Yottamine was founded in 2009 to give business executives, scientists, and governments decision makers a powerful, no compromise solution for data mining and predictive modeling that is cost-effective, always available, and able to manage huge data without limitation.

The Yottamine team consists of thought leaders in machine learning theory, senior developers, and marketing veterans from leading technology companies. They are a visionary team with a boots on the ground solutions orientation. Our members have professional backgrounds in Strategy, CRM, E-commerce, Research, and Marketing with industry experience ranging from government research, to heavy industry, high-tech, and pharma.


Dr. Te-Ming (David) Huang, Founder

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.




Prof. Vojislav Kecman, Scientific Adviser

Prof. Kecman authored several books in the areas of machine learning (data mining) and in the fields of mathematical modeling and simulation of system dynamics, notably,”Learning and Soft Computing – Support Vector Machines, Neural Networks, and Fuzzy Logic Model” published by The MIT Press, Cambridge, MA,  and “Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning”, published by Springer-Verlag, Berlin, Heidelberg. The first one is a recommended university textbook at approximately 60 universities globally. Recently, he also co-authored a book on Weakly Coupled Systems Control which is published by CRC Press, Taylor & Francis Group, in 2009.

Emily Huang, Consulting Service Adviser

With two MS degrees in Predictive Analytics and Information Management, Emily provides consulting services to clients through every stages of the machine learning project. With strong technical skills from her Predictive Analytics degree at Northwestern University and solid Information Management knowledge, Emily is able to bridge the information gap between business stakeholders and data scientists. Emily specializes in Marketing and Web Analytic, and brings with her experience in the banking industry in particular.