Machine Learning Expert Joins MSU
Machine learning is a discipline that concerns the construction and study of algorithms for learning from data, and plays a critical role in many other fields, such as computer vision, speech recognition, social network, bioinformatics, etc. As the data scale increases dramatically in the big-data era, a number of new challenges arise, which require new ideas from other areas. In his research, Ding has found that a number of fundamental and challenging machine learning problems can be modeled in high dimensional space and resolved by exploiting their geometric properties. Particularly, he is interested in designing high dimensional geometric algorithms for various machine learning problems, such as clustering, classification, regression, distributed learning, etc. His research emphasizes both the theoretical development and efficiency in reality.
Ding has published a number of papers in top conferences and journals, including SODA, NIPS, AAAI, CVPR, and PLoS Computational Biology.
Research Interests: Algorithms, Computational Geometry, Machine Learning, Pattern Recognition, Biomedical Imaging
PhD: Ding is a PhD candidate in the Department of Computer Science and Engineering at the State University of New York at Buffalo. He received his bachelor’s degree in mathematics from Sun Yat-Sen (Zhong Shan) University in 2009.
Recent Honors: A recent paper in Human Molecular Genetics is recommended by F1000Prime as an Article of Special Significance to the research on 3D pattern of chromosome territories.
Publications and Papers: See full listing on his webpage.