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://www-personal.umich.edu/~xijyang/index.html
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.
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.
Many communities are grappling with the rising popularity of new transportation platforms. One prominent example is the increased use of bicycle-based commuting. To respond to these demands, cities and towns have responded by installing a mix of fixed and mobile bicycle parking. In the current era of smart cities, there is now a demand for data-driven placement of this parking to best serve the needs of the community. This project is designed to spark the design and use of mathematical models for maximizing residential and commercial utility from parking placement. This summer, you will design general purpose tools and use them on a case study in downtown Ann Arbor. There is a mix of existing datasets (some small, some large) that can be used to verify and improve your mathematical model to reach implementable conclusions. The methods will rely on a mix of data analysis, geostatistics, simulation, and optimization. While prior experience with all of these tools is not a requirement, the project’s success hinges on experience with coding/computing. Important skills include how to upload, visualize, and adjust existing data in either Python, Matlab or R. Also important is a willingness to learning about new software packages.
Each year hundreds of drugs are in short supply nationally. These shortages negatively impact patient care, have resulted in patient deaths, and cost health systems hundreds of millions of dollars annually. The overall goals of this project are (1) to understand the causes of drug shortages, (2) to analyze the structure of the drug supply chain, (3) to examine and propose alternative policies that would reduce the impact, magnitude and frequency of drug shortages. The student will be involved in (a) literature searches, (b) data collection, (c) data analysis, and (d) spreadsheet modeling. The student will work closely with the faculty mentor and with a senior PhD student.
The electricity and gas networks are increasingly interdependent in the United States. These interdependencies have led to some undesirable effects such as the polar vortex which dramatically affected New England a few years ago. To remedy these limitations, this NSF-funded project explores how to synchronize the operations of these networks more effectively. A critical aspect underlying this effort is the development of a forecasting model that predicts nodal gas prices in the spot market based on the load, weather, and other attributes at various places of the networks.
The RITMO project uses data science and optimization to reinvent mobility in American cities, providing better access to jobs, health care, and quality food for entire segments of the population. RITMO accumulates massive data sets on mobility and use them to design new on-demand multimodal transit systems and car-sharing programs. This project aims at developing novel data-mining algorithms to derive high-fidelity activiThe RITMO project uses data science and optimization to reinvent mobility in American cities, providing better access to jobs, health care, and quality food for entire segments of the population. RITMO accumulates massive data sets on mobility and use them to design new on-demand multimodal transit systems and car-sharing programs. This project aims at developing novel data-mining algorithms to derive high-fidelity activity-based models from massive data sets, to bring a step change in our understanding of mobility. Strong algorithm and programming skills, and knowledge of C/C++ and/or Python, is strongly recommended. ty-based models from massive data sets, to bring a step change in our understanding of mobility. Strong algorithm and programming skills, and knowledge of C/C++ and/or Python, is strongly recommended.
In this project, students will engage in research to understand and support the success of engineering students, especially those historically underrepresented in the field. A student working with my research team will study: (1) the role of mentoring practices, programs, and relationships in STEM education; (2) how social community influences a mentoring intervention program; or (3) the role of resilience and grit in undergraduate engineering persistence. Responsibilities for the student include: (a) collecting data via observations, surveys, and/or interviews, (b) performing literature searches and managing data using excel or NVivo, (c) analyzing data both qualitatively and quantitatively, and (d) communicating outcomes in verbal and written form. Interested students should contact Dr. Mondisa for more information
We are conducting a variety of studies to examine driver distraction and driver workload. In one project, we are attempting to develop predictions for how long it takes to use a speech interface. As a first step, we have data on how quickly people speak. We now need data on how fast speech engines speak. We are also examining research on how people use touch screens in cars, attempting to predict task time. We need to further analyze existing data and the literature. We may also have projects related to human interfaces for automated vehicles, depending on funding between now and this summer. Finally, we have a compact driving simulator, and want to add to its capabilities (support VR, develop new scenarios tools, etc.). That person should know Java.Depending on the project, students will be doing literature review, data analysis, and other tasks. Quite frankly, given the nature of our research, it is not unusual for students to spend some time on multiple projects.
Industrial and Operations Engineering