Vehicle automation is developing at a rapid rate worldwide. While fully autonomous vehicles will be pervasive on the roadway for the next several years, many research initiatives are currently underway to understand and design approaches that will make this technology a future reality. This work ranges from the development of sensors and controls algorithms, to schemes for networks and connectivity, to the creation of in-vehicle driver interfaces. Here, one component that is key to the effective design of next-generation autonomous driving systems is the human driver and, thus studying human-vehicle interactions and defining driver’s roles/tasks will be important.

The goal of this project is to describe and measure the ways in which a person interacts with advanced vehicle automation. Students will assist with multiple activities and will learn a combination of the following: how to a) develop/code advanced driving simulation scenarios, b) collect driving performance data, c) analyze driver and performance data (using methods via software packages), and d) write technical reports and/or publications. Students may also gain experience collecting and analyzing complementary physiological measures, such as eye movement data, brain activity, skin conductance, and heart rate. The students will work closely with graduate student mentors to enhance learning.

Big Data/Machine Learning, Learning and Evaluation, Other

• No Major Restriction

Human Factors, Matlab, transportation, some experience in statistics, some computer programming and machine learning experience (in any language)

Industrial Engineering

Brandon Pitts

The emergence of the Internet of Things (IoT) has transformed our world with billions of interconnected smart devices. It has received ubiquitous adoption in numerous industries such as healthcare transportation, manufacturing, and agriculture. Most IoT systems nowadays are empowered by AI technology to automate a lot of tasks with little human intervention. However, designing and customising IoT devices for individual needs still remains challenging for lots of end users with limited technical background , which greatly hinders the IoT’s mass adoption. In order to bridge this gap and lower the entry barrier, we plan to take full advantage of the immersive technology(AR/VR), which allows common users to create, author and debug IoT behaviours effortlessly inside a simulated virtual environment. Specifically, the undergrad will participate in design and fabrication of unique IoT devices for a variety of tasks and integrating them to an overarching virtual environment. The undergrad will also get involved in the user evaluation process. This work will eventually lead to the submission of a paper to a top-tier ACM conference.

Deep Learning, Human Factors, Internet of Things, Learning and Evaluation

Applicants should have a general interest in developing mixed-reality applications and designing electronic hardware. Applicants with experience in some (not all) of the following are preferred: PCB design, embedded programming, C#, Unity,3D-CAD Software, deep learning.

Electrical and Computer Engineering

Karthik Ramani

High physical and cognitive workload among surgeons and nurses are becoming more common. The purpose of this project is to examine the contributors to these and develop technology to understand and enhance their performance.

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.
More information: https://engineering.purdue.edu/YuGroup

Big Data/Machine Learning, Human Factors, Learning and Evaluation, Medical Science and Technology

• No Major Restriction
• Industrial Engineering
• Computer Science
• Biomedical Engineering

Human Factors, Machine Learning, Sensors, Programming

Industrial Engineering

Denny Yu

Welding is a skill that requires manual dexterity, adept psychomotor skills, and attention to numerous details of the process. Virtual Reality (VR)-based simulators for welding have gained popularity in recent decades and have been integrated with in-person training methods to provide hands-on practice to learners for improving the necessary psychomotor skills. Previous research studies have shown that respondents agree with using VR-based welding simulators as a tool to develop basic welding skills in new trainees. Using a VR welding simulator, the trainee’s understanding was much clearer when doing the welding process, and welding skills also developed. In addition, the simulator helps the trainee redo the exercises without considering the wastage of workpiece material and access to other equipment. Considering these advantages offered by VR-based training methods, our research would focus on the systematic study to explore and evaluate the usage of the technology to facilitate user experience and the development of psychomotor skills required for welding. The student researcher would be needed to help conduct experiments with field subjects. He/she would collect data during the investigations and later help perform statistical analysis of the data. This work will eventually lead to submitting a paper to a top-tier ACM conference.

Human Factors, Internet of Things, Learning and Evaluation, Other

• Mechanical Engineering

Applicants with experience in the following are preferred (But not necessary) : Unity, 3D-CAD Package, Conducting experiment and data collection, Statistical Analysis

Mechanical Engineering

Karthik Ramani

Since the popularization of handheld communication devices and social media applications, opinion dynamics and social networks have played a more critical role in politics, economics, and public health issues. In particular, opinion polarization on vaccination has tolled thousands of lives in the recent pandemic. Consider the following question: "If you could only convince three nodes in a social network to get vaccinated, which nodes should you choose?"

This project will guide students to answer this resource allocation problem through analyzing the spread transmission network and the dynamic opinion network. The project will be composed of four parts:
1. Constructing epidemic spread simulators.
2. Designing a control strategy for epidemic mitigation.
3. Developing mathematical proofs which guarantee the algorithm's performance.
4. Applying the strategy to real networks generated from online COVID data as a case study.
Students who participated in the project will learn the basics of the epidemic modeling paradigm, network science, control theory, and Python/MATLAB programming skills.

Big Data/Machine Learning, Engineering the Built Environment, Learning and Evaluation, Medical Science and Technology

• No Major Restriction

Elmore Family School of Electrical Engineering

Philip E. Paré