Mathematics and Natural Sciences

Connie Roth, PhD

Professor, Emory College Of Arts And Sciences, Physics

Solvent Vapor Setup for Controlled Solvent Quenching of Polymer Glasses

Polymer glasses are used for everything from common house-hold goods to active layers in organic light emitting diode (OLED) displays. As a nonequilibrium state, glasses have properties that strongly depend on how they were formed, exhibiting time-dependent densification and associated property changes on a logarithmic timescale, collectively known as physical aging. New processing methods such as solvent vapor annealing (SVA) use exposure to solvent vapor as a means of driving a glassy polymer film into an equilibrium-liquid state to cause self-assembly of a polymer blend or block copolymer into some desired morphology before quenching the polymer back into its glass state by solvent evaporation. Glass formation via solvent quench such as solvent casting, spin-coating, and now SVA is considerably less well-understood than the more heavily studied temperature quench where glasses are formed by thermal cooling. The proposed research aims to open up a new research area in the Roth lab investigating the properties of glassy polymer films formed by solvent quench under controlled conditions. URC funding is requested to purchase and assemble the components needed for a solvent vapor setup that will enable controlled solvent vapor conditions with ellipsometry and quartz crystal microbalance (QCM) measurements. Ellipsometry quantifies the time-dependent densification of films by measuring their thickness and refractive index, while QCM determines the mass of solvent lost. URC funds will cover salary for a recent Emory grad during her gap year to assemble and calibrate the new solvent vapor setup, as well as collect preliminary data to pursue external funding.

Emily Wall, PhD

Assistant Professor, Emory College Of Arts And Sciences, Computer Science

Developing Behavior Change Interventions for Responsible Data Science

Whether measuring the effects of climate change, tracking the spread of COVID-19, or approving a loan for a fledgling business, individuals and organizations all over the world are using data science tools to make informed, data-driven decisions. While data-driven practices have tremendous potential to accelerate societal and technological innovation, they can also cause considerable harm. One well-known instance occurred when the image recognition algorithms used in Google Photos labeled a black couple as gorillas. This is just one recently publicized example; however, this represents an artifact of larger systems that can cause extensive unseen harm. We posit that analysts can play a key limiting role in such injustices by exercising responsible data science practices.

The objective of this proposal is to promote responsible data science through behavior change interventions, which requires both awareness and action by analysts to make lasting change. This research innovates on prior work by the PI in bias detection and bias mitigation by leveraging the psychological theory behind behavior change and habit formation to structure the design process for proactive tools for responsible data science. In particular most recently, the PI and colleagues developed a novel theoretical framework for behavior change interventions in data science. The proposed work will expand upon this framework to design new UI components within well-known data science tools such as JupyterLab and Observable to help analysts (i) increase awareness and (ii) take action. Specifically, we propose to address this through (1) designing and developing interventions and (2) evaluating the efficacy of the interventions toward increasing awareness and imparting behavior change.