Jordan T. Ash

jordanta -at-

I'm a PhD student in the computer science department at Princeton University working under the advisorship of Ryan P. Adams. I'm interested in building practical, robust machine learning algorithms and using them to benefit scientific discovery, engineering design, and human creativity.


Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. ICLR, 2020 (Talk).

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.
paper code

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.
paper code

Learning deep resnet blocks sequentially using boosting theory
Furong Huang, Jordan T. Ash, John Langford, and Rob Schapire. ICML, 2018.
paper code

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.
paper code

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.
book chapter