Undergraduate Research Assistantship (URA) Opportunities

Name of lead faculty member:

Cameron MacKenzie

Project title:

Integrating Simio with Python to Perform Simulation-Optimization for a Digital Twin

Desired length of time:

• 1 Semester

Research area:

• Operations Research/Analytics
• Systems Engineering/Engineering Management

Project description:

In order to be effective, a digital twin simulation should be able to conduct optimization quickly over a simulation that is updated with real-time information. Simio provides an excellent discrete-event simulation that can be updated with real-time information, but its ability to optimize over that simulation is rather limited. Python provides a simulation-optimization algorithm package called ParMOO. This project will explore the feasibility of running ParMOO from Python and then calling the Simio API in order to optimize a discrete-event simulation program in Simio. This type of simulation-optimization will allow us to calculate the Value of Information gained by using a real-time digital twin simulation

Desired student qualifications:

The student should know Simio (i.e., IE 4130 is a prerequisite). Knowledge of how to program in Python is a plus but not a requirement as long as they have a decent background in programming in R or Visual Basic. Being able to work independently and debug computer programs will be a plus. Having an understanding of how to calculate Value of Information or Value of Clairvoyance from IE 5640 will be a big plus.

Expected student tasks:

• Data analyses
• Simulation
• Modeling
• Decision analyses
• Presenting
• Integrating different software together (Simio and Python) is a major task.

Expected hours per week, per student: 

• 10

Expected project output during the semester:

• Python code on how to call Simio and some preliminary simulation-optimization results

Are there any special requirements or restrictions for URA students?

• No

Name of faculty member:

Michael Dorneich

Project title:

Investigation of decision‐making models in the adaptive behavior of pilots

Desired length of time:

• 1 Semester

Research area:

• Human Factors/Ergonomics

Project description:

This research focuses aims to evaluate the benefits of adaptive behavior models and training for pilot weather training. Of particular interest is recognition-primed decision (RPD) making, where experts match the first feasible solution. RPD is often applied to situations with time pressure, ill-define goals, and uncertainty in dynamically and continually changing conditions with high stakes. Previous research in aviation has demonstrated that pilots use traditional decision-making to handle foreseeable problems (and it works well), but RPD could have benefits in training unforeseen events in simulator training.
This semester, the project will conducting a feature review and developing an experimental approach to assess the feasibility and potential benefits of XR training scenarios focused behavior models such as RPD. XR training scenarios will be developed to include situations to indicate to pilots when they have made less-than-optimal decisions. We will develop and conduct experiments to evaluate the benefits of adaptive behavior models, such as RPD and training, for pilot weather training. The resulting work will be presented with a poster at the 2025 IMSE Student Research Symposium and submitted as a journal manuscript.

Desired student qualifications:

• Some experience in human factors, cognitive engineering, or human-computer interaction.
• Some statistical knowledge or willingness to learn
• Enjoys data-driven research
• Enjoys collecting data through human-subject experiments

Expected student tasks:

• Literature review
• Data collection
• Data analyses
• Excel
• Writing
• Presenting
• Group meetings

Expected hours per week, per student: 

• 10

Expected project output during the semester:

• Run experiments, Curate data, Analyze data, Contribute to paper

Are there any special requirements or restrictions for URA students?

• No

Name of faculty member:

Qing Li

Project title:

Software development for model assisted probability of detection

Desired length of time:

• 1 Semester

Research area:

• Operations Research/Analytics

Project description:

Nondestructive evaluation (NDE) is a critical technology in modern engineering, aimed at assessing internal defects, physical properties, and the structural integrity of materials without causing damage or compromising performance. In NDE, the probability of detection (POD) is a key statistical metric used to quantify the effectiveness of inspection procedures in detecting defects. However, obtaining sufficient measurements for traditional empirical POD estimation is frequently costly in terms of time, money, and resources. Evaluations using limited data often fail to capture the true relationship between signal response and crack size or reflect the variability in testing procedures caused by various factors and uncertainty. Furthermore, the assumptions underlying simple linear regression may be violated with insufficient samples, leading to inaccurate or imprecise POD estimates. To address these challenges, we propose to investigate multiple advanced regression methods to improve POD estimation for small samples to reduce the influence of potential assumption violations. We will also combine these regression techniques with information-augmentation methods including model-based regression or Bayesian methods to address potential assumption violations and incorporate existing information simultaneously. To benefit the broader NDE community, we will develop user-friendly software packages to implement these regression techniques for POD analysis.

Desired student qualifications:

• Have taken IE 4200/5200, Engineering problem solving using R. Is interested in data analysis and statistics.

Expected student tasks:

• Literature review
• Presenting
• Group meetings
• Software or web application development
• Assist phd students to convert code to user-friendly interfaces.

Expected hours per week, per student: 

• 10

Expected project output during the semester:

• A user-friendly interface for POD analysis

Are there any special requirements or restrictions for URA students?

• No

Name of faculty members:

Stephen Gilbert (Lead)

Michael Dorneich

Jon Kelly (Psychology)

Project title:

Cybersickness Adaptation in Extended Reality

Desired length of time:

• 1 Semester

Research area:

• Human Factors/Ergonomics

Project description:

Cybersickness, in which users of a virtual or augmented reality headset feel nauseous, headaches, and/or dizziness, is an unfortunate phenomenon that impedes the progress of widespread use of extended reality (XR), which can include VR or AR. We know some about the factors that cause cybersickness, especially the hardware factors (like latency) and the software factors (like optic flow). But, recent evidence suggests that task users are performing in VR can have a significant effect on cybersickness. This research will explore how the nature of the task affects cybersickness. Also, there’s evidence that people can adapt to the headsets. VR gamers get less sick than non-gamers, for example. We will also be studying what can trigger this adaptation.

The student on this project will aid graduate students on this National Science Foundation-funded project in collecting and analyzing data for this study. The student may review literature to help establish what cues might make people adapt, may be involved in creating the 3D Unity environment for the task, will help run participants (“Welcome to the study…”), may help create a smooth data processing pipeline using Python, and will aid in analyzing the data. The student will also be a co-author on any papers we publish. There are a variety of options for contributing to this project.

Because this project is funded by the U.S. National Science Foundation, applicants must be a U.S. citizen or permanent resident of the United States.

Desired student qualifications:

Everyone:

• Good communicator
• People skills for running participants
• Eagerness to learn more about data analysis and machine learning
• Willingness to read research papers to mine information

Some applicants should also have:

• Experience with Unity, Unreal, or Blender
• Basic python for data formatting, filtering, and analysis

Expected student tasks:

• Literature review
• Data collection
• Data analyses
• Modeling
• Excel
• Statistical analyses
• Writing
• Presenting
• Group meetings

Expected hours per week, per student: 

• 10

Expected project output during the semester:

• Data analysis, writing for a conference or journal paper, data collected

Are there any special requirements or restrictions for URA students?

• Applicants must be a U.S. citizen or permanent resident of the United States.