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. He is 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 is one of the world leading academics in the fields of machine learning and data mining with nearly three decades of experience in developing novel machine learning techniques and applying them to real-world problems. His notable contributions are in developing Local Linear SVMs Algorithm, Fast LinearSVM, Active Set Algorithms for SVMs, ISDA Algorithm for SVMs as well as in proving mathematical equivalence of RBF NNs and Fuzzy Logic Models. He was Fulbright Professor at MIT, DFG Professor at TH Darmstadt; DAAD Konrad Zuse Professor at FH Heilbronn, FHTW Berlin and SWFH Soest; Research Fellow at Drexel University, and at Stuttgart University, as well as the professor at both The University of Auckland and Zagreb University.
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, (see, www.support-vector.ws) and “Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning”, published by Springer-Verlag, Berlin, Heidelberg, (see www.learning-from-data.com). 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.