Priv.-Doz. Dr. Bogdan SavchynskyyGroup LeaderDr. Bogdan Savchynskyy HCI am IWR Berlinerstr. 43 69120 Heidelberg Room B108, Phone: +49 (6221) 54 14604 Email: bogdan.savchynskyy at iwr.uni-heidelberg.de |
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About me |
Publications | Projects | Teaching & Presentations |
Practical, Bachelor and Master Projects
I am constantly looking for motivated students for Practical, Bachelor and Master projects in the field of large-scale combinatorial optimization with applications in computer vision and machine learning. Apart from the usual formal requirements to start a project I expect- An excellent mark for my lecture "Optimization for Machine Learning" or similar background in integer linear programming and combinatorial optimization.
- Strong programming skills in Python and C++ and ability to work with a third-party code.
PhD Student Positions
I am constantly looking for motivated PhD students. Requirements are similar to those for Master students:- An excellent mark for my lecture "Optimization for Machine Learning" or similar background in integer linear programming and combinatorial optimization.
- Strong programming skills in Python and C++ and ability to work with a third-party code.
News!
2022, Jul. Our graph matching benchmark paper has been accepted to ECCV 2022! Check it at the Publication page as well as the benchmark web-page!2022, Mar. Our joint together with Dagmar Kainmüller DFG project proposal "Unsupervised Model Discovery for Stereotypical Organisms" has been accepted!
2021, Jul. Our paper describing the new state-of-the-art graph matching solver is accepted to ICCV as oral! See the Publication page.
2021, Jan. Our paper describing the new state-of-the-art graph matching solver is now on ArXiv! See the Publication page.
2020, Jan.: Two our papers have been accepted to AISTATS 2020! See the Publication page.
2019, Dec.: My book Discrete Graphical Models - An Optimization Perspective is published!
2019, Sep.: We start a regular research seminar on convex and combinatorial optimization!
2018, Aug.: My book Discrete Graphical Models - An Optimization Perspective is submitted for publication to Now Publishers!
2018, July: Our paper about a new higly parallelizable state-of-the-art message passing method for dense non-Gaussian graphical models accepted to ECCV 2018! See the reference to the source code and supplementary material at the Publication page.
2017, Nov.: Our paper about a new scalable ILP method (CombiLP) accepted to AAAI 2018! See the reference to the source code and supplementary material at the Publication page.
2017, Sep.: Our overview of CNN+CRF methods was accepted to IEEE Signal Processing Magazine!