Our Name
The name Yottamine was created by Dr. Te-Ming Huang through combining the word "Yotta" and "mine" to symbolize the idea of enormous amounts of data. Yotta, the factor of 10 to the power of 24, and the word mine" to symbolize a "data-mine", comes together to represent the service that Yottamine offers, which is data-mining for large data sets.
Our Story
Though founded by Dr. Te-Ming Huang, the technology behind Yottamine was already in the works back when Dr. Huang was conducting his Ph.D research with Professor Vojislav Kecman. While working commercially after completing his Ph.D, Dr. Huang saw an increasing demand for applying data-mining in various areas to help businesses solve real-world problems. It was then that the idea of creating a product to meet the growing demands of data-mining for large data-sets was born.
Our People
Dr. Te-Ming (David) Huang, Founder
Dr. Huang is a renowned expert in the area of machine learning and has extensive commercial experience on applying large-scale data mining techniques to various real-world problems, including digital advertisement, text classification, gene microarray analysis and traffic prediction. Prior to Yottamine Analytics, Dr. Huang was a research scientist at Microsoft and the senior scientist at INRIX where he was specialized in applying his research result to commercial applications, in particular large-scale web classification and real-time traffic prediction.
Dr. Huang made several significant contributions to the area of learning from data. His monograph "Kernel Based Algorithm for Mining Huge Data Sets, Supervised, Semi-Supervised and Unsupervised Learning" was the first book that treats supervised, semi-supervised and unsupervised learning in a unified way. He 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. In addition, he also developed the first graph-based semi-supervised learning software, SemiL, which is very popular among the research society. SemiL has been applied to many areas including natural language processing, pattern recognition and text classification.
You can visit his site learning-from-data.com/te-ming/ to find out more.
Professor Vojislav Kecman, Scientific Adviser
Director of Learning Algorithms and Applications Laboratory (LAAL)
Computer Science Department, VCU, Richmond, VA
Prof. Kecman is one of the world leading academics in the fields of machine learning and data mining with about 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, Cambridge, MA, USA; DFG Professor at TH Darmstadt; DAAD Konrad Zuse Professor at FH Heilbronn, FHTW Berlin and SWFH Soest; Research Fellow at Drexel University, Philadelphia, PA 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 about 60 universities all around the world. Recently, he also co-authored a book on Weakly Coupled Systems Control which is published by CRC Press, Taylor & Francis Group, in 2009.
You can visit his site www.people.vcu.edu/~vkecman to find out more.