IBM Israel Research Seminars

Predictive Analytics centers around the use of machine learning and statistical modeling techniques to build data mining systems and predictive modeling applications. This talk will present a synopsis of recent and ongoing related activities in the Math. Sciences department. Although there have been a number of notable successes there remain many technical problems that need to be solved to fully leverage predictive analytics for business decision support. Key project accomplishments will be highlighted. Ongoing research activities will be discussed.

About the Speaker
Chid Apte leads the Data Analytics effort in the Mathematical Sciences Department at IBM's T.J. Watson Research Center. His current areas of responsibilities include the Analytics Architectures, Predictive Modeling, and Data Mining Systems groups, which are involved in conducting research and development of analytics based middleware and applications, for areas including Supply Chain Management, Customer Relationship Management, Risk Management, and related business processes. Chid received his BTech in EE from IIT Bombay and PhD in Computer Science from Rutgers University, and has been with IBM Research since 1984. He is interested in Data Mining, Business Intelligence Solutions, and Knowledge-based Systems, and currently focused on leveraging machine learning and computational statistics for scaling up and automating data analytics for use in IBM’s industry solutions and services efforts. He has published extensively in his areas of expertise, and actively involved in organizational aspects of leading data mining conferences.