A conversation with Dr. Edward Y. Chang

1. Please share with us your view on the history and important milestones of the Chinese KDD research and application areas. Ample evidence shows that KDD has become a major topic of interest in both research and industry in China since 2006. In academia, professor Zhi-Hua Zhou at Nanjing University in 2006 chaired a National Machine Learning workshop, inviting researchers in the greater China area to share their experience. In 2009, the first Asian Conference on Machine learning was inaugurated in Nanjing. In industry, both Google and MSRA influenced China Internet leading companies such as Tencent, Baidu, Alibaba, and subsequently Renren and Shanda, to start their large-scale KDD operations. Three KDD engineers on my team were recruited to join Baidu knowledge, the primary KDD application of these Internet companies this far is monetization, improving their ad/offer relevance and hence revenue. Genome Institute (BGI) have made impressive progress in areas of computer vision, pattern recognition, and bio-genomics. Applications such as face, gesture, voice, handwriting, and license plate recognition have been widely deployed. In the bio-genomics area, a team at BGI reached a significant milestone in 2008 by sequencing the first Asian individual's diploid genome and published the result in Nature [1]. This sequencing effort took BGI one year to complete. Subsequently, speeding up genome sequencing has been among BGI's top R&D priorities. (One cannot imagine what one billion genomic sequences and their associated disease profiles can bring to advancing human health.) Researchers led by Ruiqiang Li from BGI and researchers from Google and universities at Canada and Hong Kong have met a couple of times to discuss large-scale data mining issues and solutions in hardware, algorithms, and data transportation. There is no doubt that KDD is thriving in China in several areas and its applications are rapidly growing, thanks to the increase of both data volume and demand for intelligent information analysis and trend prediction. 2. Please describe your expertise and contribution to KDD. In 2005, my team started working on developing parallel machine learning algorithms to mine large-scale datasets. My team were made publicly available through Apache foundation, and they have been downloaded more than 4,000 times. Several Google products also use these parallel algorithms. Prior to the large-scale machine learning work, my work with Simon Tong on using active learning to refine user query concepts published in 2001 [8] has been cited 850 times. Together with my works on …