Professor Minghao Zheng from the University of Western Australia will present “Osteocytes-the cell that controls bone and marrow homeostasis”.  This special lecture is hosted by Professor Wentian Yang.

Carney Methods Meetup: Cell reprogramming

Join the Carney Institute for Brain Science for a Carney Methods Meetup featuring Ashley Webb and Alvin Yu-Wen Huang, both assistant professors in the Department of Molecular Biology, Cell Biology, and Biochemistry, who will discuss methods for direct and induced pluripotent stem cell reprogramming. Carney Methods Meetups are informal gatherings focused on methods for brain science, moderated by Jason Ritt, Carney’s scientific director of quantitative neuroscience.  Videos and notes from previous Meetups are available on  (https://www.brown.edu/carney/news-events/carney-methods-meetups).

Please note: Authenticated Brown IDs are required to join the Zoom.

Data Science, Computing, and Visualization (DSCoV) Workshops

Fridays at noon

These are one-hour skills-focused workshops, designed to be hands-on, so bring a laptop if you can. They are open to anyone, and any pre-requisite knowledge or resources will be announced beforehand. More info and schedule here. 

Pizza is available, or bring your own lunch if you wish!

October 7: Collaborative Coding: How To PR with GitHub

Presenter: John Holland, Senior Data Scientist, Advanced Research Computing, Brown Center for Computation and Visualization

In this hands-on workshop we’ll be covering how to:

• Get started with collaborative coding: GitHub basics and best practices (organizations and repository naming conventions).

• Make new code suggestions using “branches”: basics (“what is a branch, anyway?“) and best practices (naming conventions).

• Pull Request (PR):

• Create a PR and ask for feedback.

• Review a PR effectively.

• Finish a PR by merging, closing, or splitting it.

Prerequisites: please ensure you have a computer and a GitHub account.

Talk Title: Doubly Robust Estimation under the Marginal Structural Cox Model

Abstract: The marginal structural Cox model (Cox MSM) has been widely used to draw causal inference from observational studies with survival outcomes. The typical estimation approach under the Cox MSM is inverse probability of treatment weighting (IPTW) using a propensity score (PS) model, which is known to be inconsistent if the propensity score model is misspecified. Effort to protect against such model misspecification involves augmentation, which has been a challenge in the past due to the non-collapsibility of the Cox regression model. In this work we develop an augmented inverse probability weighted (AIPW) estimator with doubly robust properties including rate doubly robust, that enables us to use machine learning and a large class of nonparametric methods, to overcome the non-collapsibility challenge. We study both the theoretical and empirical performance of the augmented inverse probability weighted estimator. Time permitting we will also discuss informative censoring under the Cox MSM, where the inverse probability of censoring weighting (IPCW) is needed and the augmentation of that leads to an AIPW estimator that protects against the misspecification of both the PS and the censoring models.

*Light refreshments will be served

Talk Title: Using Wearables and Apps to Characterize Your Own Recurring Average Treatment Effects

Abstract: Temporally dense single-person “small data” have become widely available thanks to mobile apps (e.g., that provide patient-reported outcomes) and wearable sensors. Many caregivers and self-trackers want to use these intensive longitudinal data to help a specific person change their behavior to achieve desired health outcomes. Ideally, this involves discerning possible causes from correlations using that person’s own observational time series data. In paper one, we estimate within-individual average treatment effects of sleep duration on physical activity, and vice-versa. We introduce the model-twin randomization (MoTR; “motor”) and propensity score twin (PSTn; “piston”) methods for analyzing Fitbit sensor data. MoTR is a Monte Carlo implementation of the g-formula (i.e., standardization, back-door adjustment); PSTn implements propensity score inverse probability weighting. They estimate idiographic stable recurring effects, as done in n-of-1 trials and single case experimental designs. We characterize and apply both methods to the two authors’ own data, and compare our approaches to standard methods (with possible confounding) to show how to use causal inference to make truly personalized recommendations for health behavior change. In paper two, we apply MoTR to the three authors, thereby providing a guide for using MoTR to investigate your own recurring health conditions—and demonstrating how any suggested effects can differ greatly from those of other individuals.

*Light refreshments will be served

Talk Title: Sparse Deep Learning

Abstract: Sparsity is popular in statistics and machine learning, because it can avoid overfitting, speed up computations, and facilitate interpretations. In deep learning, however, the full potential of sparsity still needs to be explored. This presentation first recaps sparsity in the framework of high-dimensional statistics and then introduces sparsity-inducing methods and corresponding theory for modern deep-learning pipelines.