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.
Chronic diseases such as Cancer and Cardiovascular disease are the leading cause of death in most countries. However, health outcomes improve significantly when these diseases are detected and treated early in their lifecycle, before they become symptomatic. The ability to predict a patient’s future health status and the potential outcome of medical tests and procedures allows doctors to make more effective decisions related to chronic diseases. Electronic medical records provide a valuable source of data that can be used to develop predictive models; however, observational data is influenced by several sources of bias. This project involves the development of new mathematical models using large medical datasets for prediction of disease outcomes, and for the study of optimal policies for early detection and treatment of cardiovascular disease and cancer. You will be using a combination of optimization methods, simulation, and statistical methods to address these challenges in collaboration with medical researchers. Important skills include knowledge about the theory of mathematical programming, stochastic models and experience with scientific computing including knowledge of C/C+ and/or mathematical software tools like CPLEX, Matlab, Python, and R.
We conduct research investigating human-automation and human-robot interaction. We want to understand how humans trust and interact with automated technologies/robots in a game-like environment. Students can work on various dimensions of a project including designing 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.For additional information, please visit http://icrl.engin.umich.edu/http://icrl.engin.umich.edu/
We will work on the application of modeling and operations research techniques to applied problems in healthcare, aviation, and possibly other domains. Prior projects have included work in transplant surgery, emergency medicine, and robust airline planning. Students will have the opportunity to work with a wide variety of students as well as experts from the application domains (e.g. physicians, airline managers). Desirable skills and background include IOE310, programming skills, data analysis skills, and strong writing and interpersonal skills.
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. Sample questions investigated in our team include: when to monitor and treat patients with chronic conditions? How to plan of 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 background (or willingness to acquire those skills) will be preferred.
Hundreds of lifesaving drugs are in short supply nationwide on any given day. The median duration of a shortage is 14 months. In this project, we are developing mathematical models of the drug supply chain. These models allow us to analyze how changes in the basic supply chain impact the likelihood, severity and duration of shortages. Students involved with the project will work closely with the faculty mentor and a senior PhD student on such tasks as collecting and analyzing data, conducting literature searches, and running the optimization models that are being developed. Background in statistics and undergraduate level optimization is desirable but not absolutely necessary.
Over the last 35 years, tuition costs have increased faster than has either the overall cost of living or the cost of healthcare. State funding for higher education, as a percentage of institutional expenditures, has decreased significantly over this time period. Student debt is 75% greater than all credit card debt in the US. Public support for higher education has been decreasing in recent years. Finally, the number of students enrolled in higher education institutions has decreased over the last five years.
Legislation working its way through Congress (as of December 2017) would (1) eliminate the tax deduction for interest on student loans, (2) eliminate student loan forgiveness for those who work in public sector positions, (3) tax the income on some university endowments, and (4) eliminate the tax exemption for graduate student tuition stipends. In addition, the administration proposed significant reductions in the indirect cost rate (overhead rate) that universities can charge on federal grants. This project will adopt a macro-level view of higher education funding and financing. The focus of this project is on the analysis of higher education financing trends and on the development of models that will enable the research team to predict the impact on students and institutions of proposed changes in the system. The student will assist in data analysis, literature surveys, and the development of models. A background in statistics and simulation is desirable, but not required.
Wildfires are one of the deadliest, most costly natural hazards in the U.S. They regularly cause significant impacts throughout the western U.S. The 2017 fire season has been particularly devastating. There has been much work done on fire behavior modeling, that is modeling how a given fire will spread. There has been comparatively little work done on longer-term fire risk modeling. Modern data analytic methods combined with current climate data and projections has the potential to enable a new, data-informed predictive modeling approach to longer-term fire risk modeling. Students involved in this project will work with modern data analytic methods and data to develop wildfire risk models.
Industrial and Operations Engineering