Research Centers

U-M IOE is home to two exciting research centers led by our very own faculty members and graduate students.

• Center for Ergonomics: The Center for Ergonomics is dedicated to gaining and sharing a better understanding of how tools, technologies and work practices affect health and performance and how they can be improved through human-centered design. Their research also advances basic knowledge about people’s psychological and physical abilities and limitations.
• Center for Healthcare Engineering and Patient Safety: applies operations engineering principles to pursue a healthcare system that more equitably and sustainably serves patients, providers, and institutions.

Undergraduate research symposium

IOE SURE/SROP Projects – 2025

The College of Engineering and the University of Michigan offer two programs SURE (Summer Undergraduate Research in Engineering) and SROP (Summer Research Opportunity Program) to provide undergraduate students with an opportunity to participate in summer research.  Read more about SURE criteria and selection here.

IOE Project 1 (for 1-2 students)

Trust-driven human-robot interaction
Faculty Mentor(s): Xi Jessie Yang [email protected]
Research Mode: In Lab

The use of autonomous agents to assist human performance is growing unprecedentedly. As the capabilities of autonomy advance, it would function as a full-fledged team member to perceive and analyze information, make decisions, and execute actions. For the human-autonomy/robot team to perform optimally, appropriate trust should be established between the two partners. This research aims to model trust between humans and autonomous agents and facilitate human-autonomy interaction. The students interested in this project can work on

1. design of experiments and human-subject testing and/or
2. applied machine learning to model interaction.

IOE Project 2 (for 1-2 students)

Understanding hidden low-dimensional structures in high-dimensional machine learning problems
Faculty Mentor(s): Salar Fattahi [email protected]
Research Mode: Hybrid
Prerequisites: Background in math, optimization, and statistics preferred

In this project, we aim to identify and leverage hidden yet useful low-dimensional structures, such as sparsity and low-rankness, in modern high-dimensional learning and estimation problems in machine learning. The project will address both the practical and theoretical aspects of these challenges, with a focus on understanding and enhancing existing algorithms to solve these learning problems efficiently and with provable guarantees. The required background for this project includes proficiency in Python coding, a strong foundation in mathematics and/or statistics, and basic knowledge of machine learning and optimization.

IOE Project 3 (for 1-2 students)

Computational Cognitive Modeling
Faculty Mentor(s): Yili Liu [email protected]
Research Mode: Hybrid
Prerequisites: Python programming skills

SURE students will work with Professor Yili Liu and graduate and undergrad students to develop and test computational models of cognitive human performance and human-machine interaction, human-automation interaction, and/or human-robot interaction. Excellent Python programming skill is essential; experience with SimPy, TkInter, MatPlotLib is important. Course work in IOE474 simulation is desirable though not necessarily required depending on the students’ other skills and backgrounds.

IOE Project 4 (for 1-2 students)

GenAI for Human Factors Engineering
Faculty Mentor(s): Yili Liu [email protected]
Research Mode: Hybrid
Prerequisites: AI, GenAI, web systems (such as EECS 485, EECS 492)

SURE students will work with the professor and a team of graduate and undergraduate students to develop and evaluate GenAI systems for Human Factors Engineering.

IOE Project 5 (for 1-2 students)

Cultural Aspects of Systems Engineering and Design
Faculty Mentor(s): Yili Liu [email protected]
Research Mode: Hybrid
Prerequisites:

SURE students will work with Professor Yili Liu and graduate and undergrad students to study some cultural issues in systems engineering and design through literature review, data collection and analysis, and/or model/software development, and/or controlled lab experiments. Proficiency in two or more languages (I mean human languages for this project, not Python or Cobra…) is highly desirable, though not necessarily required, depending on the student’s other experiences. Meticulous attention to details, high level of motivation and creativity, and excellent writing and data organization skills are essential. Excellent GPA and strong cultural interests and experiences are desirable.

IOE Project 6 (for 1-2 students)

Applications of Industrial Engineering and Data Analytics to Medical Decision Making and Healthcare
Faculty Mentor(s): Mariel Lavieri [email protected]
Research Mode: Unknown
Prerequisites: No prior medical/healthcare experience is necessary. Students involved will work actively with the research team and participate in our lab meetings both in Engineering and at the Hospital. Students with good communication and programming skills and some statistical/machine learning background (or willingness to acquire those skills) will be preferred.

Project Description: My research team addresses a variety of questions related to the development and application of Industrial Engineering methodology to support medical decision making and other healthcare decisions. Using large datasets, some of the sample questions investigated by our team include: when to monitor and treat patients with chronic conditions? How to plan the long-term supply and demand for transplant organs? How to identify patients with concussion? These questions are addressed both from a patient and from a system’s perspective.

The student(s) involved in this research will support model development, validation and implementation potentially including (but not limited to):

• literature searches
• data gathering/data mining
• performance of statistical analysis/machine learning
• development and coding of algorithms
• development of simulation models
• validation of findings
• development of user friendly tools to implement findings (through possibly web based applications)

IOE Project 7 (for 1-3 students)

Evaluations of human-robot interactions in operational environments
Faculty Mentor(s): Leia Stirling [email protected]
Research Mode: In Lab
Prerequisites:

In this interdisciplinary research group, we bring together methods from human factors, biomechanics, and robotics. We strive to understand the physical and cognitive interactions for goal-oriented human task performance and support operational decision making that relies on manual task performance. These goals may include reducing musculoskeletal injury risks, supporting telehealth, and improving technology usability. However, the term performance is not universally defined and requires learning about the desired task goals and the sub-tasks and motions the human will need to accomplish them.

There are different projects students may support.

• Upper Extremity Exoskeletons for Industrial Applications: Exoskeletons are currently being evaluated for many different applications. In this project, the student may support the development of a study and/or data analysis related to an powered elbow exoskeleton designed to support activities of daily living.
• Space Telerobotics: In this project, the student may support the development of a simulation study and/or data analysis related to operating a small robotic satellite that inspects a simulated space station.
• Homebased Social Robotics: In this project, students will explore how different age populations use and interact with the Amazon Astro robot.

IOE Project 8 (for 1 student)

Soccer Data Analytics: Helping UM soccer with data and machine learning
Faculty Mentor(s): Albert Berahas [email protected]
Research Mode: In Lab, Online, Remote, Hybrid
Prerequisites: Interest in soccer; coding (Python)

IOE Project 9 (for 1 student)

Improving radiotherapy treatments with optimization tools
Faculty Mentor(s): Marina Epelman (IOE), [email protected]
Martha Matuszak (NERS), [email protected]
Research Mode: In Lab
Prerequisites: Programming experience. Familiarity with optimization modeling is helpful, but willingness to learn is sufficient.

This is a joint project with Michigan Medicine Radiation Oncology group, co-supervised by Prof. Martha Matuszak. We will work on ways to use optimization modeling and computation techniques to improve treatment plans for cancer patients treated by radiotherapy. This research is a unique combination of mathematical modeling, algorithms, and computing applied to an important medical problem, and offers opportunities for interdisciplinary collaborations.

IOE Project 10 (for 1-6 students)

Applied Operations Research at the Center for Healthcare Engineering and Patient Safety
Faculty Mentor(s): Amy Cohn [email protected]
Research Mode: In Lab
Prerequisites: No prior medical/healthcare experience is necessary. Experience or coursework in fields such as industrial and operations engineering (IOE), computer science (CS), and/or data analysis is desired but not required.

The Center for Healthcare Engineering and Patient Safety (CHEPS) at the University of Michigan seeks dedicated, detail-oriented, and collaborative students to join our multidisciplinary research teams. As a Student Research Assistant, you will contribute to innovative projects aimed at improving healthcare systems through the integration of engineering, data analytics, and patient care principles.

This role provides the opportunity to work in a dynamic environment, gain hands-on experience with real-world challenges, and make a meaningful impact in healthcare. CHEPS emphasizes collaboration, professional growth, and the development of skills that prepare students for careers in academia, industry, or clinical settings.

Responsibilities:

• Assist with research and development activities, including data collection, analysis, and visualization.
• Participate in designing and testing tools and methodologies to address healthcare scheduling, optimization, and systems challenges.
• Contribute to documentation, reports, and presentations summarizing project findings and recommendations.
• Collaborate with multidisciplinary teams of researchers, clinicians, and fellow students.
• Engage in regular team meetings to discuss progress, share insights, and strategize solutions.

What you will gain:

• Practical experience working on impactful healthcare projects.
• Mentorship from experienced faculty and staff.
• Opportunities to network with peers and professionals in healthcare and engineering.
• A deeper understanding of systems optimization and its application in improving patient care.

This role is ideal for students passionate about blending technical expertise with meaningful healthcare contributions.