Te-Ming Huang

Dr. Huang has made a number of significant contributions to the science of machine learning theory and authored the monograph, “Kernel Based Algorithm for Mining Huge Data Sets, Supervised, Semi-Supervised and Unsupervised Learning”. This is the first work to treat supervised, semi-supervised and unsupervised learning in a unified way. After almost a decade since its first publication, the ISDA algorithm published in the monograph is recently adopted by MathWorks Inc. in their popular machine learning toolbox for Matlab. Dr. Huang is also the winner of the best paper award in the KES 2004 international conference due to his novel contribution to the area of semi-supervised learning. He also developed the first graph-based semi-supervised learning software, SemiL, which is very popular among researchers. SemiL has been applied to many areas including natural language processing, pattern recognition and text classification.

Prior to starting Yottamine Dr. Huang worked for a number of corporations to apply large-scale data mining techniques to key business challenges and operations optimization, targeting digital marketing, text classification, gene microarray analysis, and traffic prediction. Before Yottamine Analytics, Dr. Huang was a research scientist at Microsoft and the senior scientist at INRIX where he was specialized in applying his research to commercial applications, in particular large-scale web classification and real-time traffic prediction.