About me

I am an Assistant Professor in University of Wisconsin-Madison.

My research focuses on reliable machine learning and the design and study of expressive models that are robust to noise and generalize well in out-of-distribution data. Concretely,
  • I have worked extensively on polynomial networks (PNs) that capture high-degree interactions between inputs. My short-term goals are to understand the inductive bias and properties of existing architectures through empirical and theoretical studies. I am interested in the complete theoretical understanding of (neural/polynomial) networks, including their expressivity, trainability, generalization properties, and inductive biases. My recent work has provided the first characterization of the generalization of this class of functions, and I have focused on the spectral bias of high-degree polynomials, highlighting how PNs can learn higher frequency functions faster than regular feed-forward networks.
  • My goal is to understand the extrapolation properties of existing networks and make improvements to their performance, especially in the context of conditional generative models. In the short-term, I will continue to explore the robustness of these models to malicious attacks, as well as the impact of adversarial perturbations on different classes. In the long-term, I plan to design models that are both robust and fair, and can generalize well to unseen attribute combinations.