Jordan T. Ash
ash.jordan -at- microsoft.com
I'm a postdoctoral researcher at Microsoft Research in New York City, where I mostly think about problems related to deep learning and sequential decision making. I earned my PhD from the computer science department at Princeton University and worked under the advisorship of Ryan P. Adams.
A data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformation
Tianju Xue, Alex Beatson, Maurizio Chiaramonte, Geoffrey Roeder, Jordan T. Ash, Yigit Menguc, Sigrid Adriaenssens, Ryan P. Adams, and Sheng Mao. Soft Matter, 2020.
Learning composable energy surrogates for PDE order reduction
Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xie, and Ryan P. Adams.
On the difficulty of warm-starting neural network training
Jordan T. Ash and Ryan P. Adams, 2019.
End-to-end training of deep probabilistic CCA for joint modeling of paired biomedical observations
Gregory Gundersen, Bianca Dumitrascu, Jordan T. Ash, and Barbara E. Engelhardt. UAI, 2019.
Joint analysis of gene expression levels and histological images identifies genes associated with tissue morphology
Jordan T. Ash, Gregory Darnell, Daniel Munro, and Barbara E. Engelhardt. Nature Communications, 2019.
Unsupervised domain adaptation using approximate label matching
Jordan T. Ash, Rob Schapire, and Barbara E. Engelhardt. ICML workshop on implicit generative models, 2017.
Automated particle picking for low-contrast macromolecules in cryo-electron microscopy
Robert Langlois, Jesper Pallesen, Jordan T. Ash, Danny Nam Ho, John L. Rubinstein, and Joachim Frank. Journal of structural biology, 2014.
Fully automated particle selection and verification in single-particle cryo-EM
Robert Langlois, Jordan T. Ash, Jesper Pallesen, and Joachim Frank. Journal of structural biology. Computational Methods for Three-Dimensional Microscopy Reconstruction, Springer, 2014.