Prof. Chang Wins ACM SIGMM Technical Achievement Award

 

Professor Shih-Fu Chang, who holds a joint appointment in electrical engineering and computer science, has won the prestigious 2011 ACM SIGMM Technical Achievement Award for Outstanding Technical Contributions to Multimedia Computing, Communications and Applications.
 
Given by the Association for Computing Machinery (ACM), the professional society of computer scientists, in recognition of outstanding contributions over a researcher’s career, the SIGMM Award cites Chang’s “pioneering research and inspiring contributions in multimedia analysis and retrieval.” It will be presented to Chang at the ACM International Conference on Multimedia, where he will give a keynote speech on November 28 in Scottsdale, Arizona.
 
“I am greatly honored with this very prestigious recognition,” said Chang. “It really should be attributed to all the wonderful students and collaborators I have had the good fortune to work with during my career.”
 
He added, “We are very excited about several of our research initiatives that continue to push the frontiers of multimedia retrieval research, broaden applications to new domains such as mobile media and brain machine interfaces, and make possible powerful next-generation search engines that will enable users to cope with the explosive growth of image and video data.”
 
The SIGMM Award recognized Chang’s significant contributions that have shaped directions in many key areas of multimedia, including multimedia search, video summarization, compressed-domain manipulation, and trustworthy media. His work has been highly influential, with a broad impact across research, education, and practical applications.
 
In the 1990s, Chang and his students developed several of the first image/video search engines, such as VisualSEEk, VideoQ, and WebSEEk. He has been recognized with a wide array of technical awards and best paper awards for inventing novel systems that combine content analysis, adaptive mobile communication, and multimedia summarization.
 
Other significant developments by Chang include large-scale concept-based video search engines such as CuZero, a widely-used library of image classification models like Columbia374, international multimedia indexing and communication standards including MPEG-7 and MPEG-21, and large multimedia ontologies such as LSCOM. His group demonstrated the best multimedia indexing performance in international benchmarking forums such as TRECVID (2008 and 2010). Chang also co-led the ADVENT university-industry research consortium with the participation of more than 25 industry sponsors. Many video indexing technologies developed by his group have been licensed to companies.

 

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