The Leadership Alliance

Below you will find a selection of confirmed research projects for Summer 2024. We will continue to add projects to this page until the application deadline. This is not an exhaustive list of summer projects. For example, we will select 8 students in Neuroscience areas. We host students in all disciplines and will review all applications received. 

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.

In sophomore organic chemistry, we have all learned the classical ketone alkylation using strong bases, such as LDA, and using alkyl halides as the alkylating agent. While it is such an important transformation, this reaction is neither selective nor environmentally friendly. It generates many undesired side-products, such as Aldol and over alkylations, etc., and forms stoichiometric byproducts.  In addition, the alkyl halides are in general more toxic and expensive. Moreover, the cryogenic conditions limit the scale up. Here, the Dong group at the University of Chicago aims to develop a green chemistry approach to realize selective carbonyl alkylation reactions using simple olefins as the alkylating reagent. The key is proper design of the metal catalyst and ligand. In this project, you learn a wide range of organic and organometallic synthetic skills, knowledge on green chemistry and catalysis, as well as compound purification and characterization.

The Center for Applied AI, a research lab within the University of Chicago Booth School of Business, has multiple initiatives focusing on improving equity, reducing bias, and other societal issues. We conduct research across several of these areas, including criminal justice and healthcare. Below, we outline a few potential projects that are suitable for a Leadership Alliance scholar. Successful applicants will work on a project appropriate to their skills and experience.

Influence of noise in human hypothesis formation – Our research lab has a large focus on “human-AI interaction.” In one exercise, we showed pairs of mugshot images to survey participants, and asked those participants to identify which mugshot was more likely to be detained by a judge (see https://faceeffect.ai/). For this project, we want to measure to what extent noise in the feedback given to these participants influences their written descriptions at the end of the survey. For example, if 10% of the ‘correct’ labels are reversed, do respondents form different or weaker hypotheses? This has important implications for human-AI communication. The ideal applicant should have some statistical experience and a quantitative background.

Deep bias in generative models for scientific discovery – Recent advances in deep learning have led to powerful generative models that can produce or manipulate data (such as chest X-Rays 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 intuitive 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 healthcare or criminal justice, for example. The successful applicant should have extensive experience with deep learning and a quantitative background.