2019 Fall

Date: 2019-09-20 Time: 15:30-16:30 Location: BURN 1205 Abstract: Symmetry has played a significant role in modern physics, in part by constraining the physical laws. I will discuss how it could play a fundamental role in AI by constraining the deep model design. In particular, I focus on discrete domain symmetries and through examples show how we can use this inductive bias as a principled means for constraining a feedforward layer and significantly improving its sample efficiency.

Date: 2019-09-13 Time: 15:30-16:30 Location: BURN 1205 Abstract: In the current era of multi-omics and digital healthcare, we are facing unprecedented amount of data with tremendous opportunities to link molecular phenotypes with complex diseases. However, the lack of integrative statistical method hinders system-level interrogation of relevant disease-related pathways and the genetic implication in various healthcare outcome. In this talk, I will present our current progress in mining genomics and healthcare data.

Date: 2019-09-06 Time: 15:30-16:30 Location: BURN 1205 Abstract: In this talk, we discuss two problems of semiparametric estimation and variable selection for length-biased data with heavy censoring. The common feature of the proposed estimation procedures in the literature is that they only put probability mass on failure times. Under length-biased sampling, however, censoring is informative and failing to incorporate censored observations into estimation can lead to a substantial loss of efficiency.