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 – 2023
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 1-2 students)
Applications of Industrial Engineering and Data Analytics to Medical Decision Making and Healthcare
Faculty Mentor(s): Mariel Lavieri email@example.com
Research Mode: Hybrid
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: IN-LAB
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: PRIMARILY IN-PERSON EXPECTED
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)
Faculty Mentor(s): X. Jessie Yang, firstname.lastname@example.org
Research Mode: IN lab
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 CS is expected (java, c++, python, etc.). For students interested in experiments and data analysis, some experience with statistics is expected. To check out the specific projects a student can work on, please visit http://icrl.engin.umich.edu.
IOE Project 5 (for 1 student)
Digital Twin Calibration
Faculty Mentor(s): Eunshin Byon, email@example.com
Research Mode: Primarily in-person expected
Advances in numerical algorithms and computing power bring digital twins to the forefront of operational analysis and management of engineered systems. As a virtual representation of a physical system, a digital twin is a computer model that can be useful for monitoring, forecasting, and testing the system in a virtual world. In many applications digital twins are developed based on physics-based first principles and often require various parameters to be appropriately specified. Parameter calibration is a procedure to identify those unknown parameter values with observational data. This project aims to estimate those digital twin parameters using statistical and data-driven optimization techniques.
- Strong statistical and optimization background.
- Good communication skills
- Programming skills (R or Python)
IOE Project 6 (for 1 student)
APPLICATIONS OF INDUSTRIAL ENGINEERING AND DATA ANALYTICS TO HOSPITAL DECISION MAKING
Faculty Mentor(s): Mark Van Oyen, firstname.lastname@example.org
Research Mode: on-campus primarily
My research team emphasizes the use of healthcare data to provide optimization and decision support using modeling, simulation, optimization, machine learning, etc. Improving systems and medical decision making are important emphases. We have a large data set which requires effort to assemble in a usable manner, statistically characterize, and perform exploratory analysis. The main project anticipated concerns how patients are assigned to beds upon arrival, and how they move through various units of the hospital. In particular, we are interested in how the COVID-19 pandemic changed things, and how the hospital made major changes. We are eager to reduce unplanned readmissions to the hospital. A related project involves the new M2C2 Command center of Michigan Medicine. Our team has developed with M2C2 the optimization component of the decision support for assigning a bed to a new patient seeking admission. The group has collaborated with other faculty to optimize when to monitor (e.g., take tests) and how to treat patients with a chronic disease?
The student involved in this research will do the following:
development, validation and implementation potentially including:
- Data organization
- Investigation of an accurate understanding of the proper understanding of the data employed in models
- Data analytics and mining
- Development and coding of utilities and algorithms
- Development of simulation models is possible.
No prior medical/healthcare experience required. Good communication and programming skills and some multivariate regression or machine learning background (or willingness to acquire those skills) are assets.
IOE Project 7 (for 1-2 students)
Fast, Adaptive and Scalable Stochastic Optimization Algorithms for Deep Learning
Faculty Mentor(s): Albert Berahas, email@example.com
Research Mode: In person
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.
IOE Project 8 (for 1-2 students)
Computational Cognitive Modeling
Faculty Mentor(s): Yili Liu, firstname.lastname@example.org
Research Mode: Hybrid
SURE students will work with Professor Yili Liu and graduate students to develop and test computational models of cognitive human performance and human-machine interaction. Excellent Python programming skills are required; experience with SimPy, TkInter, MatPlotLib, and course work in IOE474 simulation are all desirable though not necessarily required depending on the students’ other skills and backgrounds.
IOE Project 9 (for 1-4 students)
Cultural Aspects of Systems Engineering and Design
Faculty Mentor(s): Yili Liu, email@example.com
Research Mode: Hybrid
SURE students will work with Professor Yili Liu and graduate students to study some cultural issues in systems engineering and design by literature review, data collection and analysis, and/or controlled lab experiments. Proficiency in two or more languages (I mean human languages for this project, not Python or Cobra…) are also desirable, though not necessarily required. Meticulous attention to details and excellent writing and data organization skills are essential. Excellent GPA and cultural interests and experiences are desirable.
IOE Project 10 (for 1-3 students)
Quantifying human performance for operational environments
Faculty Mentor(s): Leia Stirling, firstname.lastname@example.org
Research Mode: In-person/In-Lab
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 upper extremity exoskeletons designed to support overhead work and/or reaching and grasping.
- Lower Extremity Exoskeletons for mobility assist: In this project, the student may support the development of a study and/or data analysis related to lower extremity exoskeletons designed to assist ankle motion for mobility assistance.
- Quantifying Human Motion. In this project, the student will support the development and evaluation of algorithms for using inertial measurement units (IMUs) to measure human performance.