Karsten Borgwardt

Karsten Borgwardt
Born	December 1980 (age 43–44) Kaiserslautern, Rhineland-Palatinate, Germany
Nationality	German
Alma mater	LMU Munich University of Oxford
Scientific career
Fields	Computer science, Machine learning, Computational biology, Systems biology, Bioinformatics,
Institutions	Max Planck Institute of Biochemistry (2023-), Ludwig Maximilian University of Munich (2023-), ETH Zurich (2014-2023), University of Tübingen (2011-2014), Max Planck Institute for Biological Cybernetics (2008-2014), University of Cambridge (2007-2008)
Thesis	Graph Kernels (2007)
Doctoral advisor	Hans-Peter Kriegel
Website	www.biochem.mpg.de/borgwardt

Karsten Borgwardt (born 1980) is a German computer scientist specializing in machine learning and computational biology. Since February 2023, he has been a director at the Max Planck Institute of Biochemistry in Martinsried, Germany, where he leads the Department of Machine Learning and Systems Biology.

Education and career

Borgwardt was born in Kaiserslautern. He obtained a Diplom (equivalent to a master’s degree) in computer science from LMU Munich in 2004 and a Master of Science in biology from the University of Oxford in 2003. During his studies, he was a scholar of the Stiftung Maximilianeum, the Bavarian Foundation for the Promotion of the Gifted, and the German Academic Merit Foundation (Studienstiftung).

In 2007, Borgwardt earned his Ph.D. in computer science from LMU Munich. His dissertation received the Heinz Schwärtzel Dissertation Award for Foundations of Computer Science. Following a postdoctoral position at the University of Cambridge, he became a research group leader for machine learning and computational biology at the Max Planck Institute for Biological Cybernetics and the former Max Planck Institute for Developmental Biology in Tübingen in 2008.

In 2011, Borgwardt was appointed professor of data mining in the life sciences at the University of Tübingen. In 2014, he joined ETH Zurich as an associate professor in the Department of Biosystems Science and Engineering (D-BSSE) and was promoted to full professor in 2017. During his tenure at ETH Zurich, he coordinated significant research programs, including two Marie Curie Innovative Training Networks and the Personalized Swiss Sepsis Study, focusing on the prediction of sepsis using machine learning. In 2023, he was appointment as Scientific Member of the Max Planck Society and as Director at the Max Planck Institute of Biochemistry in Martinsried.

In 2012, Borgwardt co-founded Computomics, a bioinformatics service company based in Tübingen, where he serves as a scientific mentor.

Research contributions

Borgwardt’s research integrates big data analysis with biomedical research. He received the Heinz Schwärtzel Dissertation Award for his PhD thesis in 2007. develops novel machine learning algorithms to detect patterns and statistical dependencies in large biological and medical datasets. His work aims to enable the automatic generation of new knowledge from big data and to understand the relationship between the function of biological systems and their molecular properties, which is fundamental for personalized medicine.

Awards and honors

Borgwardt received scholarships from the Studienstiftung des deutschen Volkes in 2002 and 2007. He was awarded the Alfried-Krupp-Förderpreis for early career researcher in 2013 and the ERC Starting Grant in 2015. In 2016, he was among the “Top 40 under 40 in Science & Society” in Germany selected by Capital magazine. In 2018, Borgwardt was named among “25 individuals who have the potential to shape the next 25 years” by Focus magazine. In 2023, Borgwardt recieved honorary professorship from Ludwig Maximilian University of Munich by the Faculty of Chemistry and Pharmacy.

Publications from Borgwardt's group have received the Outstanding Student Paper Award in NIPS in 2009., the Graduate Paper Award in 2020 and the Remarkable Output Award in 2020 and 2021, both from the Swiss Institute of Bioinformatics.

Selected publications

• Weisfeiler-Lehman Graph Kernels (’‘Journal of Machine Learning Research’’, 2011): Introduced an efficient graph kernel based on the Weisfeiler-Lehman algorithm.
• “Early Prediction of Circulatory Failure in the Intensive Care Unit Using Machine Learning” (’‘Nature Medicine’’, 2020): Presented a machine learning approach for early detection of circulatory failure in ICU patients.
• “Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning” (’‘Nature Medicine’’, 2022): showcased the feasibility of predicting antimicrobial resistance from readily collected mass spectrometry data in the hospital. The new method is able to identify antibiotic resistance 24 hours earlier than previous methods.