Advance your academic career, collaborate globally, and expand your network— join now !

Dr. Mingmin Chi

School of Computer Science, Shanghai key laboratory of data science, Fudan Unive...

Share Link

Share

Information

Dr. Mingmin Chi received B.S. and M.S. degrees in electrical engineering from Changchun University of Science and Technology, Changchun, China in 1998 and Xiamen University, Xiamen, China in 2002, respectively, and a Ph.D. degree in computer science from University of Trento, Trento, Italy in 2006. Moreover, she was a student visitor from 2005 to 2006 at the Dept. of Schoelkopf in Max-Planck Institute for Biological Cybernetics, Tuebingen, Germany. Currently, she is an associate professor at School of Computer Science in Fudan University, China. She was a Guest Editor for the Special Iissue on big data in REMOTESENSING for the IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (JSTARS) and is for the Special Issue on Analysis of Big Data in Remote Sensing for REMOTESENSING. Her research interests include data science, big data, and machine learning with applications to astronomy, remote sensing, computer vision, natural language processing, etc.

Research Keywords & Expertise

Big Data
Data Science
High Performance Compu...
machine learning
Remote Sensing

Short Biography

Dr. Mingmin Chi received B.S. and M.S. degrees in electrical engineering from Changchun University of Science and Technology, Changchun, China in 1998 and Xiamen University, Xiamen, China in 2002, respectively, and a Ph.D. degree in computer science from University of Trento, Trento, Italy in 2006. Moreover, she was a student visitor from 2005 to 2006 at the Dept. of Schoelkopf in Max-Planck Institute for Biological Cybernetics, Tuebingen, Germany. Currently, she is an associate professor at School of Computer Science in Fudan University, China. She was a Guest Editor for the Special Iissue on big data in REMOTESENSING for the IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (JSTARS) and is for the Special Issue on Analysis of Big Data in Remote Sensing for REMOTESENSING. Her research interests include data science, big data, and machine learning with applications to astronomy, remote sensing, computer vision, natural language processing, etc.