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Yun Fu

Prof. Yun Raymond Fu

Associate Professor
Northeastern University

Department of ECE, College of Engineering
403 Dana Research Center
360 Huntington Avenue
Boston MA 02115
United States

tel: (617) 373-7328
fax: (617) 373-8970
E-mail: yunfu@ece.neu.edu
Web: http://www.ece.neu.edu/~yunfu/

Area of Expertise

Image and Video Processing, Optical Pattern Recognition, Electronic Imaging and Sensing for Security Systems


Dr. Yun Fu is an interdisciplinary faculty member (Associate Professor with Tenure) affiliated with College of Engineering and the College of Computer and Information Science at Northeastern University. He received the B.Eng. degree in information engineering and the M.Eng. degree in pattern recognition and intelligence systems from Xi'an Jiaotong University, China, respectively, and the M.S. degree in statistics and the Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign, respectively. Prior to joining the Northeastern faculty, he was a Scientist working at BBN Technologies, Cambridge, MA, during 2008-2010. He holds a Part-Time Lecturer position in the Department of Computer Science, Tufts University, Medford, MA, in 2009. He was a tenure-track Assistant Professor of the Department of Computer Science and Engineering, State University of New York, Buffalo, during 2010-2012.

Dr. Fu's research interests are Interdisciplinary research in Image and Video Processing, Optical Pattern Recognition, Electronic Imaging and Sensing for Security Systems. He has extensive publications (180+) 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. Dr. Fu is the recipient of 5 best paper awards (SIAM SDM 2014, IEEE FG 2013, IEEE ICDM -LSVA 2011, IAPR ICFHR 2010, IEEE ICIP 2007), 4 young investigator awards (2016 IEEE CIS Outstanding Early Career Award, 2014 ONR Young Investigator Award, 2014 ARO Young Investigator Award, 2014 INNS Young Investigator Award), 2015 National Academy of Engineering US Frontiers of Engineering, 2 service awards (2012 IEEE TCSVT Best Associate Editor, 2011 IEEE ICME Best Reviewer), the 2011 IC Postdoctoral Research Fellowship Award, the 2010 Google Faculty Research Award, the 2008 M. E. Van Valkenburg Graduate Research Award, the 2007-2008 Beckman Graduate Fellowship, 2007 Chinese Government Award for Outstanding Self-Financed Students Abroad, the 2003 Hewlett-Packard Silver Medal and Science Scholarship, Edison Cups of the 2002 GE Fund Edison Cup Technology Innovation Competition, and the 2002 Rockwell Automation Master of Science Award. He is currently an Associate Editor of the IEEE Transactions on Neural Networks and Leaning Systems (TNNLS), and IEEE Transactions on Circuits and Systems for Video Technology (TCSVT). His research has been extensively and continuously supported by grants and awards from NSF, NIH, ONR, ARL/ARO, AFOSR, DOD, DHS, NPS, DARPA, IARPA, IC, NGA, Google, Samsung, Adobe, etc. He is a Senior Member of IEEE, Lifetime Member of ACM, AAAI, SPIE, and Institute of Mathematical Statistics, member of INNS and Beckman Graduate Fellow during 2007-2008.

Lecture Title(s)

When Visual Data Meet Uncertainty

The emerging interdisciplinary research of dimensionality reduction from data with uncertainties has increased interest in the area of large scale image and video analysis and human-computer interaction systems under real-world scenarios. As a unique computational element, human-centered imaging, sensing and computing tightly and seamlessly connects human users to the physical or social world that is richly and invisibly interwoven with sensors, actuators, displays, and networks, embedded in the everyday objects, to obtain pleasant interaction experiences, dedicated services or social contexts. Such systems often generate large amount of cross-domain and multi-source optical data which need advance analytics tools. In this talk, Dr. Fu will mainly present his recent and ongoing research/projects of Optical Pattern Recognition and envision future research trend in big data oriented multi-source and multi-modality pattern recognition. Particularly, a general computational methodology for graph embedded dimensionality reduction will be introduced as well as solutions for computability, stability, and complexity under uncertain variations. Extensive real-world applications/demos will also be demonstrated.

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