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 was advised by Ryan P. Adams.
Investigating the role of negatives in contrastive representation learning
Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, and Dipendra Misra.
Gone fishing: neural active learning with Fisher embeddings
Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, and Sham Kakade.
Learning composable energy surrogates for PDE order reduction
Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xie, and Ryan P. Adams. NeurIPS 2020 (Talk).
On warm-starting neural network training
Jordan T. Ash and Ryan P. Adams. NeurIPS 2020.
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.
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 2020.
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. Computational Methods for Three-Dimensional Microscopy Reconstruction, Springer 2014.