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.

Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise

Markus J. Bonse, Emily O. Garvin, Timothy D. Gebhard, Felix A. Dannert, Faustine Cantalloube, Gabriele Cugno, Olivier Absil, Jean Hayoz, Julien Milli, Markus Kasper, Sascha P. Quanz

Under Review,

Preprint BibTeX

Cite this paper

@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},
}
NASA/ADS Code

Inferring molecular complexity from mass spectrometry data using machine learning

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

Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2022,

PDF BibTeX

Cite this paper

@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},
}
Poster

Atmospheric retrievals of exoplanets using learned parameterizations of pressure-temperature profiles

Timothy D. Gebhard, Daniel Angerhausen, Björn Konrad, Eleonora Alei, Sascha P. Quanz, Bernhard Schölkopf

Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2022,

PDF BibTeX

Cite this paper

@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},
}
Poster

Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework

Timothy D. Gebhard, Markus J. Bonse, Sascha P. Quanz, Bernhard Schölkopf

Astronomy & Astrophysics, 666 (A9),

Preprint PDF BibTeX

Cite this paper

@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},
}
NASA/ADS Code Dataset DOI

Physically constrained causal noise models for high-contrast imaging of exoplanets

Timothy D. Gebhard, Markus J. Bonse, Sascha P. Quanz, Bernhard Schölkopf

Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2020,

Preprint BibTeX

Cite this paper

@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},
}
NASA/ADS

Enhancing Gravitational-Wave Science with Machine Learning

Elena Cuoco, Jade Powell, Marco Cavaglià, Kendall Ackley, Michał Bejger, Chayan Chatterjee, Michael Coughlin, Scott Coughlin, Paul Easter, Reed Essick, Hunter Gabbard, Timothy Gebhard, Shaon Ghosh, Leïla Haegel, Alberto Iess, David Keitel, Zsuzsa Márka, Szabolcs Márka, Filip Morawski, Tri Nguyen, Rich Ormiston, Michael Puerrer, Massimiliano Razzano, Kai Staats, Gabriele Vajente, Daniel Williams

Machine Learning: Science and Technology, 2 (1),

Preprint PDF BibTeX

Cite this paper

@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},
}
NASA/ADS DOI

Convolutional neural networks: A magic bullet for gravitational-wave detection?

Timothy D. Gebhard*, Niki Kilbertus*, Ian Harry, Bernhard Schölkopf

Physical Review D, 100 (6),

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@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},
}
NASA/ADS Code Dataset DOI

ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets

Timothy Gebhard*, Niki Kilbertus*, Giambattista Parascandolo, Ian Harry, Bernhard Schölkopf

Accepted at the Deep Learning for Physical Sciences workshop at NeurIPS 2017,

PDF BibTeX

Cite this paper

@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},
}
Code Poster

Software Quality Control at Belle II

Martin Ritter, Thomas Kuhr, Thomas Hauth, Timothy Gebhard, Michal Kristof, Christian Pulvermacher

Journal of Physics: Conference Series, Volume 898,

PDF BibTeX

Cite this paper

@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},
}
NASA/ADS DOI

Sample Size Estimation for Outlier Detection

Timothy Gebhard, Inga Koerte, Sylvain Bouix

18th International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI 2015),

PDF BibTeX

Cite this paper

@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},
}
Poster DOI Oral presentation