Jessica Zosa Forde

I’m a machine learning researcher focusing on the empirical study of deep learning models to improve their reliability in high stakes domains such as healthcare. At Brown University, I work with my PhD advisor, Michael Littman, studying the inductive bias of overparameterized models. For the past two summers, I collaborated with Michela Paganini at Facebook AI Research on model pruning. Prior to starting my PhD, I was an active core maintainer at Project Jupyter, which maintains open source projects such as the Jupyter Notebook. I also worked as a Data Scientist, collaborating with colleagues at organizations such as McKinsey and DARPA.

I believe that the open science movement is important for improving transparency and accountability in machine learning. At Project Jupyter I co-maintained reproducibility tools such as binder and repo2docker. I also am a co-organizer of the Machine Learning Reproducibility Challenge and Machine Learning Retrospectives.

Email me at jessica_forde at brown.edu.

Fun Fact: My Project Jupyter code is in the GitHub Arctic Code Vault, 250 meters underground in a former coal mine in Svalbard, Norway.

Selected Publications

  1. Cooper AF, Lu Y, Forde JZ, De Sa C. Hyperparameter Optimization Is Deceiving Us, and How to Stop It. NeurIPS. 2021.
  2. Forde JZ*, Cooper AF*, Kwegyir-Aggrey K, De Sa C, Littman M. Model Selection’s Disparate Impact in Real-World Deep Learning Applications. Science and Engineering of Deep Learning Workshop, ICLR 2021. Contributed Talk.
  3. Paganini M, Forde JZ. Bespoke vs. Prêt-à-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding. 2020.
  4. Paganini M, Forde JZ. dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration. 2020.
  5. Paganini M, Forde J. Streamlining Tensor and Network Pruning in PyTorch. ML for Developing Countries Workshop, ICLR 2020. Contributed Talk.
  6. Paganini M, Forde J. On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks. ML for Developing Countries Workshop, ICLR 2020.
  7. Zech JR, Forde JZ, Littman ML. Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging. ML for Healthcare Workshop, NeurIPS 2019.
  8. Forde JZ, Paganini M. The scientific method in the science of machine learning. Debugging Machine Learning Models Workshop, ICLR 2019. Contributed Talk
  9. Forde J, Bussonnier M, Fortin F-A, Granger B, Head T, Holdgraf C, et al. Reproducing Machine Learning Research on Binder. 2018.
  10. Project Jupyter, M. Bussonnier, J. Forde, J. Freeman, B. Granger, T. Head, C. Holdgraf, K. Kelley, G. Nalvarte, A. Osheroff,M. Pacer, Y. Panda, F. Perez, B. Ragan-Kelley, and C. Willing. Binder 2.0-Reproducible, interactive, sharable environments for science at scale. Scipy. 2018.