Gábor Takács

E-mail: , Phone: +36/96503400/3261, Office: B610.

Introduction

I received MSc in computer engineering (2004), and PhD in machine learning (2010) from the Budapest University of Technology and Economics. My main research interests are machine learning and artificial intelligence.

Selected publications

• G. Takács, D. Tikk, Alternating least squares for personalized ranking, received Honorable Mention at 6th ACM Conference on Recommender Systems (RecSys 2012), Dublin, Ireland, 2012. [pdf] [erratum]
• G. Takács, Convex polyhedron learning and its applications, PhD thesis, Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics, Budapest, Hungary, 2010. [pdf]
• G. Takács, I. Pilászy, B. Németh, D. Tikk, Scalable collaborative filtering approaches for large recommender systems, Journal of Machine Learning Research (Special Topic on Mining and Learning with Graphs and Relations), 10: 623-656, 2009. [pdf]
• G. Takács, I. Pilászy, B. Németh, D. Tikk, Investigation of various matrix factorization methods for large recommender systems, Proc. of the 2nd KDD Workshop on Large Scale Recommender Systems and the Netflix Prize Competition, Las Vegas, Nevada, USA, 2008. [pdf]

Competitions

My program, Tyson, won the 2nd Hungarian Computer Go-moku Open Tournament (2005). I was the captain of team Ensemble that finished second in the Netflix Prize competition (2009). I won third prize in GE Flight Quest (2013).

Projects

• Polyp: Classification of polyps based on colonoscopic images (2019-).
• Parmod: Massively parallel CFD simulations on unstructured meshes (2010-2011).
• Netflix: Collaborative filtering based movie recommender system (2006-2009).
• Tyson: Tournament winner go-moku AI (2003-2007).
• TgMammo: Computer-aided breast cancer detection in X-ray images (2003-2005).
• TgLetter: An experimental postal code recognition system (2003).
• Recognizer: Handwritten digit recognition with neural networks (2002).