Ph.D. Candidate Kalantzis Co-Authors Winning Paper at IEEE ICBK 2017
Ph.D. candidate Vassilis Kalantzis received the Best Paper Award at the 2017 IEEE International Conference on Big Knowledge (ICBK) for co-authoring “Factored Proximity Models for Top-N Recommendations.”
Kalantzis was joined by fellow University of Minnesota researcher Athanasios N. Nikolakopoulos, as well as Efstratios Gallopoulos (on a sabbatical leave to U of M CS&E Fall 2016) and John Garofalakis from the University of Petras, in authoring the paper. The team’s work features a simple and versatile Latent Factor framework for Top-N recommendations, which not only outperform several state-of-the-art approaches, but also can be efficiently implemented into current multi-processor architectures.
Advised by Professor Yousef Saad, Kalantzis’s ongoing research lies in the areas of numerical linear algebra and parallel computing. Specifically, his focus is on the design, analysis and implementation of parallel numerical algorithms for the solution of large-scale linear systems, eigenvalue problems, and problems in data analysis.
ICBK was held in Heifei, China with an aim to explore fragmented knowledge from autonomous information sources. The conference provides a premier forum for Big Knowledge research, opportunities, and challenges.
Please join CS&E in congratulating Kalantzis and his co-authors on receiving this award.