Current Bio3 Seminar Series

The Bio3 Seminar Series meets every second and fourth Friday of the month, during the academic year.

*MS and PhD biostatistics students are expected to attend the bi-weekly seminar series, as part of their academic curriculum.*


Seminar Schedule – Spring 2021:

Friday, April 23, 2021 at 10:00 am

Manisha Sengupta, Ph.D. 
Acting Chief, Long-Term Care Statistics Branch National Center for Health Statistics (NCHS), MD

Abstract: In 2018, there were 52.4 million Americans aged 65 years and older and represented 16% of the population.  By 2040, it is projected that there will be about 80.8 million people in this age group.  The 85 and older population is projected to more than double from 6.5 million in 2018 to 14.4 million in 2040.  Although people of all ages may need post-acute or long-term care services, the risk of needing these services increases with age.  

The National Post-acute and Long-term Care study monitors trends in the supply, provision, and use of the major sectors of paid, regulated long-term care services. NPALS uses survey data on the residential care community and adult day services sectors, and administrative data on the home health, nursing home, hospice, inpatient rehabilitation, and long-term care hospital sectors.  This presentation will describe the study, methodological issues, some findings from the study, and a brief discussion of future possibilities.

Location: Online via Zoom

Tuesday, April 13, 2021 at 10:00 am

Xue Xiao, Ph.D. 
Bioinformatician, UT Southwestern Medical Center, TX

Abstract: Deep neural network architectures such as CNN, LSTM, GCN, have become increasingly popular as machine learning tools during the recent years. Meanwhile, nowadays the high-throughput sequencing technology has made DNA and protein sequence data to grow at an explosive rate, which has also driven the study of biological sequences in the wave of big data. Deep learning is a powerful technique for analyzing largescale data and learns spontaneously to gain knowledge. It has been widely used in biological sequence data analysis and obtained a lot of research achievements. In this presentation, Dr. Xiao will first explain some basic concepts of deep neural network, followed by the application of deep learning for biological sequences.

Location: Online via Zoom

Friday, April 9, 2021 at 10:00 am

Xiaofei Wang, Ph.D. 
Professor, Department of Biostatistics and Bioinformatics at Duke University, NC

Abstract: In this talk, we exploit the complementing features of randomized clinical trials (RCT) and real world evidence (RWE) to estimate the average treatment effect of the target population. We will review existing methods in conducting integrated analysis of the data from RCTs and RWEs. We will then discuss in detail new calibration weighting estimators that are able to calibrate the covariate information between RCTs and RWEs. We will briefly review asymptotic results under mild regularity conditions, and confirm the finite sample performances of the proposed estimators by simulation experiments. In a comparison of existing methods, we illustrate our proposed methods to estimate the effect of adjuvant chemotherapy in early-stage resected non–small-cell lung cancer integrating data from a RCT and a sample from the National Cancer Database.

Location: Online via Zoom

Friday, March 26, 2021 at 10:00 am

Pang Du, Ph.D.
Associate Professor, Department of Statistics at Virginia Tech, VA

Abstract: Literature on change point analysis mostly requires a sudden change in the data distribution, either in a few parameters or the distribution as a whole. We are interested in the scenario where the variance of data may make a significant jump while the mean changes in a smooth fashion. The motivation is a liver procurement experiment monitoring organ surface temperature. Blindly applying the existing methods to the example can yield erroneous change point estimates since the smoothly-changing mean violates the sudden-change assumption. We propose a penalized weighted least squares approach with an iterative estimation procedure that integrates variance change point detection and smooth mean function estimation. The procedure starts with a consistent initial mean estimate ignoring the variance heterogeneity. Given the variance components the mean function is estimated by smoothing splines as the minimizer of the penalized weighted least squares. Given the mean function, we propose a likelihood ratio test statistic for identifying the variance change point. The null distribution of the test statistic is derived together with the rates of convergence of all the parameter estimates. Simulations show excellent performance of the proposed method. Application analysis offers numerical support to non-invasive organ viability assessment by surface temperature monitoring. Extension to functional variance change point detection will also be presented if time allows.

Location: Online via Zoom

Friday, March 12, 2021 at 10:00 am

Michael G. Hudgens, Ph.D.
Professor, Department of Biostatistics; Director, Center for AIDS Research (CFAR) Biostatistics Core at the University of North Carolina, Chapel Hill, NC

Abstract: A fundamental assumption usually made in causal inference is that of no interference between individuals (or units), i.e., the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in infectious diseases, whether one person becomes infected may depend on who else in the population is vaccinated. In this talk we will discuss recent approaches to assessing treatment effects in the presence of interference.

Location: Online via Zoom

Friday, February 26, 2021 at 10:00 am

Yimei Li, Ph.D. 
Assistant Professor of Biostatistics, Department of Biostatistics, Epidemiology and Informatics at the University of Pennsylvania, PA

Abstract: Phase I oncology trials aim to identify the optimal dose that will be recommended for phase II trials, but the standard designs are inefficient and inflexible. In this talk, I will introduce two innovative Bayesian adaptive designs we developed to improve the efficiency of the trial or incorporate the complex features of the trial. In the first example, we propose PA-CRM, a design for pediatric phase I trials when concurrent adult trials are being conducted. The design automatically and adaptively incorporate information from the concurrent adult trial into the ongoing pediatric trial, and thus greatly improves the efficiency of the pediatric trial. In the second example, we propose a design for early phase platform trials, where multiple doses of multiple targeted therapies are evaluated in patients with different biomarkers, with an objective to identify the best drug at an efficacious and safe dose for the subpopulation defined by a given biomarker. This design addresses complex issues of such platform trials, incorporates information about toxicity and response outcomes from multiple tested doses and biomarkers, and maximizes potential patients benefit from the targeted therapies.

Location: Online via Zoom

Friday, February 12, 2021 at 10:00 am

Yuedon Wang, Ph.D.
Professor, Department of Statistics and Applied Probability at the University of California, Santa Barbara, CA

Abstract: Smoothing spline mixed-effects density models are proposed for the nonparametric estimation of density and conditional density functions with clustered data. The random effects in a density model introduce within-cluster correlation and allow us to borrow strength across clusters by shrinking cluster specific density function to the population average, where the amount of shrinkage is decided automatically by data. Estimation is carried out using the penalized likelihood and computed using a Markov chain Monte Carlo stochastic approximation algorithm.  We apply our methods to investigate evolution of hemoglobin density functions over time in response to guideline changes on anemia management for dialysis patients. Smoothing spline mixed-effects density models are proposed for the nonparametric estimation of density and conditional density functions with clustered data. The random effects in a density model introduce within-cluster correlation and allow us to borrow strength across clusters by shrinking cluster specific density function to the population average, where the amount of shrinkage is decided automatically by data. Estimation is carried out using the penalized likelihood and computed using a Markov chain Monte Carlo stochastic approximation algorithm. We apply our methods to investigate evolution of hemoglobin density functions over time in response to guideline changes on anemia management  for dialysis patients.

Location: Online via Zoom

Friday, January 25, 2021 at 10:00 am

James Zou, Ph.D.
Assistant Professor of Biomedical Data Science, Computer Science and Electrical Engineering at Stanford University, CA

Abstract: In this talk, new computer vision algorithms to learn complex morphologies and phenotypes that are important for human diseases will be presented. I will illustrate this approach with examples that capture physical scales from macro to micro: 1) video-based AI to assess heart function (Ouyang et al Nature 2020), 2) generating spatial transcriptomics from histology images (He et al Nature BME 2020), 3) and learning morphodynamics of immune cells. Throughout the talk I’ll illustrate new design principles/tools for human-compatible and robust AI that we developed to enable these technologies (Ghorbani et al. ICML 2020, Abid et al. Nature MI 2020). 

Location: Online via Zoom

The Bio3 Seminar Series are for educational purposes and intended for members of the Georgetown University community.  The seminars are closed to the public.