Katharina Morik received her doctorate from the University of Hamburg in 1981 and her habilitation from the TU Berlin in 1988. In 1991, she established the chair of Artificial Intelligence at the TU Dortmund University. She is a pioneer of machine learning contributing substantially to inductive logic programming, support vector machines, probabilistic graphical models. In 2011, she acquired the Collaborative Research Center SFB 876 "Providing Information by Resource-Constrained Data Analysis", of which she is the spokesperson. and computing architectures together so that machine learning models may be executed or even trained on resource restricted devices. It consists of 12 projects and a graduate school for more than 50 Ph. D. students. She is a spokesperson of the Competence Center for Machine Learning Rhein Ruhr (ML2R) and coordinator of the German competence centers for AI. She is the author of more than 200 publications in prestigious journals and conferences. She was a founding member, Program Chair and Vice Chair of the conference IEEE International Conference on Data Mining (ICDM) and is a member of the steering committee of and was Program Chair of ECML PKDD. Together with Volker Markl, Katharina Morik heads the working group "Technological Pioneers" of the platform "Learning Systems and Data Science" of the BMBF. Prof. Morik has been a member of the Academy of Technical Sciences since 2015 and of the North Rhine-Westphalian Academy of Sciences and Arts since 2016. She has been awarded Fellow of the German Society of Computer Science GI e.V. in 2019. Dr. Peter Marwedel studied physics at the University of Kiel, Germany. He received his PhD in physics in 1974. As a post-doc, he published some of the first papers on high-level synthesis and retargetable compilation in the context of the MIMOLA hardware description language. In 1987, his habilitation thesis in computer science was accepted. He worked as a professor for computer engineering at TU Dortmund since 1989. He is chairing ICD, a local spin-off of TU Dortmund. His research interests include design automation for embedded systems, in particular the generation of efficient embedded software. Focus is on energy efficiency and timing predictability. Dr. Marwedel published papers on energy-efficient and timing-predictable software, including compiler-supported use of scratchpad memories. He is the author of one of the few textbooks on embedded systems. The book is complemented by videos available on youtube and by publicly available slides. He served as the vice-chair of the collaborative research center SFB 876, aiming at resource-efficient analysis of large data sets since 2011. Dr. Marwedel is an IEEE Fellow. He received the EDAA Lifetime Achievement Award in 2013 and the ESWEEK Lifetime achievement award in 2014.

Katharina Morik received her doctorate from the University of Hamburg in 1981 and her habilitation from the TU Berlin in 1988. In 1991, she established the chair of Artificial Intelligence at the TU Dortmund University. She is a pioneer of machine learning contributing substantially to inductive logic programming, support vector machines, probabilistic graphical models. In 2011, she acquired the Collaborative Research Center SFB 876 "Providing Information by Resource-Constrained Data Analysis", of which she is the spokesperson. and computing architectures together so that machine learning models may be executed or even trained on resource restricted devices. It consists of 12 projects and a graduate school for more than 50 Ph. D. students. She is a spokesperson of the Competence Center for Machine Learning Rhein Ruhr (ML2R) and coordinator of the German competence centers for AI. She is the author of more than 200 publications in prestigious journals and conferences. She was a founding member, Program Chair and Vice Chair of the conference IEEE International Conference on Data Mining (ICDM) and is a member of the steering committee of and was Program Chair of ECML PKDD. Together with Volker Markl, Katharina Morik heads the working group "Technological Pioneers" of the platform "Learning Systems and Data Science" of the BMBF. Prof. Morik has been a member of the Academy of Technical Sciences since 2015 and of the North Rhine-Westphalian Academy of Sciences and Arts since 2016. She has been awarded Fellow of the German Society of Computer Science GI e.V. in 2019. Dr. Peter Marwedel studied physics at the University of Kiel, Germany. He received his PhD in physics in 1974. As a post-doc, he published some of the first papers on high-level synthesis and retargetable compilation in the context of the MIMOLA hardware description language. In 1987, his habilitation thesis in computer science was accepted. He worked as a professor for computer engineering at TU Dortmund since 1989. He is chairing ICD, a local spin-off of TU Dortmund. His research interests include design automation for embedded systems, in particular the generation of efficient embedded software. Focus is on energy efficiency and timing predictability. Dr. Marwedel published papers on energy-efficient and timing-predictable software, including compiler-supported use of scratchpad memories. He is the author of one of the few textbooks on embedded systems. The book is complemented by videos available on youtube and by publicly available slides. He served as the vice-chair of the collaborative research center SFB 876, aiming at resource-efficient analysis of large data sets since 2011. Dr. Marwedel is an IEEE Fellow. He received the EDAA Lifetime Achievement Award in 2013 and the ESWEEK Lifetime achievement award in 2014.

Katharina Morik received her doctorate from the University of Hamburg in 1981 and her habilitation from the TU Berlin in 1988. In 1991, she established the chair of Artificial Intelligence at the TU Dortmund University. She is a pioneer of machine learning contributing substantially to inductive logic programming, support vector machines, probabilistic graphical models. In 2011, she acquired the Collaborative Research Center SFB 876 "Providing Information by Resource-Constrained Data Analysis", of which she is the spokesperson. and computing architectures together so that machine learning models may be executed or even trained on resource restricted devices. It consists of 12 projects and a graduate school for more than 50 Ph. D. students. She is a spokesperson of the Competence Center for Machine Learning Rhein Ruhr (ML2R) and coordinator of the German competence centers for AI. She is the author of more than 200 publications in prestigious journals and conferences. She was a founding member, Program Chair and Vice Chair of the conference IEEE International Conference on Data Mining (ICDM) and is a member of the steering committee of and was Program Chair of ECML PKDD. Together with Volker Markl, Katharina Morik heads the working group "Technological Pioneers" of the platform "Learning Systems and Data Science" of the BMBF. Prof. Morik has been a member of the Academy of Technical Sciences since 2015 and of the North Rhine-Westphalian Academy of Sciences and Arts since 2016. She has been awarded Fellow of the German Society of Computer Science GI e.V. in 2019. Dr. Peter Marwedel studied physics at the University of Kiel, Germany. He received his PhD in physics in 1974. As a post-doc, he published some of the first papers on high-level synthesis and retargetable compilation in the context of the MIMOLA hardware description language. In 1987, his habilitation thesis in computer science was accepted. He worked as a professor for computer engineering at TU Dortmund since 1989. He is chairing ICD, a local spin-off of TU Dortmund. His research interests include design automation for embedded systems, in particular the generation of efficient embedded software. Focus is on energy efficiency and timing predictability. Dr. Marwedel published papers on energy-efficient and timing-predictable software, including compiler-supported use of scratchpad memories. He is the author of one of the few textbooks on embedded systems. The book is complemented by videos available on youtube and by publicly available slides. He served as the vice-chair of the collaborative research center SFB 876, aiming at resource-efficient analysis of large data sets since 2011. Dr. Marwedel is an IEEE Fellow. He received the EDAA Lifetime Achievement Award in 2013 and the ESWEEK Lifetime achievement award in 2014.