Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans
Lambert Leong, Michael C. Wong, Yong E. Liu, Yannik Glaser, Brandon K. Quon, Nisa N. Kelly, Devon Cataldi, Peter Sadowski, Steven B. Heymsfield, and John A. Shepherd
@article{leong2024generative,title={Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using {3D} surface scans},author={Leong, Lambert and Wong, Michael C. and Liu, Yong E. and Glaser, Yannik and Quon, Brandon K. and Kelly, Nisa N. and Cataldi, Devon and Sadowski, Peter and Heymsfield, Steven B. and Shepherd, John A.},journal={Communications Medicine},year={2024},volume={4},issue={1},number={13},doi={10.1038/s43856-024-00434-w},}
2023
Diffusion Models for High-Resolution Solar Forecasts
Yusuke Hatanaka, Yannik Glaser, Geoff Galgon, Giuseppe Torri, and Peter Sadowski
@article{hatanaka2023diffusion,title={Diffusion Models for High-Resolution Solar Forecasts},author={Hatanaka, Yusuke and Glaser, Yannik and Galgon, Geoff and Torri, Giuseppe and Sadowski, Peter},year={2023},url={arXiv:2302.00170},}
2022
Self-supervised detection of atmospheric phenomena from remotely sensed synthetic aperture radar imagery
Yannik Glaser, Peter Sadowski, and Justin E. Stopa
In NeurIPS Workshop on Machine Learning in the Physical Sciences, 2022
@inproceedings{glaser2022self,title={Self-supervised detection of atmospheric phenomena from remotely sensed synthetic aperture radar imagery},author={Glaser, Yannik and Sadowski, Peter and Stopa, Justin E.},booktitle={NeurIPS Workshop on Machine Learning in the Physical Sciences},year={2022},}
Deep learning predicts all-cause mortality from longitudinal total-body DXA imaging
Yannik Glaser, John Shepherd, Lambert Leong, Thomas Wolfgruber, Li-Yung Lui, Peter Sadowski, and Steve Cummings
@article{glaser2022deep,title={Deep learning predicts all-cause mortality from longitudinal total-body {DXA} imaging},author={Glaser, Yannik and Shepherd, John and Leong, Lambert and Wolfgruber, Thomas and Lui, Li-Yung and Sadowski, Peter and Cummings, Steve},journal={Communications Medicine},year={2022},doi={10.1038/s43856-022-00166-9},volume={2},number={102},}
Group Equivariant Neural Networks for Spectropolarimetric Inversions in Solar Astronomy
Michael Ito, Ian Cunnyngham, Xudong Sun, and Peter Sadowski
In International Conference on Learning Representations Workshop on AI for Earth and Space Sciences, 2022
@inproceedings{ito2022group,title={Group Equivariant Neural Networks for Spectropolarimetric Inversions in Solar Astronomy},author={Ito, Michael and Cunnyngham, Ian and Sun, Xudong and Sadowski, Peter},booktitle={International Conference on Learning Representations Workshop on AI for Earth and Space Sciences},year={2022},}
A ridge-to-reef ecosystem microbial census reveals environmental reservoirs for animal and plant microbiomes
Anthony Amend, Sean Swift, Mahdi Belcaid, Nicolas Centraro, John Darcy, Kiana Frank, Kauaoa Fraiola, Terrance McDermott, Margaret McFall-Ngai, Camilo Mora, Matthew Medeiros, Kirsten Nakayama, Craig Nelson, Nhu Nguyen, Randi Rollins, Peter Sadowski, Wesley Sparagon, Melisandre Tefit, Joanne Yew, Nicole Hynson, Joshua Buchanan, Danyel Yogi, and Kacie Kajihara
Proceedings of the National Academy of Sciences, 2022
@article{amend2022ridge,title={A ridge-to-reef ecosystem microbial census reveals environmental reservoirs for animal and plant microbiomes},author={Amend, Anthony and Swift, Sean and Belcaid, Mahdi and Centraro, Nicolas and Darcy, John and Frank, Kiana and Fraiola, Kauaoa and McDermott, Terrance and McFall-Ngai, Margaret and Mora, Camilo and Medeiros, Matthew and Nakayama, Kirsten and Nelson, Craig and Nguyen, Nhu and Rollins, Randi and Sadowski, Peter and Sparagon, Wesley and Tefit, Melisandre and Yew, Joanne and Hynson, Nicole and Buchanan, Joshua and Yogi, Danyel and Kajihara, Kacie},journal={Proceedings of the National Academy of Sciences},volume={119},number={33},pages={e2204146119},year={2022},doi={10.1073/pnas.2204146119},}
Recovery of TESS Stellar Rotation Periods Using Deep Learning
Zachary R Claytor, Jennifer L van Saders, Joe Llama, Peter Sadowski, Brandon Quach, and Ellis A Avallone
@article{claytor2022recovery,title={Recovery of {TESS} Stellar Rotation Periods Using Deep Learning},author={Claytor, Zachary R and {van Saders}, Jennifer L and Llama, Joe and Sadowski, Peter and Quach, Brandon and Avallone, Ellis A},journal={The Astrophysical Journal},volume={927},number={2},pages={219},year={2022},publisher={IOP Publishing},}
Deep Learning from Four Vectors
Pierre Baldi, Peter Sadowski, and Daniel Whiteson
In Artificial Intelligence For High Energy Physics, 2022
@incollection{baldi2022deep,title={Deep Learning from Four Vectors},author={Baldi, Pierre and Sadowski, Peter and Whiteson, Daniel},booktitle={Artificial Intelligence For High Energy Physics},chapter={3},pages={59--83},year={2022},publisher={World Scientific Publishing},}
2021
Evolution Informed Neural Networks for Microbiome Data Analysis
Michael Ito, Yannik Glaser, and Peter Sadowski
In 2021 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2021), 2021
@inproceedings{ito2021evolution,title={Evolution Informed Neural Networks for Microbiome Data Analysis},author={Ito, Michael and Glaser, Yannik and Sadowski, Peter},year={2021},booktitle={2021 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2021)},pages={3386-3391},doi={10.1109/BIBM52615.2021.9669640},organization={IEEE},}
2020
Sherpa: Robust hyperparameter optimization for machine learning
Lars Hertel, Julian Collado, Peter Sadowski, Jordan Ott, and Pierre Baldi
@article{hertel2020sherpa,title={Sherpa: Robust hyperparameter optimization for machine learning},author={Hertel, Lars and Collado, Julian and Sadowski, Peter and Ott, Jordan and Baldi, Pierre},journal={SoftwareX},volume={12},pages={100591},year={2020},issn={2352-7110},publisher={Elsevier},doi={https://doi.org/10.1016/j.softx.2020.100591},}
2019
2018
Learning in the machine: Recirculation is random backpropagation
@article{baldi2018recirculation,title={Learning in the machine: Recirculation is random backpropagation},author={Baldi, Pierre and Sadowski, Peter},journal={Neural Networks},volume={108},pages={479--494},year={2018},publisher={Elsevier},}
