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

Industrial Engineering, Mechanical Engineering, and/or Computer Science Engineering

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

Industrial Engineering

Brandon Pitts

Teamwork is an essential competency highly valued by both academia and industry, especially for engineers who usually work in a small group. With tens of years' development, our research group, the Comprehensive Assessment of Team Member Effectiveness (CATME), had collected millions of survey data, including peer comments. The selected SURF student will join our research group to assist with data cleaning, preparation, and analysis for educational or technical research related to teamwork, and perhaps NLP (NLP is not necessary but a plus).

Big Data/Machine Learning, Deep Learning, Learning and Evaluation, Other

ECE, CS, IE, education, social science, management, linguistics, and others

data analysis experience with R, Python, and etc.; familiar with NLP and software programming would be a plus.

Engineering Education

Matthew Ohland

By the end of 2020, the COVID-19 infection has caused more than one million deaths and a large amount of financial loss globally. To reduce the losses, social planners are implementing appropriate methods to mitigate the spread of the epidemic, such as developing vaccines, maintaining social distancing, quarantine, investing in effective medicines, etc. Meanwhile, each type of mitigation method has different costs and the decision-makers often have to carry out the policies under limited budgets. Our plan is to design epidemic mitigation methods with limited resources.

In the project, the students will participate in designing a dynamic epidemic model for COVID-19 spreading in a community. Further, the students will fill in the role of a decision-maker of the community. Given a restricted budget, the students will try to alternate the system parameters which correspond to actions such as allocating medical equipment, imposing lock-down, and distributing vaccines, so that the virus will be eradicated quickly. Once the virus is eradicated, we will study how to prevent the occurrence of subsequent waves with relatively moderate policies. Furthermore, we will extend the problem to study how to mitigate the spreading of the virus with the lowest budget possible. The students will learn to apply geometric programming ideas to solve these problems.

Learning and Evaluation, Other

Preferred: Mathematical background, programming skills, data processing experience

ECE

Philip Pare

Engineering programs provide a unique pathway for learners to reassert control over their environment, demonstrate agency and decision making, build strong social connections, and take on crucial roles in their communities, all while developing complementary professional (“21st century”) skills. We have demonstrated through our Localized Engineering in Displacement (LED101) course that authentic engineering learning opportunities can serve as a vehicle for community development while simultaneously expanding the representation of engineers to explicitly include marginalized, displaced learners. The course has run multiple times, each cohort with a central “authentic” (real-world) challenge that is the context for all learning activities. The class for which our undergraduate researcher would develop activities, assess the implementation process, and study the impact will offer as its “authentic problem” the need for students to design, build, optimize, and implement a solar-powered lighting solution for girls, mothers, and the community studying at home during COVID in Senegal.

Energy and Environment, Learning and Evaluation

EEE

interest in engineering education, fluency in French, experience with sustainable/renewable energy solutions

ENE

Jennifer DeBoer

Social media has significantly increased the rate at which news spreads through the population, enabling shifts in people’s beliefs towards the news. One such example is the disagreement on the severity of the disease over different communities during the COVID-19 pandemic. The contention over COVID-19 affects people’s attitudes and behaviors towards the policies and suggestions from the government and scientific institutions, respectively. Our question is if it is possible to mitigate the spreading of the epidemic by impacting the opinions over the social networks. Our proposed solution is to capture the opinions of the COVID-19 pandemic through dynamical social networks with both cooperative and antagonistic interactions. We will validate the network model with social network data. Through the data-based model, we will explore the role of opinion dissemination on epidemic spreading in reality. The undergraduate researchers will learn to model signed social networks via the opinions on COVID-19. The students will gain fundamental knowledge in systems and control, social network modeling and analysis, and hands-on experience in data collection, analysis, and model validation.

Big Data/Machine Learning, Learning and Evaluation, Other

Preferred: Mathematical background, programming skills, data processing experience

ECE

Philip Paré

COVID-19 has been a major challenge in the year 2020 and the epidemic modeling community has yet to come up with an accurate and reliable method for epidemic spread prediction. Some difficulties of the epidemic spread prediction problem include testing delays, testing inaccuracy and feedback effects from local health authorities’ disease mitigation policies. These complexities in the dataset will lead to inaccurate prediction and poor disease mitigation strategies if not resolved properly.

There are abundant well-organized Covid-19 datasets available online, including the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. By leveraging these datasets, we plan to design a project-based learning experience that participants will model and predict epidemic spread over a nine-week schedule. The project includes five major stages: 1) data collection, 2) model selection, 3) parameters optimization, 4) model verification, and 5) prediction. The participants will learn to model and analyze epidemic processes with compartmental models, and they will get the first-hand experience using a programming language of their choice to implement the modeling, optimization, and prediction pipeline.

Big Data/Machine Learning, Learning and Evaluation, Other

Preferred: Mathematical background, programming skills, data processing experience

ECE

Philip Paré

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.

Big Data/Machine Learning, Learning and Evaluation, Medical Science and Technology, Other

Industrial Engineering, Computer Science, Biomedical Engineering

Human Factors, Machine Learning, Sensors, Programming

Industrial Engineering

Denny Yu

Computer programs are difficult to evaluate due to the large number of possibilities. Existing evaluation systems are restricted to simple programs or impose restrictions to limit possibilities. This project aims to build an online assessment system that can evaluate non-trivial programs and assist students learning computer programming.

Big Data/Machine Learning, Cybersecurity, Deep Learning, Learning and Evaluation

computer engineering, computer science, electrical engineering

at least two courses on computer programming

Electrical and Computer Engineering

Yung-Hsiang Lu