Scholar
Sebastian Lapuschkin
Google Scholar ID: wpLQuroAAAAJ
Head of Explainable AI, Fraunhofer Heinrich Hertz Institute
Citations & Impact
Citations
18,449
H-index
36
i10-index
61
Publications
20
Co-authors
13
list available
Contact
Publications
Resume (English only)
Research Experience
- 2023-06-19: Keynote at IEEE CVPR'23 Workshop 'Safe Artificial Intelligence for All Domains', Vancouver, Canada
- 2023-04-26: Keynote at Polish Conference on Artificial Intelligence, Lodz, Poland
- 2023-03-28: Invited Talk at Workshop on 'Explainability in Machine Learning', Tübingen, Germany
- 2023-01-10: Talk at IPAM Workshop 'Explainable AI for the Sciences: Towards Novel Insights', UCLA, CA, USA
- 2022-11-24: Talk at 3rd International Workshop on Auditing AI-Systems, Berlin, Germany
- 2022-12-08: Talk at Max Planck School of Cognition Academy, Berlin, Germany
- 2022-11-26: Talk at Symposium of the German National Academy of Sciences - Leopoldina, Halle, Germany
- 2022-10-20: Talk at 3rd ERCIM-JST Workshop, Paris, France
- 2022-10-13: Talk at 2022 Workshop on Self-Supervised Learning for Signal Decoding, Aalborg, Denmark
- 2022-09-15: Talk at 11th Heinz Nixdorf Symposium, Paderborn, Germany
- 2022-10-10: Tutorial at 7th Summer School on Data Science (SSDS-2022), virtual event
- 2022-07-25: Tutorial at 7th International Gran Canaria School on Deep Learning, Gran Canaria, Spain
- 2022-06-26: Tutorial at 24th International Conference on Human-Computer Interaction, virtual event
- 2021-07-23: Talk at IEEE ICML Workshop on 'Theoretic Foundation, Criticism, and Application Trend of Explainable AI', online event
- 2021-06-20: Talk at IEEE CVPR Workshop on 'Interpretable Machine Learning for Computer Vision', online event
- 2021-06-08: Talk at 2nd Eddy Cross Disciplinary Symposium, online event
- 2021-05-19: Talk at HEIBRIDS Lecture Series, online event
- 2020-10-19: Talk at ACM CIKM'20 Workshop on 'Advances in Machine Learning and Interpretable AI', Galway, Ireland
- 2020-10-04: Talk at MICCAI 2020 Workshop on 'Interpretability of Machine Intelligence in Medical Image Computing', Lima, Peru
- 2020-09-18: Tutorial on 'Explainable AI for Deep Networks: Basics and Extensions' at ECML/PKDD 2020 (virtual event)
- 2020-09-01: Class on 'Introduction to Explainable AI' at International Summer School on Deep Learning 2020 (virtual event)
- 2020-08-28: Class on 'Interpretable and explainable deep learning' at Summer School on Machine Learning in Bioinformatics 2020 (virtual event)
- 2020-08-28: Keynote on 'Explaining the Decisions of Deep Neural Networks and Beyond' at CD-MAKE 2020 (virtual event)
- 2020-07-18: Workshop on 'XXAI: Extending Explainable AI Beyond Deep Models and Classifiers' at ICML 2020 (virtual event)
- 2019-10-27: Talk at ICCV 2019 Workshop on Interpretating and Explaining Visual AI Models, Seoul, Korea
- 2019-07-23: Tutorial on 'Interpretable & Transparent Deep Learning' at EMBC 2019, Berlin, Germany
- 2019-06-16: Talk at CVPR 2019 Workshop on Explainable AI, Long Beach, CA, USA
- 2018-10-12: Keynote at the 2018 International Explainable AI Symposium, Seoul, Korea
- 2018-10-07: Tutorial on 'Interpretable Deep Learning: Towards Understanding & Explaining Deep Neural Networks' at ICIP 2018, Athens, Greece
- 2018-09-16: Tutorial on 'Interpretable Machine Learning' at MICCAI 2018, Granada, Spain
- 2018-06-18: Tutorial on 'Interpreting and Explaining Deep Models in Computer Vision' at CVPR 2018, Salt Lake City, USA
- 2017-12-09: Workshop on 'Interpreting, Explaining and Visualizing Deep Learning' at NIPS 2017, Long Beach, CA
- 2017-12-04: Tutorial on 'Understanding Deep Neural Networks and their Predictions' at WIFS 2017, Rennes, France
- 2017-11-30: Talk at the CoSIP Intense Course on Deep Learning, Berlin, Germany
- 2017-09-12: Tutorial on 'Interpretable Machine Learning' at GCPR 2017, Basel, Switzerland
- 2017-08-28: Class on 'Interpretable ML' at DTU Summer School on Advanced Topics in Machine Learning, Copenhagen, Denmark
- 2017-03-20: Demonstration at CeBIT 2017, Hannover, Germany
- 2017-03-05: Tutorial at ICASSP 2017 on 'Methods for Interpreting and Understanding Deep Neural Networks', New Orleans, USA
- 2016-12-09: Presentation at the NIPS 2016 Workshop on Interpretable ML for Complex Systems, Barcelona, Spain
- 2016-11-24: ACCV 201
Background
- Research interests include explainable artificial intelligence, machine learning, and computer vision. Focuses on developing methods to explain and understand the decisions of deep neural networks.
