Seminar Series

The Applied Biostatistics Seminar Series consists of a series of talks with the primary purpose of furthering statistical knowledge on an applied level. Talks will focus on advances in biostatistical methods and statistical programming techniques and their translation into addressing biomedical research questions. The seminars are open to all members of the Duke community, but primarily geared toward applied statistical researchers.

New Advances in (Adversarially) Robust and Secure Machine Learning

Hongyang Zhang, PhD
Wednesday, March 31, 2021 - 02:00 at Zoom Conference

Abstract: Deep learning models are often vulnerable to adversarial examples. In this talk, we will focus on robustness and security of machine learning against adversarial examples. There are two types of defenses against such attacks: 1) empirical and 2) certified adversarial robustness. In the first part of the talk, we...
Read More

Population and medical genetic inference using Biobank-scale statistical methods

Tuesday, March 30, 2021 - 01:00 at Zoom Conference

Abstract: The quest to understand the interplay between evolution, genes and traits has been revolutionized by the collection of rich phenotypic and genetic data across hundreds of thousands of individuals in diverse populations. However analyses of these Biobank-scale datasets present substantial statistical and computational challenges. I will describe how we...
Read More

Principled Use of Real World Data in Clniical Trials

Tuesday, March 23, 2021 - 03:00 at Zoom Conference

Abstract: The gold standard for evidence generation in support of medical product evaluation and clinical decision-making has long been the randomized clinical trial (RCT). Recently, there is growing interest in the role of real world data (RWD) for evidence generation. One area of opportunity is integration of RCTs into routine...
Read More

Statistical modeling and inference in GWAS summary data based Mendelian Randomization analysis

Baolin Wu, PhD
Monday, March 22, 2021 - 11:00 at Zoom Conference

Abstract: By leveraging publicly available summary statistics from many large-scale genome-wide association studies (GWAS), Mendelian randomization (MR) has become a popular and cost-effective approach to exploiting genetic variation to study the potential causal effect of various risk factors on health outcomes in the presence of unmeasured confounding. However, due to...
Read More

Computational Regulatory Genomics

SAURABH SINHA, PhD
Friday, March 19, 2021 - 01:00 at Zoom Conference

Abstract: Gene regulation is central to an extraordinary range of biological phenomena from development to disease, as well as the evolution of diverse life forms. My group’s research develops and uses computational tools, based on probabilistic inference, machine learning, and biophysics-inspired models, to answer unsolved and topical questions related to...
Read More

Pages