Research Projects

Projects are posted below; new projects will continue to be posted through February. To learn more about the type of research conducted by undergraduates, view the 2018 Research Symposium Abstracts.

2019 projects will continue to be posted through January!

This is a list of research projects that may have opportunities for undergraduate students. Please note that it is not a complete list of every SURF project. Undergraduates will discover other projects when talking directly to Purdue faculty.

You can browse all the projects on the list or view only projects in the following categories:

Industrial Engineering


Human Factors Considerations: Older Adults and Autonomous Vehicle Systems

Research categories:  Computer Engineering and Computer Science, Industrial Engineering, Innovative Technology/Design
School/Dept.: Industrial Engineering
Professor: Brandon Pitts
Preferred major(s): Industrial Engineering
Desired experience:   Human Factors, Matlab, Transportation, some experience in statistics, some computer programming experience (in any language)

Automobiles are becoming increasingly autonomous. At the same time, the demographics of drivers using these advanced vehicles is changing. In particular, adults aged 65 years and older are the fastest growing age group worldwide and are expected to benefit from vehicle automation. However, age-related perceptual and cognitive difficulties may limit the extent to which these systems are useful for individuals in this age category. The goal of this project is, therefore, to quantify interactions between (older adult) drivers and autonomous driving systems in order to develop approaches that enhance roadway safety for various aging populations.

The SURF student will assist with collecting and analyzing data from human-subject experiments (using a laboratory driving simulator) and with writing any project publication. In addition, the student will meet regularly with faculty and graduate mentors to communicate his/her progress.


Illumination of Damage through Microtomography

Research categories:  Aerospace Engineering, Computer Engineering and Computer Science, Industrial Engineering, Material Science and Engineering, Mechanical Engineering
School/Dept.: Aeronautics and Astronautics
Professor: Michael Sangid
Preferred major(s): AAE, ME, MSE, EE, CSE, or IE
Desired experience:   Students are expected to work with Image Processing and Visualization tools, as well as Matlab.

Damage in structural materials is often difficult to quantify, instead we rely on large scale component level testing and curve fitting. With the advent of advanced microtomography, we have the ability to identify damage inside the bulk of the material, in which the samples are subjected to mechanical loading. Thus, in this project, microtomography scans will be reconstructed and the damage in the form of voids or cracks will be characterized and quantified in several material systems (including carbon fiber reinforced composites and Ti-6Al-4V produced via additive manufacturing). The interaction of damage with microstructural features will be assessed, in order to achieve a physics-based understanding of material failure.


Sensing the Human Factors in Laparoscopic and Robotic Surgery

Research categories:  Bioscience/Biomedical, Computer Engineering and Computer Science, Industrial Engineering, Mechanical Systems
School/Dept.: Industrial Engineering
Professor: Denny Yu
Preferred major(s): Industrial Engineering, other
Desired experience:   Human Factors, Matlab, Machine Learning, Healthcare, Medical Device Design

Work-related musculoskeletal disorders (MSDs) among surgeons are becoming more common. The purpose of this project is to use sensors to measure ergonomic risks and assess interventions to surgeons during laparoscopic and robotic surgery. This work will leverage sensing technology (e.g., motion tracking, pressure map, electromyography) to monitor surgeons’ ergonomics to ultimately develop recommendations on minimizing MSDs and how to better design an operating room.

The SURF student will participate in data collection in the operating room at Indiana University School of Medicine, data analysis and interpretation, and write his/her results for a journal publication. The student will regularly communicate his/her progress and results with faculty, graduate mentors, and surgeon collaborators.