Shih-Fu Chang

Shih-Fu Chang is a Taiwanese American computer scientist and electrical engineer noted for his research on multimedia information retrieval, computer vision, machine learning, and signal processing. He is currently the senior executive vice dean of the School of Engineering and Applied Science of Columbia University, where he is also the Richard Dicker Professor. He served as the chair of the Special Interest Group of Multimedia (SIGMM) of Association of Computing Machinery (ACM) from 2013 to 2017. He was ranked as the Most Influential Scholar in the field of Multimedia by Aminer in 2016.[1] He was elected as an ACM Fellow in 2017.[2]

Shih-Fu Chang
张世富
Born
Yunlin, Taiwan
Education
Awards
  • Academician, Academia Sinica 2018
  • Fellow, ACM (Association for Computing Machinery), 2017
  • Honorary Doctorate, University of Amsterdam (2016)
  • Technical Achievement Award, IEEE Signal Processing Society (2012)
  • Technical Achievement Award, ACM Special Interest Group of Multimedia (2011)
  • Fellow, AAAS (American Association for the Advancement of Science) (2010)
  • Fellow, IEEE (2004)
  • IEEE Kiyo Tomiyasu Award (2009)
Scientific career
FieldsComputer science, electrical engineering
InstitutionsColumbia University
Websitewww.ee.columbia.edu/~sfchang

Biography

Chang received his bachelor's degree in Electrical Engineering from National Taiwan University in 1985 and Ph.D. in Electrical Engineering and Computer Science from University of California at Berkeley in 1993. After his Ph.D. studies, he joined Columbia University as an Assistant Professor. He served as the Chair of Electrical Engineering from 2007 to 2010 and received joint appointment in Computer Science in 2011. He served as a co-PI and later as Co-Director of Columbia University’s ADVENT Industry Consortium, which includes more than 25 industry sponsors in the area of media technologies, from 1993 to 2003. He became the Senior Vice Dean (2012-2015) and later Senior Executive Vice Dean (2015-now) of Columbia's Engineering School, assuming a major role in the School’s efforts in Strategic Planning, Special Research Initiatives, Faculty Development, and International Collaboration. Chang is noted for his influential work in multimedia information retrieval, with broad applications in large-scale image/video search, mobile visual search, image authentication, and information retrieval with semi-supervised learning. His research has resulted in more than 10 technology licenses to companies and the creation of three startup companies. As of August 22, 2017, his publications have been cited more than 41,000 times with an h-index of 100.[3]

Awards

Chang’s notable awards include:

  • Academician, Academia Sinica 2018
  • Fellow, ACM (Association for Computing Machinery), 2017[2]
  • Most Influential Scholar in the Field of Multimedia, 2016[1]
  • Honorary Doctorate, University of Amsterdam, 384th Anniversary, 2016[4]
  • Great Teacher Award, The Society of Columbia Graduates, 2013
  • Technical Achievement Award, IEEE Signal Processing Society, 2012
  • Technical Achievement Award, ACM Special Interest Group in Multimedia, 2011
  • Fellow, AAAS (American Association for the Advancement of Science), 2010
  • IEEE Kiyo Tomiyasu Award, 2009
  • Fellow, IEEE, 2004

Research

Chang’s research includes multimedia information retrieval, computer vision, machine learning, and signal processing. The primary focus of his work is on development of intelligent methods and systems for extracting information from visual content and multimedia that are prevalent in large archives and live sources. In the early 1990s, his group developed some of the earliest and best-known content-based image search systems, VisualSEEk and VideoQ,[5][6] which set the foundation of this vibrant area. During last two decades, he has made significant contributions to the field of multimedia retrieval by developing large multimedia ontologies, large libraries of visual concept classifiers, and automatic methods for multimedia ontology construction.[7][8][9][10] These have strongly influenced design of the video search systems used in practice today.[11] He has developed several well-known compact hashing techniques[12] for efficient search over billion-scale image databases. His compact hashing work has enabled order of magnitude speedup and storage reduction in high-profile applications such as an online human trafficking crime fighting system (joint work with Svebor Karaman) that has been deployed in 200+ law enforcement agencies.[13][14] In addition, he has developed a series of fundamental methods of graph-based semi-supervised learning[15][16] that successfully address the challenge of training large-scale multimedia retrieval systems with noisy and sparse labels. These methods have been adopted in building the first commercialized brain machine interface system[17] for rapid image retrieval. The graph-based search process, based on the random walk with restart theory, developed jointly with X. Wu and Z. Li, has also been deployed in the large app recommendation system of Huawei (connecting 1/2 billion apps to 300 million users).[16]

References
  1. "2016 Most Influential Scholars in Multimedia".
  2. ACM Recognizes 2017 Fellows for Making Transformative Contributions and Advancing Technology in the Digital Age, Association for Computing Machinery, December 11, 2017, retrieved 2017-11-13
  3. "Shih-Fu Chang's Google Scholar Profile".
  4. "UvA awards honorary doctorates to computer scientist Chang and epigeneticist Feinberg".
  5. Smith, John R., and Shih-Fu Chang. "VisualSEEk: a fully automated content-based image query system." In ACM international conference on Multimedia, pp. 87-98. ACM, 1997.
  6. Chang, Shih-Fu, William Chen, Horace J. Meng, Hari Sundaram, and Di Zhong. "A fully automated content-based video search engine supporting spatiotemporal queries." Circuits and Systems for Video Technology, IEEE Transactions on 8, no. 5 (1998): 602-615.
  7. Naphade, Milind, John R. Smith, Jelena Tesic, Shih-Fu Chang, Winston Hsu, Lyndon Kennedy, Alexander Hauptmann, and Jon Curtis. "Large-scale concept ontology for multimedia." IEEE multimedia 13, no. 3 (2006): 86-91.
  8. Borth, Damian, Rongrong Ji, Tao Chen, Thomas Breuel, and Shih-Fu Chang. "Large-scale visual sentiment ontology and detectors using adjective noun pairs." In Proceedings of ACM international conference on Multimedia, pp. 223-232. ACM, 2013.
  9. Ye, Guangnan, Yitong Li, Hongliang Xu, Dong Liu, and Shih-Fu Chang. "Eventnet: A large scale structured concept library for complex event detection in video." In Proceedings of ACM international conference on Multimedia, pp. 471-480. ACM, 2015.
  10. Li, Hongzhi, Joseph G. Ellis, Heng Ji, and Shih-Fu Chang. "Event specific multimodal pattern mining for knowledge base construction." In Proceedings of ACM on Multimedia Conference, pp. 821-830. ACM, 2016.
  11. Amir, Arnon, Marco Berg, Shih-Fu Chang, Winston Hsu, Giridharan Iyengar, Ching-Yung Lin, Milind Naphade et al. "IBM research TRECVID-2003 video retrieval system." NIST TRECVID-2003 (2003).
  12. Liu, Wei, Jun Wang, Rongrong Ji, Yu-Gang Jiang, and Shih-Fu Chang. "Supervised hashing with kernels." In Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, pp. 2074-2081, 2012.
  13. Pedro Szekely, et al., “Building and Using a Knowledge Graph to Combat Human Trafficking,” In International Conference on Semantic Web (ICSW), Oct. 2015.
  14. Columbia University Content-Based Image Search System for DARPA MEMEX Project. http://www.ee.columbia.edu/dvmm/memex/
  15. Wang, Jun, Tony Jebara, and Shih-Fu Chang. "Semi-supervised learning using greedy max-cut." Journal of Machine Learning Research 14, no. Mar (2013): 771-800.
  16. Wu, Xiao-Ming, Zhenguo Li, Anthony M. So, John Wright, and Shih-Fu Chang. "Learning with partially absorbing random walks." In Advances in Neural Information Processing Systems (NIPS), pp. 3077-3085. 2012.
  17. Wang, Jun, Eric Pohlmeyer, Barbara Hanna, Yu-Gang Jiang, Paul Sajda, and Shih-Fu Chang. "Brain state decoding for rapid image retrieval." In Proceedings of the 17th ACM international conference on Multimedia, pp. 945-954. ACM, 2009.
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