By working with nationally recognized faculty and graduate students, Industrial Operations and Engineering undergrad students are able to get real-world research experience in fields like healthcare, aviation, transportation and more.
What is it like to be an undergraduate researcher?
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. Please find information below about these two exciting programs. Read more here.
We conduct research to investigate how humans interact with automated technologies/robots. Students can work on various dimensions of a project including designing computer games, coding, conducting human-subject experiments and data analysis. For students interested in game design and coding, a strong background in EECS is expected (java, c++, python etc.). For students interested in experiments and data analysis, some experience with statistics is preferred. To check out the specific projects a student can work on, please visit http://icrl.engin.umich.edu.
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 prevent hospital readmissions? 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):
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.
In this project, our aim is to develop and implement efficient computational methods for data-driven optimization problems with a wide range of applications in machine learning, including graphical model inference, robust/sparse PCA, and safe reinforcement learning. The students will have the opportunity to: (1) work on the theoretical aspects of these problems, (2) develop practical algorithms with certifiable guarantees, (3) implement their algorithms in Python/MATLAB/C++, and (4) test their developed algorithms on real datasets collected from brain networks, power systems, transportation networks, and financial markets. Students with background in optimization, machine learning, and programming (or the desire to acquire these skills) are preferred.
The admitted students are expected to:
Industrial and Operations Engineering