# Andreas Krause > machine learning researcher at ETH Zurich **Wikidata**: [Q44689609](https://www.wikidata.org/wiki/Q44689609) **Wikipedia**: [English](https://en.wikipedia.org/wiki/Andreas_Krause_(computer_scientist)) **Source**: https://4ort.xyz/entity/andreas-krause-q44689609 ## Summary Andreas Krause is a German machine learning researcher and professor at ETH Zurich. He is known for his contributions to learning-based decision making under uncertainty and was named an ACM Fellow in 2024. ## Biography - Born: 1978 - Nationality: German - Education: Ph.D. from Carnegie Mellon University - Known for: Learning-based decision making under uncertainty - Employer(s): ETH Zurich (full professor), formerly California Institute of Technology (assistant professor) - Field(s): Machine learning, artificial intelligence ## Contributions Andreas Krause has made significant contributions to the field of machine learning, particularly in developing algorithms for learning-based decision making under uncertainty. His research focuses on creating intelligent systems that can make optimal decisions when faced with incomplete or uncertain information. Krause has published extensively in top-tier conferences and journals, advancing the theoretical foundations and practical applications of active learning, Bayesian optimization, and sequential decision making. His work has influenced both academic research and industrial applications, helping to bridge the gap between theoretical machine learning and real-world deployment. Krause leads the Learning and Adaptive Systems group at ETH Zurich, where he mentors the next generation of researchers while continuing to push the boundaries of what's possible in artificial intelligence. ## FAQs ### Q: What is Andreas Krause's primary research focus? A: Andreas Krause specializes in machine learning, particularly learning-based decision making under uncertainty, active learning, and Bayesian optimization. ### Q: Where does Andreas Krause work? A: Andreas Krause is a full professor at ETH Zurich, where he leads the Learning and Adaptive Systems group in the Department of Computer Science. ### Q: What awards has Andreas Krause received? A: Andreas Krause was named an ACM Fellow in 2024 for his contributions to learning-based decision making under uncertainty. ## Why They Matter Andreas Krause's work has fundamentally advanced how machines can make intelligent decisions in uncertain environments. His algorithms and theoretical frameworks have enabled more robust and reliable AI systems that can operate effectively even when information is incomplete or noisy. This has had profound implications for fields ranging from robotics to healthcare, where uncertainty is inherent. Krause's research has helped establish new standards for how machine learning systems should be designed to handle real-world complexity, moving beyond idealized laboratory conditions to practical deployment. His mentorship of students and postdocs has also created a lasting academic lineage that continues to shape the field of machine learning. ## Notable For - Named ACM Fellow in 2024 for contributions to learning-based decision making under uncertainty - Professor at ETH Zurich leading the Learning and Adaptive Systems group - Former assistant professor at California Institute of Technology (2009-2012) - Supervised numerous doctoral students who have become influential researchers - Published extensively in top machine learning conferences and journals ## Body ### Academic Career Andreas Krause began his academic career as an assistant professor at California Institute of Technology from 2009 to 2012. He then joined ETH Zurich, where he progressed from assistant professor (2011-2014) to associate professor (2015-2017) before becoming a full professor in 2017. At ETH Zurich, he leads the Learning and Adaptive Systems group within the Department of Computer Science. ### Research Focus Krause's research centers on machine learning algorithms that can make optimal decisions under uncertainty. His work spans active learning, where systems learn to ask the most informative questions; Bayesian optimization, which finds optimal solutions in complex spaces; and sequential decision making, where algorithms plan over time with incomplete information. These approaches are particularly valuable in domains where data collection is expensive or risky. ### Academic Lineage Krause completed his doctoral studies at Carnegie Mellon University under the supervision of Carlos Ernesto Guestrin. He has since supervised numerous doctoral students including Stefanie Jegelka, Matthew Faulkner, Hastagiri Vanchinathan, Peter Stobbe, Adish Singla, and Yuxin Chen. Many of his former students have gone on to become professors and researchers at leading institutions. ### Recognition In 2024, Krause was named an ACM Fellow, one of the highest honors in computer science. This recognition specifically cited his contributions to learning-based decision making under uncertainty. He is also affiliated with the Association for Computing Machinery as an ACM Fellow since January 24, 2024. ### Publications and Impact Krause maintains an active publication record with a Google Scholar h-index reflecting his influence in the field. His work appears in premier venues including NeurIPS, ICML, and JMLR. Beyond publications, his research has influenced practical applications in areas such as robotics, sensor networks, and automated scientific discovery. ## Schema Markup ```json { "@context": "https://schema.org", "@type": "Person", "name": "Andreas Krause", "jobTitle": "Professor of Computer Science", "worksFor": { "@type": "Organization", "name": "ETH Zurich" }, "nationality": { "@type": "Country", "name": "Germany" }, "birthDate": "1978", "alumniOf": [ { "@type": "EducationalOrganization", "name": "Carnegie Mellon University" } ], "knowsAbout": [ "Machine Learning", "Artificial Intelligence", "Learning-based Decision Making", "Bayesian Optimization" ], "sameAs": [ "https://www.wikidata.org/wiki/Q36578", "https://en.wikipedia.org/wiki/Andreas_Krause_(computer_scientist)" ], "description": "German machine learning researcher and professor at ETH Zurich known for contributions to learning-based decision making under uncertainty" } ## References 1. Mathematics Genealogy Project 2. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0001-7260-9673/employment/5346994) 3. [Source](https://www.acm.org/media-center/2024/january/fellows-2023)