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. In particular, I will cover two main topics: (1) a statistical approach to assess gene set enrichments using genetic and transcriptomic data; (2) multimodal latent topic model for mining electronic healthcare and whole genome sequencing data from small patient cohort.

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. We propose two estimation procedures by computing the likelihood contribution of both uncensored and censored observations. For variable selection problem, we introduce a unified penalized estimating function and use an optimization algorithm to solve it. We discuss the asymptotic properties of the resulting penalized estimators. The work is motivated by the International stroke Trial dataset collected in Argentina in which the survival times of about 88% of the 545 cases are censored.