HIV/AIDS effects millions of people all over the world. The antiretroviral therapy used to treat HIV is effective, but HIV first must be diagnosed and then monitored to measure the treatment effectiveness to eliminate transmission to others and increase a patient’s quality of life. The Linnes Lab uses state of the art microfluidic technologies to prevent, detect, and understand the pathogenesis of diseases, such as HIV. This undergraduate summer research project will focus on developing new technology for HIV diagnostics that will also aid in diagnostics research of other bloodborne illnesses. The student will learn about biological sample preparation, nucleic acid amplification methods, microfluidic device design, fabrication, and testing, and rapid prototyping tools such as 3D printing and laser cutting. The researcher will develop a new tool for sample preparation of the blood that minimizes the number of user steps to integrate into an easy-to-use point-of-care diagnostic tool for people living with HIV to monitor their viral load within the convenience and privacy of their homes. The new tool design specifications include that it must be compatible with the smartphone imaging platform, microfluidic chip, and the HIV assay to diagnose the disease with high sensitivity and specificity.

Biological Characterization and Imaging, Fabrication and Robotics, Human Factors, Medical Science and Technology, Nanotechnology

• Biomedical Engineering
• Biochemistry
• Biological Engineering - multiple concentrations
• Microbiology

3d printing and prototyping, medical technology

Weldon School of Biomedical Engineering

Jacqueline Linnes

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

Our project is funded by the U.S. Environmental Protection Agency (EPA) and involves an interdisciplinary collaboration between engineers, chemists, and psychologists at Purdue University and New York University (NYU). We will elucidate determinants of indoor dust ingestion in 6- to 24-month-old infants (age range for major postural and locomotor milestones). Specific objectives are to test: (1) whether the frequency and characteristics of indoor dust and non-dust mouthing events change with age and motor development stage for different micro-environments; (2) how home characteristics and demographic factors affect indoor dust mass loading and dust toxicant concentration; (3) how dust transfer between surfaces is influenced by dust properties, surface features, and contact dynamics; and (4) contributions of developmental, behavioral, and socio-environmental factors to dust and toxicant-resolved dust ingestion rates. In addition, the project will (5) create a shared corpus of video, dust, toxicant, and ingestion rate data to increase scientific transparency and speed progress through data reuse by the broader exposure science community.

Our transdisciplinary work will involve: (1) parent report questionnaires and detailed video coding of home observations of infant mouthing and hand-to-floor/object behaviors; (2) physical and chemical analyses of indoor dust collected through home visits and a citizen-science campaign; (3) surface-to-surface dust transfer experiments with a robotic platform; (4) dust mass balance modeling to determine distributions in and determinants of dust and toxicant-resolved dust ingestion rates; and (5) open sharing of curated research videos and processed data in the Databrary digital library and a public website with geographic and behavioral information for participating families.

The project will provide improved estimates of indoor dust ingestion rates in pre-sitting to independently walking infants and characterize inter-individual variability based on infant age, developmental stage, home environment, and parent behaviors. Dust transport experiments and modeling will provide new mechanistic insights into the factors that affect the migration of dust from the floor to mouthed objects to an infant’s mouth. The shared corpus will enable data reuse to inform future research on how dust ingestion contributes to infants’ total exposure to environmental toxicants.

U.S. EPA project overview: https://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract_id/11194

Biological Characterization and Imaging, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Human Factors

• No Major Restriction

We are seeking students passionate about studying environmental contaminants and infant exposure to chemicals in the indoor environment. Preferred skills: experience with MATLAB, Python, or R. Coursework: environmental science and chemistry, microbiology, physics, thermodynamics, heat/mass transfer, fluid mechanics, developmental psychology.

Lyles School of Civil Engineering

Brandon Boor

Social media platforms (SMPs) are a ubiquitous and powerful technology. Their influence on society cannot be understated. However, unlike many other classes of engineered computer systems, SMPs are a bit of a “Wild West” – we lack insight into effective engineering practices to ensure proper function and safety for users. The goal of this project is to investigate the mechanisms that underlie the software engineering process of this technology. The student will investigate the difficulties faced by software engineers when building SMPs. Tasks will include analysis of issue reports regarding open-source SMPs, assistance with interview design for SMP engineers, and reporting of public failures of major SMPs.

Human Factors

• No Major Restriction

Good programming background, e.g. knows multiple programming languages including scripting languages like Python Strong English skills Curiosity about the human side of software engineering

Electrical & Computer Engineering

James Davis

Bio or physiological sensors have become ubiquitous, and this additional sensing capability is being integrated into clothing and other devices (e.g. smart watches) that we wear to constantly provide us with feedback about our health. Physiological sensors can also be used to infer mental processes occurring in the brain; this is called "psychophysiological" sensing. In our lab we are studying the use of various types of psychophysiological measurements to infer or predict human decision-making and other cognitive factors (such as their trust) during interactions with machines, robots, and other autonomous systems. This type of research is important for improving the safety and performance of autonomous systems designed to interact with or assist humans, such as autonomous vehicles, nurse robots, or surgical robots used by physicians. We are looking for a student to set up a new sensor, called functional Near-InfraRed Spectroscopy (fNIRS), for use in our lab and to test it against some prior work we've done with similar sensors to study human trust in automation.

Big Data/Machine Learning, Human Factors, Other

• Mechanical Engineering
• Industrial Engineering
• Electrical Engineering
• Aeronautical and Astronautical Engineering
• Civil Engineering

Prior experience or familiarity with sensors, hardware, and interfacing such devices with software is desirable.

School of Mechanical Engineering

Neera Jain

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

In various emerging applications of autonomy, including autonomous driving, teleoperation, and assistive robotics, a human and an autonomous system closely interact with each other. This project’s goal is to develop a mixed-reality platform to facilitate research on trustworthy human-robot interaction. The platform will enable the creation of different environments and scenarios, in which human users/operators and robots interact, to facilitate the development and training of learning-based control algorithms for autonomous systems. The undergraduate researcher will contribute to setting up the mixed-reality platform as well as the design and implementation of high-level control algorithms.

Big Data/Machine Learning, Fabrication and Robotics, Human Factors

• No Major Restriction

Electrical and Computer Engineering

Mahsa Ghasemi

The objective of this project is to utilize state-of-the-art proton transfer reaction mass spectrometry (PTR-MS) to evaluate emissions and exposures of volatile chemicals in buildings. My group is investigating volatile chemical emissions from consumer and personal care products, disinfectants and cleaning agents, and building and construction materials. You will assist graduate students with full-scale experiments with our PTR-MS in our new Purdue zEDGE Tiny House and process and analyze indoor air data in MATLAB.

Big Data/Machine Learning, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Human Factors, Internet of Things

• No Major Restriction

Preferred skills: experience with MATLAB, Python, or R. Coursework: environmental science and chemistry, physics, thermodynamics, heat/mass transfer, and fluid mechanics.

Lyles School of Civil Engineering

Nusrat Jung

There is growing interest from Space agencies such as NASA and the European Space Agency in establishing permanent human settlements outside Earth. To advance knowledge in the field, the Resilient Extra-Terrestrial Habitat Institute (RETHi) is taking steps to develop technologies that will enable resilient habitats in deep space, that will adapt, absorb and rapidly recover from expected and unexpected disruptions without fundamental changes in function or sacrifices in safety.
To study, demonstrate, and evaluate the technologies developed in pursuit of this mission, a multi-physics cyber-physical testbed is being founded at the Ray W. Herrick Laboratories at Purdue University with collaboration from partners at three universities and two industrial partners. It allows to examine emergent behaviors in habitat systems and the interactions among its virtual (computational) and physical components. The testbed will consider a habitat system and will aim to emulate the extreme temperature fluctuations that happen in deep space. To achieve this goal, a thermal transfer system is being developed, consisting of a chiller, an array of glycol lines, in-line heaters, actuated valves, and a series of sensors. Operated under a tuned controller, the thermal transfer system can cool or heat a certain surface area of the structure of the habitat to maintain a given temperature. However, to fully control the thermal transfer system is not straightforward. One of the critical challenges is its deep uncertainty, which results from inaccurate or long-delay sensors, variant test setup, complex controller design, etc. Therefore, a systematic study is needed to quantify the uncertainties to facilitate the thermal transfer system development. Emulation of a particular scenario considering a meteoroid impact will be performed, with random variations in the location and size of the impact and resulting consequences.
We also aim to consider design trade-offs aimed toward the goals of resilience. Thus, we have also established a modeling platform to support rapid, stochastic simulations of habitat systems to quantify the space architectures that enhance resilience. These might consider the important features of the robots, the sensors, and the structure itself that make the habitat resilient. Physics-Infused modeling is a gray-box method to model physical parameters using low-fidelity/computationally-efficient models in conjunction with high-fidelity/computationally-expensive samples. We combine samples from the high-fidelity model framework with low-fidelity dynamic models and create a better combination for state prediction to achieve this goal. One of the critical problems here is the difference in state space of the models and finding the optimal method to sample a high-fidelity model.
We are looking for undergraduate students to play key roles in this project, under the guidance of a graduate student and faculty members. The students are also expected to prepare a poster presentation on the results, and author a research paper if the desired results are achieved.

Deep Learning, Human Factors, Material Modeling and Simulation, Thermal Technology

• No Major Restriction
• Mechanical Engineering
• Aeronautical and Astronautical Engineering
• Civil Engineering
• Computer Engineering
• Computer Science

Students interested in this project should be critical thinkers, and have good experimental skills. Some projects will require programming skills (Python), CAD skills, and experience in MATLAB/Simulink.

Mechanical Engineering, Civil Engineering, Aerospace Engineering (we will have multiple faculty advising)

Shirley Dyke

The objective of the proposed work is to develop an in-depth understanding of the interrelationships between innovator challenges and the resources they require to succeed. This will be done by analyzing innovator stories from publicly available podcasts like ‘How I built this’, and ‘Y-combinator’ which present first-hand accounts of well-known innovator paths to success. Stories from the podcasts will be analyzed to highlight specific problems faced by innovators and the ways they addressed them. The project will involve working with a relational database and a graphical user interface to document and call out variables that are key to each studied story. These data will be analyzed to generate insights to help future innovators. The project offers the opportunity to work closely with a graduate mentor who will assist in conducting the analysis and synthesizing results to draw out insights.

Human Factors, Other

• No Major Restriction

There are no specific prerequisites in coursework or knowledge for the project. All that is needed is curiosity and enthusiasm to learn about innovation.

School of Civil Engineering

Joseph Sinfield