I am a fifth-year PhD student in Carnegie Mellon Univiersity's Machine Learning Department advised by Ameet Talwalkar. My research focuses on the theory and applications of explainable machine learning, with a focus on understanding powerful black-box models such as neural networks and random forests.

Recent Updates

  • March 2021: We released a white paper on the need to ground methods from interpretable machine learning in real use cases.
  • Jan 2021: Our paper on the connections between interpretable machine learning and learning theory through the lens of local approximation explanation fidelity was accepted at ICLR 2021.
  • October 2020: ExpO was accepted at NeurIPS 2020.
  • July 2020: ELDR was presented at ICML 2020.