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Biography: |
Bin Zhang is a Principal Scientist and Group Leader of Sage Bionetworks, a non-profit research organization started in 2009 that grew out of a decade of intense well-funded work at Rosetta Inpharmatics, a wholly owned subsidiary of Merck & Co. Before he joined Sage, he worked at Merck & Co. first as a senior research scientist from and then as a Research Fellow. Prior to joining Merck & Co., he was a post-doctoral fellow and then a Research Faculty and Senior Biostatistician at David Geffen Medical School of University of California at Los Angeles. He holds a Ph.D. and a master degree in Computer Science from the State University of New York at Buffalo, a master degree in electronic engineering from Tsinghua University, Beijing, China, and a bachelor's degree in electrical engineering from Tongji University, Shanghai, China.
His expertise lies in bioinformatics and computational biology, image processing, pattern recognition and data mining. He has developed or significantly contributed to several influential gene network inference algorithms which have been extensively used to identify pathways and gene targets involved in a variety of diseases such as cancer, atherosclerosis, Alzheimer's, obesity and diabetes etc. One of such applications was selected as the second most influential brain tumor publication in the year 2006 by the brain tumor research portal at www.braintumour.net. His recent research along this direction has been highlighted by Nature(http://www.nature.com/news/2008/080314/full/news.2008.669.html). His early research on image pattern recognition significantly contributed to several large-scale pattern recognition systems of national interest including U.S. Handwritten Address Identification System, United Kingdom Handwritten Address Identification System and Handwritten Document Comparison System. He has published 30 journal papers, including a number of high profile papers in Nature, Nature Genetics and PNAS, 1 book chapter, 24 peer reviewed conference papers and 16 abstracts. He is the recipient of the Best Paper Award of ICDAR 2003 ─ the Seventh International Conference on Document Analysis and Recognition. |