Here is a list of papers that I have worked on:

  1. Deep Learning Interpretation: Flip Points and Homotopy Methods, Mathematical and Scientific Machine Learning Conference, Princeton University, 2020.
  2. Auditing and Debugging Deep Learning Models via Decision Boundaries: Individual-level and Group-level Analysis, to be published in La Matematica, Official Journal of the Association for Women in Mathematics, 2021.
  3. Debugging Trained Machine Learning Models Using Flip Points, ICLR Workshop on Debugging Trained Machine Learning Models, 2019.
  4. Learning Diverse Gaussian Graphical Models and Interpreting Edges, SIAM International Conference on Data Mining, 2019.
  5. Deep Learning Generalization and the Convex Hull of Training Sets, Deep Learning through Information Geometry Workshop, NeurIPS, 2020.
  6. Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets
  7. A Homotopy Algorithm for Optimal Transport, 12th Annual Workshop on Optimization for Machine Learning, NeurIPS, 2020.
  8. Interpretable Insights about Medical Image Datasets: Using Wavelets and Spectral Methods, ICML Workshop on Human Interpretability in Machine Learning, 2020.
  9. Social Fairness, Accountability and Transparency of the Data Economy: Using Machine Learning to Combat the Emptiness of Privacy Policies, ICML Workshop on Law & Machine Learning, 2020.
  10. A Sketching Method for Finding the Closest Point on a Convex Hull, work in progress, 2021.
  11. Federated Learning without Revealing the Decision Boundaries, work in progress, 2021.
  12. Using Wavelets to Analyze Similarities in Image-Classification Datasets
  13. Investigating Decision Boundaries of Trained Neural Networks
  14. Refining the Structure of Neural Networks Using Matrix Conditioning
  15. Optimizing Real-time Decisions in Hierarchical Humanitarian Aid Delivery Systems
  16. A Probabilistic Framework and a Homotopy Method for Real-time Hierarchical Freight Dispatch Decisions

Web Analytics Made Easy -