I am a PhD candidate in the ECE department at Northeastern University and part of Dr. Jennifer Dy’s Machine Learning Lab at the SPIRAL research center. I’m broadly interested in improving transparency in black-box prediction models, specifically in relation to interpretability and uncertainty quantification.

I collaborate with the Channing Division of Network Medicine in applying machine learning methods to various challenges related to Chronic Obstructive Pulmonary Disease (COPD). COPD is a lung disease commonly associated with smoking and long-term exposure to lung irritants. We work with multiomics, spirometry data, and lung imaging to improve subtyping, disease progression, diagnosis, etc.


Research Interests

  • Black-Box interpretability, especially feature attribution and feature selection methods.
  • Uncertainty Quantification in Neural Networks
  • Variational Inference and Normalizing Flows
  • Geometric Deep Learning



Aria Masoomi, Davin Hill, Zhonghui Xu, Craig P. Hersh, Edwin K. Silverman, Peter J. Castaldi, Stratis Ioannidis, & Jennifer Dy. Explanations of Black-Box Models based on Directional Feature Interactions. Accepted at ICLR 2022 as a spotlight presentation (~5% of submissions). [Paper] [Poster] [Code]


Davin Hill, Max Torop, Aria Masoomi, Peter J. Castaldi, Michael H. Cho, Jennifer Dy, & Brian D. Hobbs. Deep Learning Utilizing Discarded Spirometry Data to Improve Lung Function and Mortality Prediction in the UK Biobank. Abstract. Accepted for oral presentation at ATS 2022 (~5% of submissions). [Poster]


Zulqarnain Khan, Aria Masoomi, Davin Hill, & Jennifer Dy. Analyzing the Effects of Classifier Lipschitzness on Explainers. Manuscript in preparation. [Preprint]