Research

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🔎 My research focuses on how we can build trustworthy machine-learning systems by making them interpretable. In my work, interpretability is grounded seriously via close collaboration with domain experts, e.g. medical doctors or cell biologists. These collaborations have given rise to useful methodology, roughly split into two areas: (1) building more effective transparent models and (2) improving the trustworthiness of black-box models. Going forward, I hope to help bridge the gap between transparent models and black-box models to improve real-world healthcare.

🌳 Whenever possible, building transparent models is the most effective route towards ensuring interpretability. Transparent models are interpretable by design, including models such as (concise) decision trees, rule lists, and linear models. My work in this area was largely motivated by the problem of clinical decision-rule development. Clinical decision rules (especially those used in emergency medicine), need to be extremely transparent so they can be readily audited and used by physicians making split-second decisions. To this end, we have developed methodology for enhancing decision trees. For example, replacing the standard CART algorithm with a novel greedy algorithm for tree-sums can substantially improve predictive performance without sacrificing predictive performance. Additionally, hierarchical regularization can improve the predictions of an already fitted model without altering its interpretability. Despite their effectiveness, transparent models such as these often get overlooked in favor of black-box models; to address this issue, we've spent a lot of time curating imodels, an open-source package for fitting state-of-the-art transparent models.

🌀 My second line of my work focuses on interpreting and improving black-box models, such as neural networks, for the cases when a transparent model simply can't predict well enough. Here, I work closely on real-world problems such as analyzing imaging data from cell biology and cosmology. Interpretability in these contexts demands more nuanced information than standard notions of "feature importance" common in the literature. As a result, we have developed methods to characterize and summarize the interactions in a neural network, particularly in transformed domains (such as the Fourier domain), where domain interpretations can be more natural. I'm particularly interested in how we can ensure that these interpretations are useful, either by using them to embed prior knowledge into a model or identify when it can be trusted.

🤝 There is a lot more work to do on bridging the gap between transparent models and black-box models in the real world. One promising avenue is distillation, whereby we can use a black-box model to build a better transparent model. For example, in one work we were able to distill state-of-the-art neural networks in cell-biology and cosmology into transparent wavelet models with <40 parameters. Despite this huge size reduction, these models actually improve prediction performance. By incorporating close domain knowledge into models and the way we approach problems, I believe interpretability can help unlock many benefits of machine-learning for improving healthcare and science.

resources + posts

imodels python package

intro-ml slides (berkeley cs 189)

ml-ai comprehensive guide

interpretability cheat sheet

experience

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  Fall 22 - Present

  msr (deep learning lab)

  large language models and explanations

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  Summer 21 - Summer 22

  paige ai (ai scientist)

  developed deep learning models for pathology

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  Fall 17 - spring 22

  berkeley (bin yu lab)

  phd research in interpretable ml

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  Summer 20

  amazon research (pietro perona lab)

  developed methods for causal benchmarking of fairness in computer vision models

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  Summer 19

  pacmed ai (healthcare interpretability)

  developed methods for interpreting medical machine-learning models

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  Summer 17

  meta ai (computer vision)

  Worked on unsupervised pretraining of CNNs for semantic segmentation

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  Fall 16 - Spring 17

  uva (Yanjun Qi Lab)

  Contributed to development of novel weighted-L1, multi-task Gaussian graphical models

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  Summer 14, 15, 16

  hhmi (Srini Turaga Lab)

  Improved CNNs for neural-image segementation + explored dendritic backpropagation

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  Fall 14 - Fall 16

  uva (William Levy Lab)

  Simulated computational models of stochastic neurons