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Thomas Bäck

Thomas Bäck

Chief Scientist

thom@norceresearch.no
Jon Lilletuns vei 9 H, 3. etg, 4879 Grimstad, Norway

I am a computer scientist by training, and my academic work is multidisciplinary. My work intersects strongly with biology (being the inspiration for global optimization algorithms gleaned from the model of organic evolution), engineering (being the source of design optimization problems from areas such as automotive, aerospace, ship design, and others), and business applications, giving me practical insights in improved optimization results (through my own industrial experience, solving hard optimization problems for industry).

In my work, I combine my interest in a deep, fundamental understanding of algorithms and their working principles with an empirical analysis of their results and my curiosity for solving hard real-world problems. I am inspired by trying to solve the apparently “unsolvable” practical problems (e.g., car safety optimization with more than one hundred parameters in less than two hundred simulation runs). Finding the best approach to solve such problems and understand its fundamental working principles is fascinating me and drives my research interest. Consequently, I am recognized worldwide as a leading expert in developing advanced variants of evolutionary algorithms for solving the most challenging industrial optimization problems, while at the same time striving for a solid scientific understanding of the foundations of algorithms.

Currently, my main interest is in automatizing the algorithm development process for new optimization algorithms, to ensure that the best possible optimization algorithm is created automatically, given a new optimization problem. The LION approach would allow me to further explore this new line of research.

My two NWO-funded projects and the EU Marie Curie Industrial Training Network (ITN) ECOLE are at the base of the ATOPGOAL ambition, since they are dealing with demanding practical optimization problems and advanced machine learning algorithms such as deep learning, advanced Gaussian processes, and efficient global optimization. They create a framework in which ATOPGOAL will be perfectly embedded, enabling the breakthrough research that builds on the output provided by these projects.

Thomas Bäck

Division

Energy & Technology

Research Groups

Smart instrumentering og industriell testing

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