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Bernhard Schölkopf

University of Tübingen · DE
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Area of research
Artificial Intelligence · Computer Vision and Pattern Recognition
Research interest
Research interests include Computer science, Artificial intelligence, Physics, Data science, Positron emission tomography, and Medicine.
h-index
citations
2,143
works
14
NIH funding
primary concept
email

Recent publications

Artificial intelligence for modelling infectious disease epidemics
Nature 2025cited by 123position: middledoi
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration<sup>0</sup>
2024cited by 115position: middledoi
Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET–MRI data
Nature Biomedical Engineering 2023cited by 13position: middledoi
Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good
2023cited by 4position: middledoi
A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions
Scientific Data 2022cited by 241position: middledoi
The unpopular Package: A Data-driven Approach to Detrending TESS Full-frame Image Light Curves
The Astronomical Journal 2022cited by 39position: lastdoi
Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies
Scientific Reports 2022cited by 16position: middledoi
From Variational to Deterministic Autoencoders
International Conference on Learning Representations 2020cited by 74position: last
Inferring causation from time series in Earth system sciences
Nature Communications 2019cited by 923position: middledoi
THE POPULATION OF LONG-PERIOD TRANSITING EXOPLANETS
The Astronomical Journal 2016cited by 98position: lastdoi
Modeling confounding by half-sibling regression
Proceedings of the National Academy of Sciences 2016cited by 67position: firstdoi
A SYSTEMATIC SEARCH FOR TRANSITING PLANETS IN THE<i>K2</i>DATA
The Astrophysical Journal 2015cited by 160position: lastdoi
Assessing attention and cognitive function in completely locked-in state with event-related brain potentials and epidural electrocorticography
Journal of Neural Engineering 2014cited by 34position: middledoi
On the empirical estimation of integral probability metrics
Electronic Journal of Statistics 2012cited by 236position: middledoi

Grants

No grants ingested yet.

Frequent collaborators

David W. Hogg · New York University4 papers (2015–2022)Daniel Foreman-Mackey · Flatiron Health (United States)4 papers (2015–2022)Sergios Gatidis · Artificial Intelligence in Medicine (Canada)3 papers (2022–2023)Timothy D. Morton · Princeton University2 papers (2015–2016)Benjamin T. Montet · UNSW Sydney2 papers (2015–2022)Dun Wang · New York University2 papers (2015–2016)Jonas Peters · ETH Zurich2 papers (2016–2019)Christian la Fougère · German Cancer Research Center2 papers (2022–2023)Thomas Küstner · Charité - Universitätsmedizin Berlin2 papers (2022–2022)Marten Scheffer · Wageningen University & Research1 papers (2019–2019)Philipp Mayer · Heidelberg University1 papers (2022–2022)Niels Birbaumer · University of Tübingen1 papers (2014–2014) · 1 papers (2022–2022)Egbert H. van Nes · Arizona State University1 papers (2019–2019)Wenjia Bai · Imperial College London1 papers (2022–2022)Tobias Pischon · German Cancer Research Center1 papers (2022–2022)Ruth Angus · Columbia University1 papers (2022–2022)Michael J. Black · Brown University1 papers (2020–2020)Michael Bensch · University of Tübingen1 papers (2014–2014)Antonio Vergari · Edinburgh College1 papers (2020–2020)
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