When you apply, we will ask you to rank your top three interests from the research projects listed below. To frame the social relevance of the research, each project is aligned with at least one Sustainable Development Goal (SDG) defined by the United Nations.

Your project choices are important because they will determine which mentors will evaluate your application. We therefore encourage applicants to explore each mentor’s website to learn more about the individual research activities of each lab. 




Facilitating Transgender Identity Consolidation with Virtual Reality

Mentor: Evan Suma Rosenberg
SDG: Gender Equality

Individuals that identify as transgender often face discrimination, harassment, or physical violence, making it uncomfortable or potentially unsafe to express a non-conforming gender identity, even with family or close friends.  The stressors can have dire implications, leading to significantly increased risks for mental illness, substance abuse disorders, and suicide.  This project seeks to investigate the potential of virtual reality (VR) to improve the wellbeing of transgender adolescents and adults.  VR is a rapidly emerging medium that can provide new opportunities to explore non-conforming gender expressions.  This interdisciplinary project aims to design, develop, and scientifically evaluate a novel system that enables transgender individuals to experience virtual embodiment with a diverse array of customizable body representations.  REU students will have the opportunity to contribute to the design and development of this framework in collaboration with researchers in family social science.


Reducing Inequitable Barriers for Engaging with Virtual Reality Technologies

Mentor: Evan Suma Rosenberg and Victoria Interrante
SDG: Gender Equality

Half or more of all people who have ever used VR technology have at some point experienced cybersickness – feelings of nausea, disorientation and eye strain – and cybersickness is becoming a major obstacle to the wider deployment of VR for socially beneficial purposes in areas such as education, psychotherapy, job training, implicit/unconscious bias reduction training, cultural heritage, manufacturing, design, and more. As cybersickness disproportionately affects women, developing strategies to predict and prevent cybersickness onset or mitigate cybersickness severity is especially important to ensure equal opportunity of access to this important technology. The REU student will work with a large team of students and faculty from Computer Science and Kinesiology to develop and test software written using Unity/C# or Babylon.js/TypeScript to assist in conducting remote human subjects experiments and analyzing collected data.


Exploring Virtual Reality to Mitigate Implicit Bias

Mentors: Evan Suma Rosenberg and Victoria Interrante
SDG: Reduced Inequalities

Implicit biases are unconscious stereotypes that can affect our interpretation of factual information and influence the decisions we make in ways that we are not overtly aware of, leading to negative consequences in a very wide range of important areas including: public safety; hiring, promotion and salary decisions; and the provision of timely and appropriate medical treatment.  Virtual reality technology has tremendous potential as a medium for implicit bias reduction training, yet there are many open questions in how best to deploy VR for maximum benefit.  The REU student will work with a small team including a Ph.D. student and a social psychologist to help design and pilot potential VR interventions.


Equitable Algorithms: How Intelligent Systems Can Serve the Needs of Community Members

Mentor: Loren Terveen
SDGs: Gender EqualityReduced Inequalities

Organizations have been deploying intelligent algorithms to support their decision making processes for several decades, but awareness of this trend has exploded recently. Algorithms have been used to support judges in making parole decisions, social workers in evaluating whether a child should be removed from their family home, and employers in evaluating resumes of job applicants. In many cases, fundamental inequities have been identified, leading some researchers and advocates to argue that algorithms should not be used at all for these sorts of sensitive decisions.   We are fortunate to study these issues in the context of Wikipedia, the world's largest encyclopedia and collaborative knowledge production project. Algorithms play a prominent role in Wikipedia, for example, identifying edits that are likely to be damaging and recommending articles for people to work on. However, Wikipedia also is known to suffer from biases, particularly against contributions by and about women. Algorithms have the potential to make such problems worse (e.g., by reinforcing existing biases) or better (e.g., by directing people toward editing underrepresented topics or blocking harmful behaviors).   We have been studying how algorithms operate in Wikipedia and coming up with methods for designing more effective and equitable algorithms. We will define a specific REU project in this space that meets your particular interests and abilities.


Technologies for Connecting Older Adults and Elementary School Children

Mentor: Lana Yarosh
SDG: Quality Education

Intergenerational mentorship provides children with one-on-one support that can improve academic performance and increase resilience, while also helping older adults remain active and connected. Computing allows to expand access to intergenerational mentorship and make the experience more fun for both the mentors and the students. Working closely with a local non-profit for older adults and Twin Cities public school teachers, we will develop and build new prototypes to support reading together, helping with homework, and playing games across distance. As an example, we may build a tablet-based augmented reality application that allows older adults and children to do a virtual scavenger hunt together while reading through a book.


Computational Support for Recovery from Addiction

Mentor: Lana Yarosh
SDG: Good Health and Well-Being

Addiction and alcoholism are one of the greatest threats to people’s health and well-being. Early recovery is a particularly sensitive time, with as many as three fourths of people experiencing relapse. Computing provides an opportunity to connect people with the support they need to get and stay in recovery. Working closely with members of the recovery community, we will conduct and analyze qualitative formative work to understand people's needs and values. Based on insights gained from this work, students will contribute to the development and deployment of interactive technologies for computational support for recovery. As an example, we may work on a mobile application to help connect people who are new to recovery with more experienced members of the recovery community.


Discovering Dosage of Alternative Treatments to Opioid Use Disorder Recommended by Online Communities

Mentor: Stevie Chancellor
SDG: Good Health and Well-Being

There are online communities that support those struggling with opioid use disorder (colloquially called opioid addiction) and facilitating recovery outcomes. In addition to promoting well-ground treatment strategies for recovery, some online communities also promote clinically unverified treatments for recovery. These include untested drugs and substances, questionably legal drugs, and over-the-counter medications. Little research exists on which alternative treatments people use, whether these treatments are effective for recovery, or the side effects of their use. This project will discover how people take and consume these substances to facilitate opioid use disorder recovery. This includes names of substances, what quantities and dosage, and how individuals describe the effectiveness of these substances. Students will build a computational language system with state-of-the-art language models that automatically identify what substances are used and the dosage amounts recommended by communities for effective recovery. They will also work on the entire data pipeline, including data gathering, annotation, and cleaning; model training and engineering; and model testing and evaluation on real-world examples.


Applications of Virtual Reality in Addressing Neuropsychiatric Disorders

Mentor: Victoria Interrante
SDG: Good Health and Well-Being

This project focuses on efforts to develop and test the use of immersive virtual reality technology to assist in the early detection childhood-onset neuropsychiatric disorders and to support the implementation and quantitative assessment of therapeutic interventions. The summer intern will work closely with a team that includes faculty from the Medical School (Psychiatry) as well as experts in computer vision, visualization, and virtual reality.  The principle targeted disorder for detection and quantification is Tourette syndrome, which is characterized by observable, involuntary repetitive motor movements and vocalizations.  We will also be considering potential VR-supported interventions for obsessive-compulsive disorder, attention-deficit hyperactivity disorder, and autism; many of these neuropsychiatric conditions are co-occurring. Research efforts will focus on computational methods and hardware for human behavior analysis and on the development of immersive virtual environments that may be useful in diagnosis and treatment.


Applications of Computer Graphics in Sensorimotor Rehabilitation

Mentor: Victoria Interrante
SDG: Good Health and Well-Being

This project focuses on helping to develop a gaming front-end for a rehabilitation robot to be used in physical therapy for people recovering from stroke or injury.  Repetitive training/exercises are important to enabling optimal outcomes after brain injuries that impair motor control and sensorimotor perception, yet the monotony of typical exercise regimens, combined with user frustration due to inability to perform the requisite movements, can result in reduced adherence and a concomitant lack of progress in achieving positive outcomes.  The REU student will work closely with a team that includes faculty and postdoctoral researchers in Kinesiology, as well as experts in computer graphics on this project.


Immersive Data Storytelling for Climate Action

Mentor: Dan Keefe
SDG: Climate Action

The objective is to design and create a new software platform for bringing high-end 3D scientific data visualization to a wider audience.  We will target two delivery platforms:  3D web (for the widest possible dissemination) and science museum planetarium dome (for a compelling immersive display experience).  The project will leverage a long-term existing lab partnership with the Bell Museum of Natural History, which is located on campus and accessible via campus transportation.  The project is a perfect fit for computer science students who also have an interest in writing, storytelling, filmmaking, and/or visual art.  The research will make use of a custom 3D data visualization engine developed via the NSF-funded Sculpting Visualizations project (an art-science collaboration) and reimagine the computer graphics software as an interactive data storytelling platform.  Also drawing on existing collaborations, we will work directly with climate scientists and data from their latest supercomputer simulations to, for example, to tell the story of what is happening to the Ronne Ice Shelf in Antarctica.


Applications of Virtual Reality in Forestry

Mentor: Victoria Interrante
SDGs: Climate Action, Life on Land

In this project, the summer intern will work in an interdisciplinary team with faculty from computer science and forestry to explore the use of VR for forestry-related aims, potentially including: research into the potential benefits of virtual "forest-bathing"; using VR to support a better understanding of the potential impacts of climate change on forest resources; using VR to solicit user feedback on alternative strategies for supporting forest recovery from disease/damage.


VR Visualizations for Exploring Fossil Bones in the Context of Living Animal Motion Data

Mentor: Morgan Turner
SDG: Life on Land

The study of living animal motion has greatly enhanced our understanding of how extinct dinosaurs and their relatives once stood and walked. The shapes of bones are physical data shared by both living and extinct animals and serve as the foundation of relating skeletal form to animal function. Recent studies of live animals have generated large volumes of reconstructed skeletal animations and we believe a new approach to interactive visualization is the key that will allow us to unlock the potential of this data, particularly in their application to fossil bones. This summer, the Interactive Visualization Lab is looking for 2 students to help us integrate a physical and virtual skeleton in Virtual and Augmented Reality (VR and AR) and create immersive and interactive data visualizations to help us explore the relationship between bone shape and skeletal movement. 


Evaluating the Cost of Deep Perceptual Failures in Underwater Human-Robot Interaction 

Mentor: Junaed Sattar
SDG: Life Below Water

This project will focus on developing methods for autonomous underwater robots to measure the probabilities of failures of deep-learned algorithms for robotic perception. The project will extend the work of the interactive robotics and vision laboratory in underwater human-robot interaction. Robots capable of following divers or understanding their hand gestures are essential for collaborative task execution underwater, but any failures in these capabilities can be extremely costly and dangerous to humans, robots, and the environment. This project will look into creating AI-based methods to evaluate the failure of deep-learned perception algorithms to ensure such failures do not lead to costly failures.


Underwater HRI