The Leadership Alliance

Potential Research Projects

See below for more information on potential research projects for Summer 2022. Please indicate your interest in participating in a specific project on your statement of purpose.  

Advanced Neurodiagnostics

We are developing new ways to study neurological diseases using electrophysiology and mathematical modeling. Our current projects include validating a new method for diagnosing the fatal disease ALS, developing a new approach to diagnosing seizures that originate in the hippocampus, and modeling how neuromodulation can control seizures. The goal of these projects is to directly translate basic science concepts into improved patient outcomes.

Ataxia Mechanisms and Measurement

We apply molecular and cellular biology and animal genetic techniques to understand the pathogenesis of neurological disorders such as ataxia and autism and design target therapies. In parallel, we are using wearable sensor technology to develop a more accurate way to assess severity of ataxia and the benefit of treatments.

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 the Virtual PIMCO Decision Research Laboratories during Summer 2022. 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 real participants, and guiding them through precise study protocols in order to learn about how humans 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 assistants are welcome to attend as they deepen their understanding and interest in the field of behavioral science.

Brain Trauma Research

We are interested understanding the pathophysiology of severe traumatic brain injury (TBI) and in improving functional outcomes of patients after such injuries. Critical care after TBI aims to mitigate secondary brain injury (SBI) via the continuous monitoring of intracranial pressure (ICP) and the partial brain tissue oxygen tension (PbtO2), among other physiologic variables.

Catalytic Tools for Organic Synthesis

Have you wondered how small-molecule drugs are synthesized in a wet lab, how a metal catalyst works, and how organic nanomaterials are prepared? The Dong lab at UChicago are developing new, powerful catalytic tools for organic synthesis. The student will be paired with a senior graduate student or a postdoc fellow to work on developing new catalysis reactions that allow activation of insert chemical bonds or preparing nanographene materials. After the training period, the student will gain valuable skills on carrying out multi-step synthesis, operating air/moisture-sensitive reactions, and preparing nano-materials. 

Center for Applied Artificial Intelligence

The Center for Applied AI is looking for research interns for a project using deep learning to understand bias in the US criminal justice system. We are developing a data-driven approach to understanding kinds of bias that are often imperceptible to humans. The successful applicant will interact with a suite of deep learning models (such as convolutional neural nets or GANs) to identify and explain actual causes of bias in the criminal justice context. An undergraduate with a strong statistics, computer science, or economics background would be well suited to this project.

The Center for Applied AI is a lab within the Booth School of Economics. We have multiple initiatives focused on improving equity, reducing bias, and finding solutions to other societal issues. We conduct research across several of these areas, including multiple projects that are suitable for an undergraduate to contribute to. We welcome all applicants with a strong quantitative background and some programming experience to apply.

Connectomics and Neural Networks

We are pioneering new techniques for mapping the fine structure of the nervous system at industrial scale. These include large volume automated electron microscopy for mapping neuronal connections at the nanoscale – ‘connectomics’, synchrotron source X-ray microscopy for mapping the cellular composition of entire brains, and genetic labeling of specific cell types for x-rays and electrons. We apply these tools to brains in the service of answering the questions: how do brains grow up and age and how do brains differ across individuals, phylogeny, and disease.

Deep Ocean Circulation in a Changing Climate
Deep ocean circulation changes are believed to have played a major role in modulating past climatic changes, and are likely to again play an important role in the future. To better understand the ocean’s role in climate change we need to understand how the deep ocean circulation responds to changes in the surface climate on time-scales from decades to millennia. The student will contribute to this effort by performing computations using a numerical model for the deep ocean overturning circulation.  Useful background for this project includes calculous (ideally multi-variate calculous) and some programming skills (ideally in Python).
Delivery of Stroke Care

We use mixed methods including community-participatory research, machine learning, human factors engineering, and implementation science to design, develop, validate, and test models to improve delivery of stroke care in the community, prehospital, and hospital settings. Currently, we are focused on improving inter-facility transfer processes, prehospital stroke screening for severe stroke, and stroke diagnosis in the emergency room.

Fairness in Face Recognition

Face recognition technologies (FRT) have numerous practical and useful applications, but many voices have been raised in recent years about their potential dangers, related to issues of bias, privacy, transparency, permanence, function creep, anonymity, effects on free speech, and more. Some of the claims, both for the effectiveness of FRT and for their dangers, are vague and not adequately supported by data. There are both regulatory and technical approaches that may be promising in their ability to limit the negatives and enhance the positives of FRT. This project involves considering a mix of technical, social science, and legal policy perspectives related to these topics. Students will assess various technical results and claims, investigate how some systems are used in practice (including both technical and human considerations), and examine the landscape of legal and regulatory issues. This will include reading scholarly literature in several areas and writing a final report summarizing key findings; it may also involve discussions with students and/or scholars outside of computing, e.g., in public policy or social science. It may also involve working with a PhD student on technical approaches to understand and improve face recognition systems with respect to fairness issues. 

Machine Learning and Bias

The Toyota Technological Institute at Chicago seeks a research assistant to aid in investigating the effects that different kinds of bias in training data can have on the outputs of machine learning systems.  The student will aid in this effort by performing controlled experiments in which different kinds of bias are injected into training data to observe the effects on a machine learning system, as well as the results of different mitigation approaches.  For more information: https://ttic.uchicago.edu/~avrim/.

Mechanisms underlying neural circuit assembly
Memory and Cognitive Impairment

We conduct laboratory studies on cognitive impairments that occur in neurological disorders and develops novel interventions for these impairments using a variety of brain stimulation methods. The main focus is on understanding and influencing neurocognitive processes involved in memory for life events, which is disrupted in individuals with amnesia. Research projects involve human participants and use methods such as functional MRI, transcranial magnetic brain stimulation (TMS), and recordings from neurosurgically implanted depth electrodes.

Miniature self-driving cars

We are looking for students to help us develop and implement algorithms that enable small self-driving cars (Duckiebots) to autonomously navigate a model town (Duckietown), complete with roads, intersections, signage, traffic lights, and pedestrians. You will have the opportunity to work on a variety of problems that are fundamental to self-driving and robotics more generally, including localization and mapping, obstacle detection, planning under uncertainty, reinforcement learning, and multi-vehicLe coordination, using a combination of classic architectures and modern machine learning-based approaches. Students will use modern software architectures built with Python, the Robot Operating System (ROS), and Docker as they deploy algorithms that run entirely on board the Duckiebots. Students will be part of the Duckietown initiative (http://duckietown.org), a rapidly growing international effort to make robotics and autonomy accessible to everyone.. 

Sign Language Understanding from Video

We have been working on methods for automatic processing of American Sign Language (ASL) video, including detection, transcription, and translation into English.  The long-term goal of this project is to make sign language processing as successful and usable as text and speech processing have become, so that deaf individuals can take advantage of recent advances in artificial intelligence technologies and so that deaf and hearing individuals can communicate as seamlessly as possible.  This project involves a mix of computer vision, natural language processing, and machine learning.  Students participating in this research will help design and conduct experiments, read and discuss papers in the area, and analyze and visualize data and results.  The prerequisites for this project are a basic understanding of vectors and vector distances (e.g. from an introductory linear algebra or multivariate calculus course), good programming skills (ideally in Python), and an adventurous spirit for experimentation and exploration!  Additional background in machine learning, computer vision, or language processing may be useful but is not required.

Spoken word embeddings

Word embeddings are vector representations of words.  They are ubiquitous throughout natural language processing (NLP), where they are typically learned from large amounts of text data and are intended to represent the meanings of words.  For example, the embedding vectors of words with similar meanings or functions tend to be near each other.  This Leadership Alliance project is related to research on automatically learning vector embeddings of *spoken* words.  For spoken words we may want to represent their meaning, as for written words, but we also want their embedding vectors to represent how they sound.  Spoken word embeddings have been used to search for queries in spoken archives, as well as to improve the performance of automatic speech recognition systems.  The goal of this project is to develop tools for exploring and understanding spoken word embeddings.  The outcome of the project could be, for example, a software package hosted on a web site that can be used by anyone to explore the space of words in a variety of languages.  The prerequisites for this project are a basic understanding of vectors and vector distances (e.g. from an introductory linear algebra or multivariate calculus course), good programming skills (ideally in python), and an adventurous spirit for experimentation and exploration!  Additional background in machine learning or audio/text processing may be useful but is not required.

Stigler Center for the Study of the Economy and the State

The Stigler Center at the University of Chicago Booth School of Business seeks a motivated and detail-oriented research assistant (RA) to assist with the next phase of a research project that aims to better understand the American business elites. In particular, we are exploring the characteristics of the business elites since the 1900s and how they have or have not changed over time. As an RA on this project, you will work alongside a research professional conducting research from day one. By the end of summer, you will have experienced the various aspects of research work in economics, including data work (cleaning and analysis), literature review, coding (Python/Stata/R), and more. This position is especially suited to students who want to gain exposure to hands-on research work and/or are considering pursuing academic research as a potential career.

The Stigler Center for the Study of the Economy and the State designs, discusses, and disseminates ideas around the political economy of competition, the role of special interests, and the future of capitalism and free markets.