Publications
Most are also listed on Google Scholar.
* Denotes co-first author.
Conference and Journal Publications
Benchmarking of Machine Learning Ocean Subgrid Parameterizations in an Idealized Model
Tackling Climate Change with Machine Learning
Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement
Evaluating the Interpretability of Generative Models by Interactive Reconstruction
Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models
Design Continuums and the Path Towards Self-Designing Key-Value Stores that Know and Learn
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Hydrodynamic Irreversibility in Particle Suspensions with Non-Uniform Strain
Theses
Right for the Right Reasons: Training Neural Networks to be Interpretable, Robust, and Consistent with Expert Knowledge
Training Machine Learning Models by Regularizing their Explanations
The Compression and Concentration of Classical and Quantum Information
Senior Thesis, Haverford College, 2011
[pdf]
Workshop Papers
Behavioral Experiments for Gathering Labeled Animal Vocalization Data
Controlled Direct Effect Priors for Bayesian Neural Networks
Refactoring Machine Learning
NeurIPS Workshop on Critiquing and Correcting Trends in Machine Learning, 2018
[pdf]
Learning Qualitatively Diverse and Interpretable Rules for Classification
The Neural LASSO: Local Linear Sparsity for Interpretable Explanations
NeurIPS Workshop on on Transparent and Interpretable Machine Learning in Safety Critical Environments, 2017
[pdf]