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 – Fall 2022:

Friday, November 11, 2022 at 10:00 am

Robert Lund, Ph.D.
Professor and Chair, Department of Statistics, University of California, Santa Cruz

Abstract: This talk introduces changepoint issues in time-ordered data sequences and discusses their uses in resolving climate problems.  An asymptotic description of the single mean shift changepoint case is first given.  Next, a penalized likelihood method is developed for the multiple changepoint case from minimum description length information theory principles.  Optimizing the objective function yields estimates of the changepoint numbers and location time(s). The audience is then walked through an example of a climate precipitation homogenization. The talk closes by addressing the climate hurricane controversy: are North Atlantic Basin hurricanes becoming more numerous and/or stronger? 

Location: Online via Zoom

Friday, October 28, 2022 at 10:00 am

Aaron C. Courville, Ph.D.
Associate Professor, Department of Computer Science and Operations Research, Université de Montréal, Canada

Abstract: In this presentation, I’ll talk about recent work in my group that looked into a strange finding:  when training neural networks, periodically resetting some or all parameters can be helpful in promoting better solutions.  Beginning with a discussion of our findings in supervised learning where we relate this strategy to Iterated Learning, a method for promoting compositionality in emergent languages. We then show how parameter resets appear to offset a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later. We apply this reset mechanism to algorithms in both discrete (Atari 100k) and continuous action (DeepMind Control Suite) domains and observe consistently improving performance. I conclude with some recent finding and speculation about the underlying causes behind the observed effects of parameter resets. 

Location: Online via Zoom

Friday, October 14, 2022 at 10:00 am

Deepak Parashar, Ph.D.
Associate Professor, University of Warwick, England, UK

Abstract: Current precision medicine of cancer matches therapies to patients based on average molecular properties of the tumour, resulting in significant patient benefit. However, despite the success of this approach, resistance to drugs develops leading to variability in the duration of response. The approach is based on static molecular patterns observed at diagnosis whereas cancers are constantly evolving. We, therefore, focus on Dynamic Precision Medicine, an evolutionary-guided precision medicine strategy that explicitly considers intra-tumour heterogeneity and subclonal evolution and plans ahead in order to delay or prevent resistance. Clinical validation of such an evolutionary strategy poses challenges and requires bespoke development of clinical trial designs. In this talk, I will present preliminary results on the construction of such trial designs. The work is a joint collaboration with Georgetown (Robert Beckman, Matthew McCoy).

Location: Online via Zoom

Friday, September 23, 2022 at 10:00 am

Junrui Di, Ph.D. 
Associate Director, Early Clinical Development Digital Sciences & Translational Imaging Quantitative Sciences, Pfizer Inc. 

Abstract: In recent years, wearable and digital devices are gaining popularity in clinical trials and public health research due to technology advances, thanks to their reduced size, prolonged battery life, larger storage, and faster data transmission. In public health, countless research has been conducted to better understand the complex signals collected by wearable and digital devices and their relationship with human health. In clinical trials, emphasis has been given to the derivation and validation of novel digital endpoints and proper statistical methods to model such digital endpoints to objectively quantify disease progression and treatment effects. Such devices provide us a unique opportunity to reconsider the way data can be collected, from snapshot in-clinic measurements to continuous recording of behaviors of daily life. This presentation will highlight some representative examples of statistical and machine learning methodologies and applications of wearable and digital devices in health research.

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.