Bo Zhang is now a professor of Computer Science and Technology Department of Tsinghua University, the fellow of Chinese Academy of Sciences. In 1958 he graduated from Automatic Control Department of Tsinghua University, and became a faculty member since then. From 1980 to 1982 he visited UIUC, USA as a scholar. He is now the technical advisor of Fujian government, and the member of Technical Advisory Board of Microsoft Research Asia. He is engaged in the research on artificial intelligence, artificial neural networks, genetic algorithms, intelligent robotics, pattern recognition and intelligent control. In these fields, he has published over 150 papers and 4 monographs, where 2 are English versions. Recently, he founded the center for neural and cognitive computation and the research group for multimedia information processing. The group has got some important results in image and video analysis and retrieval.
Ting Chen is currently a professor of biological sciences, computer science, and mathematics at USC. Before joining USC in 2000, he had been a lecturer of genetics at Harvard University, working as a research fellow in George M. Church’s Laboratory. He obtained his Ph.D. degree in computer science from SUNY Stony Brook in 1997, and B.E. degree in computer science and technology from Tsinghua University in 1993. He received Sloan Research Fellowship in 2004.
Xiaolin Hu received his B.E. and M.E. degree in vehicle engineering from Wuhan University of Technology in 2001 and 2004, respectively, and Ph.D. degree in Computer Aided Engineering from the Chinese University of Hong Kong in 2007. He jointed Computer Science and Technology Department at Tsinghua University in 2007 as a postdoctoral fellow, and he is currently an assistant professor of Tsinghua University.
Modern high-throughput DNA sequencing techniques are capable of sequencing individual or mixtures of genomes and transcriptomes in natural systems ef?ciently and at a low cost. The speed and the low cost of these new sequencing technologies have resulted in the generation of massive biological data sets, providing enormous opportunities for environmental, ecological or general health-research oriented metagenomic analysis, through direct-sequencing of micro-organisms in biological samples followed by computational analysis, classification and annotation of the sequencing data. In the first part of this talk, we will discuss the computational challenges in the data analysis as well as the need for energy-efficient computation. Our nervous system has provided a fantastic solution to energy-efficient computation for many types of data analyses. During decades, numerous brain-inspired computational models including neural networks have been developed for solving engineering problems, but most of them were designed in the spirit of simulating brain functions on conventional computers, while the low cost computational techniques utilized by the brain was in general not considered. The latter part of the talk will focus on opportunities and challenges in developing energy-efficient neural computation models.