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Prof'in Dr. Barbara Hammer

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18. Center for Interdisciplinary Research / ZiF Boards / Academic Advisory Board

Member of the Advisory Board of ZiF - Bielefeld University - Faculty of Technology

Curriculum Vitae

since 2023: Recipient of a LAMARR Fellowship
since 2023: Principal investigator in MSCA Doctoral network LEMUR - Learning with Multiple Representations
since 2022: Chair of large-scale NRW-network project SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems with 39 principal investigators
2022: Best Paper Award ICANN 2022, eight previous best paper awards including ICDM2016
since 2021: Principal investigator in TRR 318 Constructing explainability
since 2021: Principal investigator in ERC Synergy Grant Smart Water Futures: designing the next generation of urban drinking water systems
since 2020: Chair of NRW-wide research training group on trustworthy AI Data-NinJA with 10 principal investigators
since 2017: Professor (W3), Machine Learning, Bielefeld University
2010 - 2017: Professor (W2), Theoretical Computer Science, Bielefeld University
2005: Research stay as guest professor, SAMOS, University Paris I
2004 - 2010: Professor (W2), Theoretical Computer Science, Clausthal University of Technology
2003: Research stays at University of Padova and University of Birmingham
2003: Habilitation in Computer Science, University of Osnabrück
2001: Research stay at University of Pisa
2000 - 2004: Independent junior research group leader, Machine Learning, University of Osnabrück
2000: Research stay at Centre for Artificial Intelligence and Robotics, Bangalore
1999: Research stay at Rutgers University
1999: Doctorate, supervisor: Volker Sperschneider, Mathukumali Vidyasagar (external reviewer), Machine Learning, University of Osnabrück, Germany
1995 - 1999: Research Assistant, Theoretical Computer Science, University of Osnabrück
1989 - 1995: Studies of Mathematics, University of Osnabrück, Germany

Current research topics

Barbara Hammer's expertise lies in the field of intelligent data analysis, explainable machine learning, and trustworthy AI. The methods range from the foundation of algorithms to their efficient application and interdisciplinary aspects, for example in the context of fairness or social implications. Specific current topics are learning for complex spatial-temporal or non-euclidean data including graph networks, explainability of models, or online learning and learning with drift. Applications range from the field of intelligent technical systems to critical infrastructure and the life sciences.

Memberships and Functions

Editorial Board of various journals, e.g. IEEE TPAMI

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