Publications

Here is a list of all my publications (excluding conference abstracts). Alternatively, you can also take a lookt at my Google Scholar profile, or search for me on NASA/ADS.

@article{Bonse_2023,
  title         = {{Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise}},
  author        = {Markus J. Bonse and Emily O. Garvin and Timothy D. Gebhard and Felix A. Dannert and Faustine Cantalloube and others},
  year          = 2023,
  eprint        = {2303.12030},
  eprinttype    = {arXiv},
}

@article{Gebhard_2022,
  title         = {{Inferring molecular complexity from mass spectrometry data using machine learning}},
  author        = {Timothy D. Gebhard, Aaron C. Bell, Jian Gong, Jaden J. A. Hastings, G. Matthew Fricke, Nathalie Cabrol, Scott Sandford, Michael Phillips, Kimberley Warren-Rhodes, Atılım Güneş Baydin},
  year          = 2022,
  month         = 12,
  addendum      = {Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2022},
}

@article{Gebhard_2022,
  title         = {{Atmospheric retrievals of exoplanets using learned parameterizations of pressure-temperature profiles}},
  author        = {Timothy D. Gebhard and Daniel Angerhausen and Björn Konrad and Eleonora Alei and Sascha P. Quanz and Bernhard Schölkopf},
  year          = 2022,
  month         = 12,
  addendum      = {Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2022},
}

@article{Gebhard_2022,
  title         = {{Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework}},
  author        = {Timothy D. Gebhard and Markus J. Bonse and Sascha P. Quanz and Bernhard Schölkopf},
  year          = 2022,
  month         = 10,
  doi           = {10.1051/0004-6361/202142529},
  publisher     = {{EDP} Sciences},
  volume        = 666,
  pages         = {A9},
  journal       = {Astronomy \& Astrophysics},
}

@article{Gebhard_2020,
  title         = {{Physically constrained causal noise models for high-contrast imaging of exoplanets}},
  author        = {Timothy D. Gebhard and Markus J. Bonse and Sascha P. Quanz and Bernhard Schölkopf},
  year          = 2020,
  month         = 10,
  eprint        = {2010.05591},
  eprinttype    = {arXiv},
  addendum      = {Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2020},
}

@article{Cuoco_2020,
  title         = {{Enhancing gravitational-wave science with machine learning}},
  author        = {Elena Cuoco and Jade Powell and Marco Cavaglià and Kendall Ackley and Michał Bejger and Chayan Chatterjee and Michael Coughlin and Scott Coughlin and Paul Easter and Reed Essick and Hunter Gabbard and Timothy Gebhard and Shaon Ghosh and Leïla Haegel and Alberto Iess and David Keitel and Zsuzsa Márka and Szabolcs Márka and Filip Morawski and Tri Nguyen and Rich Ormiston and Michael Pürrer and Massimiliano Razzano and Kai Staats and Gabriele Vajente and Daniel Williams},
  year          = 2020,
  month         = 12,
  journal       = {Machine Learning: Science and Technology},
  volume        = 2,
  number        = 1,
  pages         = {011002},
  doi           = {10.1088/2632-2153/abb93a},
}

@article{Gebhard_2019,
  title         = {{Convolutional neural networks: A magic bullet for gravitational-wave detection?}},
  author        = {Timothy D. Gebhard and Niki Kilbertus and Ian Harry and Bernhard Schölkopf},
  year          = 2019,
  month         = 9,
  journal       = {Physical Review D},
  volume        = 100,
  number        = 6,
  doi           = {10.1103/physrevd.100.063015},
  publisher     = {American Physical Society ({APS})},
  url           = {https://doi.org/10.1103/physrevd.100.063015},
}

@article{Gebhard_2017,
  title         = {{ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets}},
  author        = {Timothy Gebhard and Niki Kilbertus and Giambattista Parascandolo and Ian Harry and Bernhard Schölkopf},
  year          = 2017,
  month         = 12,
  booktitle     = {Workshop on Deep Learning for Physical Sciences (DLPS) at the 31st Conference on Neural Information Processing Systems (NeurIPS)},
  url           = {https://dl4physicalsciences.github.io/files/nips_dlps_2017_13.pdf},
}

@article{Ritter_2017,
  title         = {{Software Quality Control at Belle II}},
  author        = {Ritter, Martin and Kuhr, Thomas and Hauth, Thomas and Gebhard, Timothy and Kristof, Michal and Pulvermacher, Christian},
  year          = 2017,
  month         = 10,
  journal       = {Journal of Physics: Conference Series},
  volume        = 898,
  pages         = {072029},
  doi           = {10.1088/1742-6596/898/7/072029},
  publisher     = {{IOP} Publishing},
}

@inproceedings{Gebhard_2015,
  title         = {{Sample Size Estimation for Outlier Detection}},
  author        = {Gebhard, Timothy and Koerte, Inga and Bouix, Sylvain},
  year          = 2015,
  pages         = {743--750},
  doi           = {10.1007/978-3-319-24574-4_89},
  booktitle     = {Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015},
  publisher     = {Springer International Publishing},
  editor        = {Navab, Nassir and Hornegger, Joachim and Wells, William M. and Frangi, Alejandro F.},
  isbn          = {978-3-319-24574-4},
}