Artificial Intelligence and Analysis Professor from MIT to Help Guide Anomaly Detection Company’s Technology Roadmap
FRAMINGHAM, Mass. – June 16, 2014 – Prelert, the anomaly detection company, today announced that Tommi Jaakkola, Ph.D., a professor at the Massachusetts Institute of Technology’s (MIT) Computer Science and Artificial Intelligence Laboratory and leader in the field of machine learning and artificial intelligence has joined the company’s advisory board.
“We are honored and excited to have Tommi join the Prelert advisory board,” said Stephen Dodson, Ph.D., Prelert’s CTO. “As a leading authority on machine learning, Tommi’s deep experience and expertise can help direct our R&D efforts, ensuring that our automated behavioral analytics provide customers with the most advanced and easy-to-use solution for their data analytics problems.”
The author of more than 100 academic papers, Jaakkola’s research interests include machine learning, statistical inference and estimation and analysis and development of algorithms for various modern large scale estimation problems, such as those involving predominantly incomplete data sources. His applied research focuses on problems in natural language processing as well as genomics.
“Companies are increasingly receptive to the idea that in order to be successful they need to garner value from the data they’re collecting – and understand that automated tools are necessary to analyze it,” added Jaakkola. “It is an exciting time for Prelert. The company’s technology is perfectly positioned to help companies analyze and understand that data.”
Following a postdoctoral position in computational molecular biology, Jaakkola joined the MIT Electrical Engineering and Computer Science (EEC) faculty in 1998. He received his Ph.D. from MIT in computational neuroscience in 1997 and his M.Sc. in theoretical physics from Helsinki University of Technology in 1992.
About Prelert
Prelert is the anomaly detection company. Its automated behavioral analytics make it easy for users and developers to uncover real-time insights into the operational opportunities and risks hidden in massive data sets. By using unsupervised machine learning technology, Prelert enables non-data scientists to go beyond the limits of search to quickly derive value from their organization’s data. To learn more, please visit www.prelert.com or follow @Prelert.