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?
IOE SURE/SROP Projects – 2022
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.
IOE Project 1 (for 1 student)
Applications of Industrial Engineering and Data Analytics to Medical Decision Making and Healthcare
Faculty Mentor(s): Mariel Lavieri email@example.com
Research Mode: Remote
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):
- Literature searches
- Data gathering/data mining
- Performance of statistical analysis
- 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)
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.
IOE Project 2 (for 1 – 4 students)
APPLIED OPTIMIZATION PROJECTS AT THE CENTER FOR HEALTHCARE ENGINEERING AND PATIENT SAFETY
Faculty Mentor(s): Amy Cohn firstname.lastname@example.org
Research Mode: Hybrid
The Center for Healthcare Engineering and Patient Safety (CHEPS) brings together multi-disciplinary teams to improve healthcare delivery and patient experiences. As a student researcher, you will contribute invaluable skills and knowledge to help ensure project success. We will work on the application of modeling and operations research techniques to applied, real-world problems. Prior projects have included work in transplant surgery, emergency medicine, and precision health. Students will have the opportunity to work with a wide variety of other students as well as experts from the application domains (e.g. physicians, nurses, clinical managers). Dependent on funding, there may be the opportunity to continue the position into the next academic year.
- Interest in applying IOE to healthcare
- Ability to manage a project and supervise a project team
- Strong communication skills
- Interest in working in a multi-disciplinary collaborative environment
Student employees at CHEPS:
- work closely with faculty and other students from many different backgrounds
- learn about complex systems and how different people view and contribute to healthcare
- get hands-on experience in a wide range of clinical environments
- contribute skills and knowledge while learning from others
IOE Project 3 (for 1 – 2 students)
Approximation and Online Algorithms
Faculty Mentor(s): Viswanath Nagarajan email@example.com
Research Mode: Remote or Hybrid
This project will investigate efficient algorithms for approximately solving “hard” optimization problems. The specific problem(s) considered can be from a variety of applications, such as telecommunication, supply-chain management and scheduling.
Students will learn the following:
- designing algorithms with mathematically rigorous performance guarantees.
- implementing these algorithms in Python (or another programming language).
- data collection.
- testing algorithms computationally.
- reading papers on related topics.
Collaboration with graduate students is also expected. Background in optimization, understanding mathematical proofs and programming is preferred.
IOE Project 4 (for 1- 2 students)
Fast, Adaptive and Scalable Stochastic Optimization Algorithms for Deep Learning
Faculty Mentor(s): Albert Berahas firstname.lastname@example.org
Research Mode: Remote, Online
Deep learning has emerged as one of the most popular paradigms in machine learning due to its unprecedented successes in many domains such as computer vision, speech and image recognition, and machine translation. The goal of this project is to develop some of the next generation algorithms for training deep neural networks. Our algorithm(s) will be endowed with the following characteristics: fast, algorithms with sound theoretical guarantees and good practical performance; adaptive, algorithms with minimal dependence on hyper-parameters; and, scalable, algorithms able to solve problems with millions of variables. The student(s) involved in this research project will participate in all aspects of the development of the algorithms (algorithmic, theoretical, computational).
Students will be expected to:
- Read, understand and present related literature.
- Develop prototype codes for initial algorithmic testing.
- Analyze (mathematically) the properties of the algorithms developed.
- Develop efficient implementations of the algorithms developed.
- Report progress in weekly meetings.
- Collaborate with the research group.
- Write a report of findings at the end of the program.
No prior research experience required. Elementary knowledge of Python required.