The Pratt School of Engineering is offering research experience opportunities across all its departments: Biomedical Engineering, Civil and Environmental Engineering, Electrical and Computer Engineering, Mechanical Engineering and Materials Science. Grand Challenge REU participants have the opportunity to conduct research in a large spectrum of interdisciplinary topics broadly organized into five areas: energy, environment, health, national security, and learning.
The following is a list of research projects available during the summer 2020.
Additional projects will be added soon.
Network based approaches to understand neurodegenerative diseases
Grand Challenge: Reverse engineer the brain
Faculty Mentor: Alexandra Badea , Associate Professor in Radiology
The goal of this project is to familiarize students with interdisciplinary approaches to understand the brain. The main thrust is on developing computational approaches using graph analysis and visualization of diffusion tensor imaging and derived tracts. We will use human brain high resolution diffusion tensor imaging. The goal is to use simple animal models to help better understand human neurodegenerative diseases, such as Alzheimer's disease. The student will use R, MATLAB, or python programming for graph analysis (or his /her choice of a programming language), and compare several options for visualizing and assessing the quality of reconstructed tractography data. I am interested in supporting the development of computational skills in engineering students, with a particular focus on supporting female students.
Drone Detection for Public Safety
Grand Challenge: Restore and Improve Urban Infrastructure
Faculty Mentor: Mary Cummings , Professor in the Department of Electrical and Computer Engineering
This project will focus on the testing and design improvements for an acoustic system that alerts critical stakeholders of possible drone threats. This will include developing human-machine collaborative approaches to machines learning. The work conducted by the REU student will include to conduct tests and assist in algorithm improvements. The ideal candidate will possess a background in python, Java or C programming languages.
Materials Informatics and Visualizations
Grand Challenge: Engineering the tools of scientific discovery
Faculty Mentor: Stefano Curtarolo , Professor in the Department of Mechanical Engineering and Materials Science
AFLOW is a software framework for materials discovery, powering aflow.org and the largest database for inorganic materials (3M+ entries, 500M+ properties). Features are employed for thermodynamic descriptor development and machine learning analyses. Such models have fueled the synthesis of new rare-earth-free magnets and high-hardness carbide systems, both materials being the first of their kind to be discovered by prediction.
Several projects are available: property-workflow development, machine learning and data-mining, creation of online applications (interactive analyses and visualizations).
For additional information, please visit http:// aflow.org .
Modeling and Simulation of Laser Lithotripsy
Grand Challenge: Engineering better medicines
Faculty Mentor: John Dolbow , Professor in Mechanical Engineering and Materials Science
Laser lithotripsy is an emerging medical procedure for the treatment of kidney stones. In this treatment, a laser at the end of a fiber-optic probe is brought in close proximity of a kidney stone. The stone is then broken up into smaller pieces as it is targeted with the laser. The process involves a strong coupling between fluid dynamics, thermo-mechanical loading, and fracture. A current focus concerns optimizing the treatment protocol so as to maximize the fragmentation of the stone with the fewest number of laser pulses possible.
This project will focus on the use of high-performance computing and model-based simulations to explore how laser pulses might be modified to increase stone fragmentation while minimizing tissue damage. The research will involve performing simulations with a state-of-the-art modeling and simulation code and calibrating results against experimental observations at Duke. The ideal candidate is familiar with math and computer science as well as solid and fluid mechanics.
Modeling the Human Voice Through 3D Printing and Simulation
Grand Challenge: Engineering better medicines
John Dolbow, Professor in Mechanical Engineering and Materials Science,
Wilkins Aquino, Professor in Mechanical Engineering and Materials Science
Dennis Frank-Ito, Assistant Professor of Surgery
Voice disorders affect a significant number of people around the world. A particular kind of voice disorder results from the thinning of the vocal cord, leading to the voice becoming weak and not well projected. The physics behind voice generation is fairly complicated, making the problem difficult to study by scientists, engineers and clinicians alike.
The student working on this project would generate a model of a flow-induced sound-generating system that mimics human voice production and can be studied in a laboratory setting. This would be accomplished by 3D printing a patient-specific human larynx, and examining how different flexible components can lead to different sound generation. Another objective would be to use these 3D printed models to validate existing computational models. The student will also work with a team of researchers in support of this project from the Pratt School of Engineering and the Duke University Medical Center.
3D Printing of Titanium and Cobalt Alloys for Orthopedic Applications
Grand Challenge: Engineer better medicines
Faculty Mentor: Ken Gall , Professor in Mechanical Engineering and Materials Science
Additive manufacturing (AM, or 3D printing) has enabled the evolution of tissue engineering scaffold designs with increased complexity at macro-scale (part scale geometry) and meso/micro-scale (lattice architecture and surface topography). Topology optimization, including architecting lattice with tunable properties for specified functionality is highly relevant to the biomedical field and structural implants. In particular, selective laser melting (SLM) has been investigated as a method to create porous titanium alloy (Ti6Al4V) and cobalt chromium (CoCr) scaffolds for orthopedic applications due to the material’s high strength, corrosion resistance, and biocompatibility. This project will focus on material and mechanical characterization of scaffolds for orthopedic applications. REU students will work with grad students and post-docs to characterize the scaffolds produced by 3D printing. Work will include sample preparation, microscopy, mechanical testing, data analysis, and literature review. The ideal candidates possess previous experience in CAD and/or 3D Printing.
For additional information, please visit http://gallgroup.pratt.duke.edu/.
Enhancing Virtual Reality: Intelligent Augmented Reality
Grand Challenge: Enhance virtual reality
Faculty Mentor: Maria Gorlatova , Assistant Professor of Electrical and Computer Engineering
Augmented reality (AR) is a rapidly developing technology area with potential for transforming many daily human experiences. While promising, current AR systems are somewhat limited in their capabilities, in particular in multi-user experiences, high energy consumption, and general lack of robustness and adaptability. The goal of this project is to obtain in-depth experimental understanding of the limitations of current augmented reality experiences, and to establish how these limitations can be addressed.
Students involved in this work will experiment with develop and experiment with different augmented reality applications, experiences, and platforms, in order to understand the systems and network loads of different operations, key drivers of immersive user experiences, and the potential of edge and cloud computing platforms to address the discovered limitations. The project involves both the development of Unity-based holographic experiences, and real-world holographic deployments with Google ARCore mobile device platforms and Magic Leap One headsets.
The project is best suited for students who have an experimental mindset and who enjoy obtaining in-depth understanding of system performance, physical phenomena, and human behavior. Relevant technical preparation includes: General software development skills Coursework in communications and/or networking preferred but not required Coursework in machine learning preferred but not required.
For additional information, please visit https://maria.gorlatova.com/current-research/ .
Optimized Parameters of Brain Stimulation
Grand Challenge: Re-engineering the brain
Faculty Mentor: Warren Grill , Professor in Biomedical Engineering
The Grill lab specializes in analysis and design of devices that use electrical activation of the nervous system to restore function to individuals with neurological impairment. This project focuses on design and testing of temporal patterns of brain stimulation to treat the symptoms of Parkinson’s disease. The project will make use of computer-based modeling combined with measurements of both neural activity and behavior in an animal model of Parkinson’s disease. Students working in this research project will develop an understanding of the principles of brain stimulation, the application of computer modeling to study brain stimulation, as well as practical aspects of conducting experiments using brain stimulation. Student with interest in neuroscience and medical devices are ideal candidates for this project. The students will work directly with a PhD student and Research Scientist and have the opportunity to interact with a diverse group of students and staff working across a broad array of neurodevices. The project will make use of computer-based modeling combined with measurements of both neural activity and behavior in an animal model of Parkinson’s disease. Students working in this research project will develop an understanding of the principles of brain stimulation, the application of computer modeling to study brain stimulation, as well as practical aspects of conducting experiments using brain stimulation. Student with interest in neuroscience and medical devices are ideal candidates for this project. Previous experience with writing computer code is extremely helpful.
Grand Challenge: Engineer better medicines
Faculty Mentor: Christine Payne , Associate Professor in Mechanical Engineering and Materials Science
All cells have a resting membrane potential driven by an ion gradient. We will use this parameter to control bacterial growth and biofilm formation. The use of electricity, instead of drugs, provides a new method to control these parameters. Students will work with a team to culture bacteria, etch gold electrodes, and measure bacterial growth. While no specific skills are required; students must be curious, independent, and hard working.
For more information, please visit http://payne.pratt.duke.edu/.