Research Projects for Summer 2022

Duke's Pratt School of Engineering is offering research experience opportunities across each its four academic departments:

Grand Challenge REU participants have the opportunity to conduct research across a large spectrum of interdisciplinary topics broadly organized into five areas: energy, environment, health, national security, and learning—

Five topic areas of Grand Challenges REU program

The following is a list of research projects available during the summer 2022.

More projects will be added soon.

Assessing neural network and image labeling brittleness 

Grand Challenge: Enhance Virtual Reality

Faculty Mentor: Mary Cummings , Professor in the Department of Electrical and Computer Engineering

As artificial intelligence is increasingly used in safety-critical applications like self-driving cars and air taxis, there is a critical need to determine how errors or inconsistencies in image labelling affect neural network performance. To this end, this project will examine how problems in machine and human labelling affect neural network metrics and determine what could be done to mitigate resulting problems. Applicants should have taken at least one introductory course in machine learning. 

Computational Design of New Materials for Solar Energy Harvesting

Grand Challenge: Make solar energy more economical

Faculty Mentor: Volker Blum , Associate Professor in the Department of Mechanical Engineering and Materials Science

The Blum group offers an active research environment in computational materials science, addressing topics ranging from simulations of materials for energy research (e.g., new candidate photovoltaic absorber materials and materials facilitating stable, non-toxic devices based on new absorbers[15]) to development of new methods and computer software for materials based on the first principles of quantum mechanics. The group currently consists of two post-doctoral researchers and five Ph.D. students. An undergraduate researcher would interact closely with these experienced group members in addition to Prof. Blum himself. Prof. Blum co-founded and leads the internationally developed FHI-aims software for computational materials science. FHI-aims are an efficient, computer program package developed to predict the properties of condensed matter based on first principle, description of the electronic structure. Undergraduate researchers will benefit from the group’s embedding into an international community (Berlin, Munich, London, Beijing, Helsinki, and others) of other leading groups in computational materials science. It is important to note that students from engineering disciplines may not have previous experience with quantum mechanics, and undergraduate projects do not necessarily require such experience. The ideal candidate would have some programming experience, a keen interest in computational materials science; and they should not be shy to engage and solve computational problems that arise during their research by programming (e.g., using scripting languages like python or high-level programming languages).

Energy-Related Materials and Device Characterization for Thin-Films Deposited by Resonant-Infrared Matrix-Assisted Pulsed Laser Evaporation (RIR-MAPLE)

Grand Challenge: Make solar energy more economical

Faculty Mentor: Adrienne Stiff-Roberts , Jeffrey N. Vinik Professorof Electrical and Computer Engineering

The RIR-MAPLE technique was developed by the Stiff-Roberts group with the objective of reducing guest material degradation during deposition by exciting specific molecular vibrational bond stretches in the host material via infrared radiation[14]. RIR-MAPLE is a laser-based thin-film deposition technique appropriate for organic and hybrid organic-inorganic materials, including polymers, colloidal quantum dot/polymer hybrid nanocomposites, and hybrid perovskites. These materials are investigated for optoelectronic and energy-related devices, such as light emitting diodes, solar cells, and supercapacitors. In this project, the student will investigate the materials properties and device performance of organic/hybrid materials deposited by RIR-MAPLE using atomic force microscopy, UV-visible absorption spectroscopy, photoluminescence spectroscopy, external quantum efficiency, solar cell fill factor measurements, or light emitting diode luminance measurements.

Engineering new nanomedicines: Lab automation and machine learning to predict protein-nanoparticle interactions

Grand Challenge: Engineer better medicines

Faculty Mentor: Christine Payne , Associate Professor in Mechanical Engineering and Materials Science

Nanoparticles used as nanomedicines encounter a complex of blood serum proteins that adsorb on the surface of the nanoparticle. This adsorbed “corona” of proteins leads to the clearance of nanomedicines from circulation before they can reach their target, instead accumulating in the liver and spleen. Currently lacking is the ability to predict what proteins will adsorb on the surface of a nanoparticle. As a result, each new nanoparticle and surface modification requires a series of expensive and time-consuming experiments to identify the adsorbed proteins. Our goal is to use machine learning to predict what proteins will interact with a nanoparticle surface as a function of surface properties. This research project will generate a large data set of protein-nanoparticle interactions using a robot to automate experiments for the necessary throughput. The research team will use this library, which will be publicly available, to determine which proteins adsorb on the surface of nanoparticles as a function of nanoparticle diameter and surface properties and then use this library for subsequent cellular experiments. The use of controlled nanoparticle parameters, in combination with automated sample preparation, will enable the first quantitative understanding of protein-nanoparticle interactions, important for environmental and industrial nanoparticle exposure, as well as therapeutic and diagnostic applications. This research also provides an ideal training platform for students to address fundamental questions of nanoscience with advances in research automation, relevant to future academic or industry jobs. REU students participating in this project will work as part of a team consisting of chemists, materials scientists, and data scientist. They will learn a range of skills (dynamic light scattering, nanoparticle functionalization, gel electrophoresis, proteomics and python).

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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 developing and experimenting with different augmented reality applications, experiences, and platforms; 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, a background in communications and/or networking (preferred but not required), and familiarity in machine learning (preferred but not required).

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Modeling and Simulation of Laser Lithotripsy

Grand Challenge: Engineering better medicines

Faculty Mentor: John Dolbow , Professor in the Departments of Mechanical Engineering and Materials Science, Civil and Environmental Engineering and Mathematics

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 as shown on Figure 1. The process involves a strong coupling between fluid dynamics, thermo-mechanical loading, and fracture. A current focus concerns optimizing the treatment protocol 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.  


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 and animal brain high resolution diffusion tensor imaging. The goal is to build predictive models to help understand human neurological conditions, with a focus on neurodegeneration, and Alzheimer's disease. The students will use R, MATLAB, or python programming for model building and graph analyses (or his /her choice of a programming language), develop deep learning approaches, 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. 

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 neuro-devices. Previous experience with writing computer code is extremely helpful.