Research Opportunities

Experience: SCARF: Summer College of Agriculture Research Fellowship

Find your passion in undergraduate research! The Summer College of Agriculture Research Fellowship (SCARF) is designed to expose you to a variety of research fields throughout the College of Agriculture.

SCARF is a summer program that is open to Purdue College of Agriculture undergraduate students. This fellowship is a paid, 10 week program. Students experience in-depth, hands-on research, participate in a series of science communication workshops, attend faculty seminars, and industry tours.

Find out more information and apply here: https://ag.purdue.edu/department/oap/cate/research/scarf.html

Not required, but it is recommended that students have a faculty mentor already.

Campus: West Lafayette

Number of Students Needed: 20

This experience will occur: May, June, July

Send resumes to: Ms., Elizabeth Byers, cate@purdue.edu

Experience: Sleep: Explorations in Neurodevelopment and Neurodegeneration

Description: Within the Sleep and Developmental Studies Laboratory (https://hhs.purdue.edu/sleep/), students will assist with data collection, coding, cleaning, analysis prep, and literature reviews. Current studies focus on sleep and neurodevelopmental disorders (ASD, ADHD, Lowe Syndrome, Fragile X, Angelman Syndrome) and neurodegenerative disorders (Alzheimer's Disease).

Campus: West Lafayette

Number of Students Needed: 2

This experience will occur: May, June, July

Send resumes to: Dr. A.J. Schwichtenberg, ajschwichtenberg@purdue.edu

Experience: Building Paths to Better Futures for Youth of Latine Rural, Farm Worker, or Agricultural Families

Description: The Purdue Puentes Project is a longitudinal study of the health and well-being of Latine youth (ages 10-15) in rural, farm worker, or agricultural worker families funded by the National Institute on Minority Health Disparities (NIMHD). The project is headed by Dr. Yumary Ruiz in the Department of Public Health and Dr. Zoe Taylor in the Department of Human Development and Family Science at Purdue University.

Campus: West Lafayette

This experience will occur: May, June, July, August

Send resumes to: Project Manager, Kleyton Lentz, lentz7@purdue.edu

Experience: Supporting high-intensity interval training with mindfulness for cognitive enhancement in children.

Description: The Physical Activity and NeuroCognitive Health (PANCH) lab is conducting a research project to investigate the efficacy of combining high-intensity interval training (HIIT) with mindfulness for enhancing children’s cognition. Specifically, this project includes five laboratory visits. The visit one includes collecting data about child participants’ demographic information, physical activity experiences (i.e., sport, exercise), and physical abilities such as motor competence and aerobic fitness. During each of the visit 2-5, child participants will complete a single session of 20-min activities, including (1) mindful HIIT, (2) HIIT, (3) mindfulness, and (4) seated rest. Before and after each activity session, child participants will complete a series of cognitive tasks while their brain activation is recorded using an electroencephalogram (EEG) system. The goal of this project is to determine the best practice method to integrate mindfulness into exercise to boost children’s brain and cognitive function. Our lab utilizes a multidisciplinary approach combining kinesiology, psychology, and neuroscience to better understand how we can use exercise as a strategy to optimize childhood cognitive function. Students will have opportunities to learn skills, including but not limited to: (1) administer exercise/resting metabolic test (i.e., VO2max), (2) assess cognitive function and brain activities using an electroencephalogram (EEG) system, (3) deliver different modes of exercise interventions, (4) perform data reduction and statistical analysis, (5) present research findings in research conferences, (6) recruit and interact with child participants and their parents, and (7) collaborate with a group of research personnel.

Campus: West Lafayette

Number of Students Needed: 1

This experience will occur: May, June, July, August

Send resumes to: Assistant Professor, Alvin Kao, kao28@purdue.edu

Experience: Reconstructing life and death in ancient Nubia

Description: This project will focus on ostelogical data collection and analysis focused on understanding health experiences during life and treatment during death for individuals who lived in the past Egyptian/Nubian community of Tombos, Sudan. Specific projects can be geared towards student interest. The goal of the summer research is a data collection and analysis project that will result in a conference presentation and coauthored publication.

Campus: West Lafayette

Number of Students Needed: 2

This experience will occur: May, June, July, August

Send resumes to: Professor, Michele Buzon, mbuzon@purdue.edu

Experience: Predicting the Impacts of Artificial Intelligence on Workers: A Systematic Literature Review

Description: This research project aims to synthesize knowledge on how AI and automation impact workers and the labor market. As part of a two-university team, you'll dive into a systematic review of academic literature from 2001 to 2024, exploring various predictions and real-world impacts of AI on job gains, losses, and quality (such as worker stress or well-being). You'll actively participate in developing a conceptual framework to assess these studies and distinguish between different methodologies and assumptions. This involves qualitatively coding papers, analyzing data, and contributing to a comprehensive overview that will guide evidence-based policies and practices. Moreover, your work will help create a public-facing web portal, making this crucial information accessible to a broader audience. Join us to make a tangible impact on the future of work discussions and AI governance!.

Campus: West Lafayette

Number of Students Needed: 2

This experience will occur: May, June, July, August

Send resumes to: Dr. Daniel Schiff, dschiff@purdue.edu

Experience: Understanding Native Copper Innovation in the North American Arctic and Subarctic

Description: This project will examine the innovation of native copper metallurgy among various Hunter-Gatherer groups in the North American Arctic and Subarctic beginning approximately 2,000 years ago. A GIS consisting of archaeological sites relevant to this study has already been built. The next steps include: 1) classifying artifacts using an existing typology and 2) using GIS to analyze copper artifact variability across time and space to better understand the social connections that might have facilitated the spread of this technology. The goal of the summer research is to build on current efforts and perform analyses that will result in a conference presentation and coauthored publication.

Campus: West Lafayette

Number of Students Needed: 1

This experience will occur: June, July, August

Send resumes to: Associate Professor, H. Kory Cooper, hkcooper@purdue.edu

Experience: AI-Powered Human Action Decomposition in AR-Assisted Safety Training

Description: This study explores and identifies how workers use personal protective equipment (PPE). To achieve these objectives, a human action decomposition (HAD) framework will be proposed that uses deep learning algorithms to categorize similar poses of workers into identical groups. Then, a two-step clustering analysis will be conducted using the proposed HAD framework in conjunction with a human action dataset to identify representative PPE-related safety actions of workers.

Campus: Indianapolis

Number of Students Needed: 2

This experience will occur: May, June, July, August

Send resumes to: Assistant Professor of Practice, Kwonsik Song, kssong@purdue.edu

Experience: Processing and Characterization of Different Types of Cough Sounds

Description: Different respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, and coronavirus-caused diseases, e.g., COVID-19, SARS, and MARS, have some common symptoms, such as coughs and breathlessness. However, survey-based traditional approaches, such as the Leicester Cough Questionnaire (LCQ), Cough-Specific Quality-of-Life Questionnaire (CQLQ), and COPD Assessment Test (CAT) used for disease assessment often suffers from recall burden, human errors, and biases. With the advancement of smartphone sensing and artificial intelligence (AI), we can detect coughing patterns from smartphone microphone audio signals. Thereby, this smartphone sensing can help us to automate the disease symptom reporting process and enhance patient-physician communication. Therefore, it is important to assess different types of coughs obtained from healthy people as well as patients with different respiratory diseases, such as COPD and COVID-19. Working with an interdisciplinary research team, in this project, students will first process cough audio recordings obtained from different sources and then, they will visualize and compute different properties of those cough sounds. During this project, student researchers will be closely guided in every step, including problem formulation, data processing and characterizations, statistical analysis and data visualization, and interpretation of findings. Thereby, participating in this project, our student researchers will achieve technical expertise to solve real-world problems.

Campus: West Lafayette

Number of Students Needed: 2

This experience will occur: May, June, July ,August

Send resumes to: Assistant Professor, Sudip Vhaduri, svhaduri@purdue.edu

Experience: Soft-Biometric-based IoT Authentication

Description: With the emergence of the internet of things (IoT) and recent advancement of smart sensing technology, smartphones and wearables, such as Fitbits, are packed with a range of sensors that can help to keep track of our health and fitness, unlock cars and homes, validate and complete financial transactions, among several other services. Often these services are delivered based on users’ personal information. However, due to size and computing limitations, traditional authentications using face recognition, irish scan, and electrocardiography (ECG) signals are not convenient for market wearables. Therefore, it is crucial to develop a user authentication that can validate a user utilizing the user’s less informative coarse grained data collected by personal devices, such as smartphones and wearables, and a multi-modal data fusion technique. Working with an interdisciplinary research team, in this project, students will first process various types of data, e.g., heart rate, gait, breathing sounds obtained from smartphones and Fitbits. Then, students will visualize and compute different features. Finally, students will develop machine learning models to authenticate a user. During this project, student researchers will be closely guided in every step, including problem formulation, data processing and characterizations, statistical analysis and data visualization, machine learning model development, and interpretation of findings. Thereby, participating in this project, our student researchers will achieve technical expertise to solve real-world problems..

Campus: West Lafayette

Number of Students Needed: 2

This experience will occur: May, June, July, August

Send resumes to: Assistant Professor, Sudip Vhaduri, svhaduri@purdue.edu

Experience: Detect Healthy and Unhealthy Coughs Using Machine Learning Models

Description: While millions of people are dying due to coronavirus-caused COVID-19 disease and other respiratory diseases, including asthma, and COPD, developing predictive machine learning models that can detect healthy and unhealthy coughs from audio recordings of a person can be very useful to develop artificially intelligent (AI) systems to objectively report cough symptoms to physicians even in a remote set up. While developing these predictive models, it will be crucial to validate them across multiple datasets. These models will process data securely and protect people’s privacy. Thereby, these secure machine learning models and their AI applications can help to set up a bridge between healthcare providers and their patients remotely. Thereby, the proposed predictive models have a long-term impact by minimizing people’s suffering and improving their quality of life across the globe. Working with an interdisciplinary research team, in this project, students will first process audio recordings obtained from smart wearables. Then, students will visualize and compute different features. Finally, students will develop machine learning models to detect healthy and unhealthy coughs. During this project, student researchers will be closely guided in every step, including problem formulation, data processing and characterizations, statistical analysis and data visualization, machine learning model development, and interpretation of findings. Thereby, by participating in this project, our student researchers will achieve technical expertise to solve real-world problems. The students will also get a clear idea of how their scientific discovery contributes to the entire community in terms of developing new sustainable global health solutions that are wide-scale adaptable at a low cost.

Campus: West Lafayette

Number of Students Needed: 2

This experience will occur: May, June, July, August

Send resumes to: Assistant Professor, Sudip Vhaduri, svhaduri@purdue.edu

Experience: Toward Identifying A User from Missing IoT Biometrics

Description: With the emergence of the internet of things (IoT), smart sensing devices ranging from smart wearables, such as Fitbits or Apple watches, to smartphones are packed with a range of sensors helping us with a range of services from unlocking cars and homes to validating financial transactions, among several other services. But, often these services are delivered based on a user’s sensitive personal information, including demographic identity, and various biometric data, ranging from heart rate to breathing patterns. Therefore, it is important to understand how missing biometric samples can be fatal to predict a user’s identity and his/her entire cyber-physical space. This project will utilize machine learning and data fusion techniques on wearable and smartphone data to predict a user’s identity to better understand possible risks and foster global security. Working with an interdisciplinary research team, in this project, students will first process various types of data, e.g., heart rate, gait, and breathing patterns, among several others obtained from smartphones and smart wearables. Then, students will visualize and compute different features. Finally, students will develop machine learning models to authenticate a user. During this project, student researchers will be closely guided in every step, including problem formulation, data processing and characterizations, statistical analysis and data visualization, machine learning model development, and interpretation of findings. Participating in this project, our student researchers will achieve technical expertise to solve real-world problems. The students will also get a clear idea of how their scientific discovery contributes to the entire community in terms of securing their cyber-physical space.

Campus: West Lafayette

Number of Students Needed: 2

This experience will occur: May, June, July, August

Send resumes to: Assistant Professor, Sudip Vhaduri, svhaduri@purdue.edu

Experience: Toward understanding pilots’ stress through literature review and data analysis

Description: While flying in the sky is thrilling and exciting as a student pilot, it also leads to physiological arousal and stress due to flying a plane through life-risk situations, involving maneuvers, weather, etc. during training. Therefore, there is a need for a thorough understanding of the existing works in the area and the need to conduct a detailed study with detailed analysis to compare the stress of pilots and other occupations. During this project student researchers will work to generate insights from collected data to bridge between the literature and the future.

Campus: West Lafayette

Number of Students Needed: 2

This experience will occur: May, June, July, August

Send resumes to: Assistant Professor, Sudip Vhaduri, svhaduri@purdue.edu

Experience: Modeling, Control, and Simulation of Large-Scale Inverter-Based Resources

Description: In this research project, students will collaborate with the PI and senior PhD students to develop a scalable framework for modeling, control, and simulation of large-scale, grid-interactive, inverter-based resources. They will engage with both academic and industry partners, internally and externally, to design algorithms and use cases related to this topic.

Campus: West Lafayette

Number of Students Needed: 2

This experience will occur: May, June, July, August

Send resumes to: Associate Professor, Xiaonan Lu, lu998@purdue.edu