The University of Chicago

The Leadership Alliance

Potential Research Projects

Please see below for a sample list of research projects. Please note this is not a complete list. We will continue to add opportunities and will have additional projects in the Humanities and Social Sciences. 

Behavioral Science

The Center for Decision Research at the University of Chicago Booth School of Business seeks conscientious research assistants to conduct behavioral science and social psychology experiments through a variety of settings through the PIMCO Decision Research Laboratories during Summer 2023. Undergraduate students studying social psychology, cognitive psychology, and behavioral economics are encouraged to apply.

Each research assistant will have an opportunity to facilitate behavioral science experiments led by faculty and graduate student researchers of the Center. These studies involve interfacing with participants, and guiding them through precise study protocols in order to conduct research and learn about how we understand ourselves and each other.

Each research assistant will also have an opportunity to be mentored by members of the CDR’s academic community, including faculty members, postdoctoral researchers, and graduate students. Workshops and presentations of original and emergent research by behavioral scientists around the world are offered by the Center, and research assistants are welcome to attend as they deepen their understanding and interest in the field of behavioral science.

Board Homophily
We are studying the effect of ethnic and religious homophily – implicit or explicit biases towards members of the same or similar ethnic or religious groups – in corporate governance. Using data from BoardEx, we investigate the effects of this kind of affinity on the appointment of directors and executives, executive accountability, and the composition of the first post-IPO board. The strength of this homophily will be compared against other, traditionally investigated biases, such as educational and gender-based networks. Finally, we aim to determine whether these biases are correlated.
Business elites
This research project aims to better understand the social mobility of American business leaders (defined as the executives of the 150 largest US companies every five years between 1900 and 2015) compared to the general population. The goal is to venture beyond anecdotal evidence of well-known American business dynasties, to examine the extent to which past and present business leaders rose through the ranks, or enjoyed a privileged position. Possible changes in patterns over time will also be investigated.
Deep bias in generative models for scientific discovery

Recent advances in deep learning have led to powerful “counterfactual” models that can realistically manipulate input data (such as chest XRays or images of people’s faces). However, the notion of “similarity” implied by such a deep learning model does not always agree with the notion of similarity familiar to most humans. In this project, we wish to identify common dimensions of disagreement between machines and humans. This has important implications for the use of such counterfactual models for chemistry and healthcare, for example. The successful applicant should have some quantitative education and experience using Python. 

Generalizability of heart condition predictions

Using machine learning models, it is possible to predict some kinds of adverse health events such as sudden cardiac death (SCD), using only the data collected from an EKG. Because of natural variation in populations (among other things), these predictions can vary from source to source. In this project, we wish to take a model trained to predict an outcome such as SCD, measure its performance on a new data source, and diagnose the drivers of reduced performance. The successful applicant should have some interest in healthcare and experience using Python.

Particle Physics
Our goal is to understand the fundamental particle content of the universe. We use the ATLAS Experiment at the Large Hadron Collider to study the highest energy collisions ever produced in a laboratory, and search for evidence of new fundamental particles. At UChicago, our group specializes in upgrading the ATLAS detector and pursuing new collider technologies to access even higher energies in the future. More info here: https://kdipetri.web.cern.ch/Research.html
Stellar Archaeology
When stars are born, they inherit the elemental composition of the material they are born out of. Astronomers use spectroscopy to study the elemental composition of these stars in order to answer questions about the cosmic origin of the elements and the formation of our Milky Way galaxy, an approach known as stellar archaeology. In this research project, students will learn to determine the elemental composition of stars, using spectra obtained from the 6.5m Magellan telescopes.