Experimental Design using Active Learning and Bayesian Optimization

At the 9th International Summer School on AI and Big Data, Dr. Gunar Ernis and Dorina Weichert (Fraunhofer IAIS) will talk about Experimental Design using Active Learning and Bayesian Optimization. The talk will take place on Thursday 06.07.2023 from 4:30 p.m. – 5:30 p.m.

Talk: Experimental Design using Active Learning and Bayesian Optimization

Real-world data can be very expensive: laboratory experiments, numerical simulations, training large neural networks not only takes a lot of time, but also a lot of money. Nevertheless, the collection of these data is mandatory if progress is to be made. Statistical design of experiments is a traditional way to find out the necessary data from the ones that may be generated. However, there have also been relevant advances from the AI field in recent years: active learning and Bayesian optimization offer the possibility to create particularly efficient sequential experimental designs.

In this talk, relevant methods from Bayesian Optimization and Active Learning, their similarities, differences, and limitations will be presented. In addition to standard extensions for practical use, we will show excerpts from the state-of-the-art and finally the application in really relevant applications: the United Nations Sustainable Development Goals.

Dr. Gunar Ernis

Dr. Gunar Ernis is Business Unit Manager Industrial Analytics. He holds a PhD in Experimental Particle Physics and has been working as a Data Scientist at Fraunhofer IAIS since 2016. He is intensively involved in the analysis of data in the industrial environment (Industry 4.0) and is active in several projects there that deal with condition monitoring and predictive maintenance.

Dorina Weichert

Dorina Weichert is a mechanical engineer and works and researches at Fraunhofer IAIS in the area of Design of Experiments and Bayesian Optimization. She prefers to work with Gaussian processes, which efficiently combine a small amount of data with prior knowledge to create a trustworthy model. In her day-to-day work, she supports experts in the Industrial Analytics Team in designing experiments, analyzing data, and optimizing processes.

Read more about the 9th International Summer School on AI and Big Data.