Skip to main content
MSU CSE Colloquium Series 2014-2015: Hu Ding Title: Novel Geometric Algorithms for Machine Learning Problems

Hu Ding
State University of New York at Buffalo

Time: Tuesday, Feb 10, 2015 (10am)
Location: EB 3105


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 this talk, I will show that such challenges in a number of fundamental machine learning problems can be resolved by exploiting their geometric properties. Particularly, I will present three geometric-algorithm-based results for various machine learning problems: (1) a unified framework for a class of constrained clustering problems in high dimensional space; (2) a combinatorial algorithm for support vector machine (SVM) with outliers; and (3) algorithms for extracting chromosome association patterns from a population of cells. The first two results are for fundamental problems in machine learning, and the last one is for studying the organization and dynamics of the cell nucleus, an important problem in cell biology. Some geometric-algorithm-based future work in machine learning will also be discussed.


I am a final year Ph.D student under supervision of Dr. Jinhui Xu, in the Department of Computer Science and Engineering, State University of New York at Buffalo, since Sep 2009. I received my bachelor degree in Mathematics from Sun Yat-Sen (Zhong Shan) University in Jun 2009. My research centers around designing efficient algorithms for machine learning and pattern recognition, especially on large-scale, high dimensional, and noisy datasets. My research emphasizes both theoretical development and their applications in real world, e.g., data analytics, data mining, big data, social network, computer vision, and biomedical image analysis.


Dr. Pang-Ning Tan