Mentor Areas
I am broadly interested in topics at the intersection of social science and computer science, with a particular interest in computational studies of media, collective intelligence, network diffusion, and common sense.
Description:
There are 4 main research areas undergraduates can contribute to:
1. Media Analytics: Large-Scale, Shared Data for the Study of the Information Ecosystem
Objectives: The goal of this project is to understand the information ecosystem across media (the web and TV). In parallel we hope to design, build, and test tools and services to improve the state of public knowledge and discourse.
Opportunities: Research opportunities vary, including data engineering/preprocessing/cleaning to statistical modeling and more advanced text processing at scale. Starting from raw data, the first step of the project is transferring the text data into efficient, and searchable structured data in AWS databases. Data analytics and design of websites for various media-related research questions is also another line of research.
2. Quantifying Commonsense
Objectives: Commonsense is challenging to define generally, yet widely used as a persuasive framing, and simultaneously as a focal concern in the development of artificial intelligence systems. This leads to assumptions and biases around what others hold to be commonsense and contributes to a potentially misplaced trust in this notion. We are measuring how common commonsense actually is through online experiments, and further trying to predict individuals' level of commonsense based on who they are and what else they believe.
Opportunities: Help build components of this experiment and contribute to the data processing and analysis.
3. High throughput virtual lab experiments for team performance
Objectives: Traditional social scientific experiments measure a small set of conditions at one time. Each experiment, however, makes many (often unique) assumptions about things it is not studying. As a consequence two experiments measuring the same conditions (but with different assumptions) might see different results. This is particularly problematic when studying team performance because team experiments are subject to more variance than individual experiments. We are studying team performance while manipulating many variables at once (including those others usually make assumptions about). By systematically running repeated experiments with minor variations in these variables, the underlying relationship of individual factors contributing to team performance can be better understood.
Opportunities: Help select variables, run experiments and perform analysis.
4. Visualizing COVID
Objectives: Data and visualizations around COVID surface unprecedented challenges for decision makers and local governments — existing visualizations don’t provide useful insights for most decisions and the data are so numerous that ad-hoc analysis is impossible. We aim to provide explorable, interactive visualizations to help decision makers understand what’s actually happening and how to make the best choices in their response. We use a combination of visualization and GIS tools such as D3, Vega, Observable, and ESRI.
Opportunities: Design and execute visualizations that will be used by our local government.
Preferred Qualifications
Potential candidates require strong programming skills and basic knowledge of statistical inference. Experience working with AWS services is a plus.
Project Website
Learn more about the researcher and/or the project here. Faculty Website
Details:
Preferred Student Year
Junior, Senior
Academic Term
Fall, Spring, Summer
I prefer to have students start during the above term(s).Volunteer
Yes
Yes indicates that faculty are open to volunteers.Paid
Yes
Yes indicates that faculty are open to paying students they engage in their research, regardless of their work-study eligibility.Work Study
Yes
Yes indicates that faculty are open to hiring work-study-eligible students.