The State of North Rhine-Westphalia provides a grant for the AI Graduate College Data-NInJA („Trustworthy AI for Seamless Problem Solving: Next Generation Intelligence Joins Robust Data Analysis“), coordinated by Prof. Barbara Hammer, University Bielefeld. The grant consists of seven PhD tandems, which were selected out of 37 applications for this grant by an expert jury.

TH Köln takes part in this Graduate College with a PhD tandem together with Ruhr-University Bochum (RUN). Under the title „(RL)³: Representation, Reinforcement und Rule Learning“ the research is conducted by Prof. Laurenz Wiskott from the Neuroinformatics Institute, RUB, and Prof. Wolfgang Konen from the Cologne Institute of Computer Science, TH Köln, together with two PhD students at each institution. The reseach aims at explainable and interpretable AI models, where methods from (deep) reinforcement learning and representation learning will play an important role. The new element of this research project is that both PhD students, one at RUB and one at TH Köln, will work closely together in the PhD tandem, together with their supervisors.

First Place in Opitz Reward 2020 for Jordan Scholzen (Source: Jordan Scholzen / TH Köln)

Jordan Scholzen got with his Bachelor's thesis "Künstliche Intelligenz in der Kompositionslehre - Eine Untersuchung von Long-Short-Term-Memory-Netzen zur Analyse von Kontrapunkten nach Fux" the first place in the Opitz-Innovation Reward 2020. The thesis, supervised by Prof. Dr. Wolfgang Konen, investigates how a supportive AI for music scholars studying composition can look like. Scholzen showed that neural networks of LSTM type allow to learn whether a certain musical line in a counterpoint violates or adheres to the rules once formulated by famous baroque music instructor Johann Joseph Fux.

It is characteristical and shows the importance of the area AI that all 3 Opitz rewards in 2020 covered themes connected with AI. More about Opitz-Innovationspreis 2020 (sorry, in German only) can be found here. The full Bachelor's thesis (again sorry: only German) can be found
here.

The Covid-19 crisis pushed many of us to change our working styles. As universities all over Germany changed to an online version, video streaming became a daily necessecity for students to follow the lectures and for professor to be able to evaluate the students' presentations. We hear very often that the stay-home orders amend Covid-19 crisis had an immediate positive impact on the air-pollution and the environment in general but the question is if our new adapted life-style keeps this positive impacts also on the longer runs. Therefore, it is very interesting to answer questions like: "What are the CO2 costs of video streaming?". Professor Wolfgang Konen takes a look at this very important question in this article (unfortunately only in German).


I am very happy to announce that on Wednesday, April 8th 2020, the PhD-colloquium of Samineh Bagheri, which I had the honor to supervise at TH Köln, successfully took place. Her thesis “Self-Adjusting Surrogate-Assisted Optimization Techniques for Expensive Constrained Black Box Problems” deals with state-of-the art optimization algorithms supported by RBF surrogate models.  In normal times we would have been all traveling to Leiden University and would have made the ceremony in the prestigious Leiden senate hall. But times are not normal. Due to Covid-19 it was impossible to proceed as planned. But Leiden University is very innovative, and they decided to do their first-time ever fully-online (read more...)

 

We are happy to inform you that a new version of the SACOBRA package is available on CRAN. SACOBRA is a self-adjusting surrogate assisted optimization framework designed for expensive black-box constrained problems.

This version of SACOBRA can handle black-box equality constraints  as described in this paper. Also the online model selection algorithm described in an award-winning paper is available to use in our recent release of SACOBRA. Additionally, the implementation of all 24 G-problems (the most commonly used test-suite for black-box constrained optimization problems in optimization community) is directly available in our package. If you have used SACOBRA 1.1 you might have experienced repeated warning messages coming from nloptr package, this issue is also resolved for the moment.

 

You can easily install SACOBRA by running the following line in your R command line:

install.packages("SACOBRA",dependencies=T)

 

If you have any interesting or challenging constrained optimization problems that you want to solve efficiently, apply SACOBRA and let us know about your experience.