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Yun Raymond Fu, Ph.D. - Global Resilience Institute. Boston, MA, US

Yun Raymond Fu, Ph.D. Yun Raymond Fu, Ph.D.

Associate Professor, Electrical and Computer Engineering Interdisciplinary with, Computer and Information Science, Northeastern University | Faculty Affiliate, Global Resilience Institute

Boston, MA, UNITED STATES

Professor Fu focuses on cyber-physical systems, machine learning and computational intelligence, security and systems/tools.

Biography

Yun Raymond Fu received the BEng degree in information engineering and the MEng degree in pattern recognition and intelligence systems from Xian Jiaotong University, China, respectively, and the MS degree in statistics and the PhD degree in electrical and computer engineering from the University of Illinois at Urbana- Champaign, respectively. He is an interdisciplinary faculty member affiliated with the College of Engineering and the College of Computer and Information Science with Northeastern University since 2012. His research interests are machine learning, computational intelligence, big data mining, computer vision, pattern recognition, and cyber-physical systems. He has extensive publications in leading journals, books/book chapters and international conferences/workshops. He serves as associate editor, chairs, PC member and reviewer of many top journals and international conferences/workshops. He received seven Prestigious Young Investigator Awards from NAE, ONR, ARO, IEEE, INNS, UIUC, Grainger Foundation; seven Best Paper Awards from IEEE, IAPR, SPIE, SIAM; three major Industrial Research Awards from Google, Samsung, and Adobe, etc. He is currently an associate editor of the IEEE Transactions on Neural Networks and Leaning Systems (TNNLS). He is fellow of SPIE and IAPR, a lifetime senior member of ACM, lifetime member of AAAI, OSA, and Institute of Mathematical Statistics, member of Global Young Academy (GYA), AAAS, INNS and Beckman Graduate Fellow during 2007-2008. He is a senior member of the IEEE.

Areas of Expertise (4)

Cyber-Physical Systems Machine Learning and Computational Intelligence Security and Systems/Tools and Measurement Pattern Recognition

Accomplishments (5)

Faculty Fellow

2017

Young Investigator Award

Office of Naval Research

Young Investigator Award

International Neural Network Society

Young Investigator Award

Army Research Office

Outstanding Research Award

Søren Buus

Education (4)

University of Illinois: Ph.D., Electrical and Computer Engineering

University of Illinois: M.S., Statistics

Xi'an Jiaotong University: M.Eng, Pattern Recognition and Intellignece Systems

Xi'an Jiaotong University: B.Eng, Information Engineering

Articles (5)

Semi-supervised Deep Domain Adaptation via Coupled Neural Networks IEEE Transactions on Image Processing

Zhengming Ding, Nasser M Nasrabadi, Yun Fu

2018

Domain adaptation is a promising technique when addressing limited or no labeled target data by borrowing welllabeled knowledge from the auxiliary source data. Recently, researchers have exploited multi-layer structures for discriminative feature learning to
reduce the domain discrepancy. However, there are limited research efforts on simultaneously building a deep structure and a discriminative classifier over both labeled source and unlabeled target...

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Structure-Preserved Unsupervised Domain Adaptation IEEE Transactions on Knowledge and Data Engineering

Hongfu Liu, Ming Shao, Zhengming Ding, Yun Fu

2018

Most existing work of domain adaptation aims to learn a classifier on a source domain and then predict the labels for target data, which indicates that only the knowledge derived from the hyperplane is transferred to the target domain and the structure information is ignored.

To that end, we develop a novel unsupervised domain adaptation framework, which ensures the whole structure of source domains will be preserved and transferred to serve the task on the target domain. For that purpose, both source and target data are put together for clustering and the well-preserved structure information from the source domains will facilitate and guide the adaptation process in the target domain...

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Residual dense network for image super-resolution The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, Yun Fu

2018

A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep CNN based SR models do not make full use of the hierarchical features from the original low-resolution (LR) images, thereby achieving relatively-low performance. In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR.

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Age synthesis and estimation via faces: A survey IEEE Transactions on Pattern Analysis and Machine Intelligence

Yun Fu, Guodong Guo, Thomas S Huang

2010

Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance. Derived from rapid advances in computer graphics and machine vision, computer-based age synthesis and estimation via faces have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as forensic art, electronic customer relationship management, security control and surveillance monitoring, biometrics, entertainment, and cosmetology. Age synthesis is defined to rerender a face image aesthetically with natural aging and rejuvenating effects on the individual face. Age estimation is defined to label a face image automatically with the exact age (year) or the age group (year range) of the individual face. Because of their particularity and complexity, both problems are attractive yet challenging to computer-based application system designers. Large efforts from both academia and industry have been devoted in the last a few decades. In this paper, we survey the complete state-of-the-art techniques in the face image-based age synthesis and estimation topics. Existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are also provided with systematic discussions.

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Image-based human age estimation by manifold learning and locally adjusted robust regression IEEE Transactions on Image Processing

Guodong Guo, Yun Fu, Charles R Dyer, Thomas S Huang

2008

Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. The aging process is determined by not only the person's gene, but also many external factors, such as health, living style, living location, and weather conditions. Males and females may also age differently. The current age estimation performance is still not good enough for practical use and more effort has to be put into this research direction. In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages. The novel approach improves the age estimation accuracy significantly over all previous methods. The merit of the proposed approaches for image-based age estimation is shown by extensive experiments on a large internal age database and the public available FG-NET database.

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