting Professor of Electrical Engineering, University of Massachusetts
1975-present Professor of Electrical Engineering, Duke University

Consulting Activities
1962: (Summer) Gibbs & Hill Consulting Co., New York City, (Electrical Power Plant Design)
1969-72: Consultant, Safeguard ABM, Western Electric Co., (Reliability, OR Models, Quality Control)
1974-75: Operation Researcher, Center for the Study of Aging and Human Development, Duke Medical Center, (Statistical Pattern Recognition)
1975-76: Consultant, Emerson ElectricWorld Headquarters at St. Louis (Advanced F-5E Fighter Plane, Optimal Estimation and Digital Control Systems).
1976: Researcher, Research Department, Grumman Aerospace Corporation, (Highly Reliable Systems).
1977-Present: Member, Board of Directors, Knowledge Systems Institute, Chicago.
1977-78: Consultant to Assistant Secretary of the State of North Carolina on energy Matters.
1977-79: Investigator, behavioral Neuropharmacology Laboratory, Duke Medical Center.
1978-89: Consultant, Western Electrical Co., (Kalman Filter design for Naval Surface Ships Navigation).
1980-90: Consultant, LORD Corporation (Touch Sensors, Robotics)
1986-2000 Advisor and Distinguished Lecture Chair to Computer Science Department, Tamkang University, Taiwan, Republic of China.
1990-91: Summers, ASEE-NASA Fellow, Langley Space Research Center, NASA.
1991-Present: Member, Board of Directors, Intelligent Machines, Inc., Sunnyvale, California.
2000-Present: Member of advisory board, Gravidata, Inc., a start-up information company with 20 employees.
2000-Present: Advisor for EICA, Equipo de Investigacion en Computo Artificial, Universidad Anahuac de Xalapa, Mexico

Grants and Awards
1968-76: Principal Investigator and Associate Investigator on research projects supported by Goddard Space Flight Center, NASA.
1972-74: Faculty Fellow, ASEE-NASA (Goddard Space Flight Center).
1978: Lecture Chair in Mathematical System Theory, Tamkang College, Taiwan, Republic of China.
1981 & 1982 Distinguished National Professorship at Huazhong University of Science and Technology and other Universities, Ministry of Education, People's Republic of China.
1988-Present: Hing Hua University, Honorary Board Member, University Foundation
1993-97: NSF and EPRI Research Grant
1993-96: CANON, Inc., Japan Research Center
1995-99: Holiday Inn World Wide Research Grant.
1997-Present: Director, Cooperative Research program between Duke University and Yuan Ze University.
1998-Present: Southwest Jiao Tung University, Advising Professor.
1998-2001: Visiting Chair Professor of Computer Science & Engineering, Yuan Ze University.
1999-2000: Cross Century Outstanding Scholar Award in Fuzzy Logic by Chinese Oversea Talents Foundation, Republic of China. (pure gold Chinese painting, etc.).
2000-2003 Co-Investigator (Principal Investigator, Erich Jarvis, Ph.D., Duke University Medical Center, Department of Neurobiology), Klingenstein Fund, 2000-EJ. Whitehall Foundation and Packard Foundation.

Professional Affiliations
Founder:
Association for Intelligent Machinery.
Joint Conference on Information Sciences.
Fuzzy Theory and Technology Conference (7th, 2000).
Member:
Institute of Electrical and Electronics Engineers.
Association for Computing Machinery
Classification Society
Pattern Recognition Society
North American Fuzzy Information Processing Society
Honorary member, Hing Hua Technical University Educational Foundation.

Summary

This talk consists of three major components all have to do with information processing research at three different hierarchical levels.
At the top of the level, it has to do with the goal-and-scope of the information processing research which involves the contents and the institutions assume the leadership. In most cases, the government funding agencies, foundations and research institutions including universities. The decisions made on research priorities have never been easy. The impact comes from publishing industry should never be neglected. The culture of ranking certainly has been intensified in recent time. However, the IF, Impact Factor and various Citation Indices, in my opinion, is not the same as diplomas. To say the least, we need more dialog on these.
The second level of discussion deals with the reflection on fundamental research, technology transfer, commercialization and job creation which so happen to be a very hot debating topic at present time. Two examples depends heavily on the successful breakthrough of the information processing research, bioinformatics or DNA computers and economics & financing, will be used for illustration purpose.
Finally, I hope to discuss with audience some often overlooked issues, perhaps they are all popped up naturally due to my personal experiences. You may or may not agree with my personal view, however, there is no lack of sincerity on my part urging me to share with you all.
The cutting edge research on web technology has profound impact on the way we live and conduct our daily business. It has also forced us to rethink the priority of conducting our research and development in industry and academic institution alike. The impact is so great to such an extend that I am inclined to redefine the "Space of Information Processing Research" from traditionally one dimension to a multiple dimensionality. Consequently, the new research culture of computer scientists and engineers, inevitably, has to be adaptively adjusted by sheer necessity and the recognition of reality.

* The opinion expressed in this talk reflects entirely my personal view, hence it does not reflect on Duke University, or any institutions of which I have been affiliated with.

Title : "Transparent Linguistic Interface in Fuzzy Systems Modeling"

Dr. Chen received his Ph.D. degree from Purdue University in 1988 and M.S. degree from University of Michigan in 1985. Currently he is Dean and Chair Professor of the Faculty of Science and Technology, University of Macau. He has been a Professor and the Chair of the Department of Electrical and Computer Engineering, Associate Dean for Research and Graduate Studies of the College of Engineering, University of Texas at San Antonio, Texas.

His current research interests include theoretic development in computational intelligence, intelligent systems, cyber-physical systems, robotics and manufacturing automation, networking, diagnosis and prognosis, and life prediction and life-extending control. He is an elected Fellow of IEEE and AAAS.

He is the President-Elect and Vice President on Conferences and Meetings of the IEEE Systems, Man and Cybernetics Society (SMCS), where he has been the Vice President of the Technical Activities on Systems Science and Engineering and co-founder of three Technical Committees and two local chapters. He received SMCS Outstanding Contribution Award in 2008 and 2010. Dr. Chen is a member of Tau Beta Pi and Eta Kappa Nu honor societies and has been the faculty advisor for Tau Beta Pi Engineering honor society. In addition, he is an ABET (Accreditation Board of Engineering and Technology Education) Program Evaluator for Computer Engineering, Electrical Engineering, and Software Engineering programs.

"Prof. Philip Chen appointed honorary professor by Obuda University in Hungary"

Summary

System modeling is building a model of the studied system for the purpose of analysis, control and optimization. Except consulting the experts of the system to build the system model, more viable approach is learning the model from historical data collected from the system. Classification, forecasting, and clustering are three typical learning tasks in system modeling. Because they are widely used in many fields, these learning tasks are intensively studied by researchers from different research communities in machine learning, pattern recognition, and computational intelligence.

Considering the shortcomings of previous studies on transparency, performance, and usability, the transparency of fuzzy classification systems is first studied. A new transparent linguistic interface generation method for fuzzy systems will be introduced. With the new linguistic interface, the fuzzy decision trees demonstrate both interpretability and improved classification performance. Extending this approach, a new fuzzy clustering algorithm named multiple kernel fuzzy c-means will be discussed. This multiple kernel fuzzy c-means offers us a great vehicle to fusion information from multiple heterogeneous or homogeneous sources. Applications in image segmentation problems demonstrate multiple kernel fuzzy c-means¡¯s great advantages and potentials.