Naoki Abe  Naoki Abe photo       

contact information

Senior Manager and Principal RSM, Center for Computational and Statistical Learning, Member of AoT
Thomas J. Watson Research Center, Yorktown Heights, NY USA



Naoki Abe is a principal research staff member and senior manager of Center for Computational and Statistical Learning within the Blockchain, Cognitive Solutions and Mathematical Sciences Department.

Dr. Abe obtained his B.S. and M.S. degrees in computer science from MIT in 1984, and Ph. D. in computer science from University of Pennsylvania in 1989. He was an intern at IBM Watson Research Center for many years during this time, including a one-year temporary position in 1984 to 1985. He also spent a year as a post-doctoral researcher at U. C. Santa Cruz and conducted research in the area of computational learning theory. During the 1990’s he worked for NEC Research Laboratories where he was engaged in research in theory and applications of machine learning. During this time, he was also involved in government-sponsored projects of Real World Computing and Discovery Science. From 1998 to 2000, he was an adjunct associate professor at the Tokyo Institute of Technology. In 2001 he rejoined IBM Watson Research Center as a research staff member, where he has since been conducting research in the areas of machine learning and data mining.

He has been with IBM Research since 2001, conducting research in the development of novel machine learning methodologies that open up new applications in a variety of areas of business analytics. His research activities have ranged from applications of reinforcement learning to business analytics, methods for anomaly detection, temporal causal modeling, cost-sensitive learning and on-line active learning, among other topics. In recent years, he has been involved in the applications of data analytics specifically to agriculture, currently leading the IBM team in a government funded joint project with Purdue University on integrated genotype phenotype analysis for accelerating breeding of biofuel crops. Methodologies that he co-developed through these research efforts have made their ways into a number of IBM offerings, including Temporal Causal Modeling engine in SPSS Modeler and Statistics, Granger anomaly detection engine in Smart Cloud Predictive Insights, the Tax Collections Optimizer and Signature Solutions “Next Best Action” and “CFO-dashboard.”