Hu Ding
State University of New York at Buffalo
http://www.acsu.buffalo.edu/~huding/
Time: Tuesday, Feb 10, 2015 (10am)
Location: EB 3105
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Abstract:
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.
Biography: 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.Host: Dr. Pang-Ning Tan |