Research Projects

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. 

Five topic areas of Grand Challenges REU program

The following is a list of research projects available during the summer 2018. 

Computational design of new materials for solar energy harvesting

Grand Challenge: Make solar energy more economical (Energy)

Faculty Mentor: Volker Blum, Associate Professor in 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., photovoltaic materials), 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 three 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. Undergraduate researchers will benefit from the group's embedding into an international community (Berlin, Munich, London, Hefei/China, 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 and 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 (python) or high-level programming languages.

Cooperative control of robotic sensor networks

Grand Challenge: Prevent nuclear terror (Security)

Faculty Mentor: Michael Zavlanos, Assistant Professor in Mechanical Engineering and Materials Science

The Zavlanos Laboratory specializes in the area of networked dynamical systems and distributed control, with applications to robotic, sensor, and communication networks. Controlling teams of small, inexpensive and agile autonomous robots has recently become a very promising approach towards the design of more modular and robust robotic systems. The focus of this research is on networks of small, ground and aerial, mobile robots carrying specialized, inexpensive and possibly inaccurate sensors. These systems can be used for a wide range of tasks including environmental monitoring and mapping, cooperative manipulation, and search and rescue missions. While such tasks have been tested with great success in controlled lab environments, the sensing and coordination mechanisms needed for precise localization, ego-motion estimation, and control in uncertain and unpredictable environments remain underdeveloped. The goal of this project is to integrate mobility, specialization, and collaborative sensing in a distributed multi-robot system that allows for reliable and on-demand sensing accuracy as the task, e.g., the mobile target, evolves in space and time. The interested REU fellow will get involved with the development of algorithms and/or their experimental validation. They will obtain experience in controls, robotics, sensing, and/or communications, as well as in the mathematical and computational techniques required for the study of such systems.

Development of Effective Sanitation Technologies for Under-developed Areas

Grand Challenges: Provide access to clean water & Restore and Improve Urban Infrastructure (Environment)

Faculty Mentor: Jeffrey Glass, Professor of Electrical and Computer Engineering

One in three people world-wide do not have access to appropriate sanitation of human waste. This results in dirty drinking water and several million preventable deaths each year related to gastroenteritis from its consumption. This is a preventable problem, but requires rethinking how human waste should be sanitized in areas where infrastructure such as sewers, electricity and running water are not available. Our work, in collaboration with RTI International and the Bill and Melinda Gates Foundation, aims at reinventing the toilet so that it is off-grid, affordable, energy efficient, and capable of sanitizing human waste in under-developed areas.

However, malodor nuisance is a major risk factor in the adoption of effective sanitation technologies. Foul-smelling sanitation facilities persuade people to practice open defecation in developing countries. Most common odorant molecules consist of carbon backbone ending with functional groups such as aldehydes, alcohols, or ketones. Interestingly, the change of the chemical functional group on a common carbon backbone can result in dramatically different odorant perceptions, from fruity to waxy or grassy for instance. Our previous work has demonstrated the capability to modulate malodor and generate a pleasant olfactory perception simply by applying an electrical signal to the offending liquid/gas source.

The REU project will aim at expanding the family of odorants that can be treated through the modulation process. The undergraduate student will be expected to use a variety of electrochemical (electrolysis, voltammetry and electrochemical impedance spectroscopy) and physical-chemical (chromatography and nuclear magnetic resonance spectroscopy) techniques for synthesis and characterization. The student will gain knowledge in fundamental and experimental analytical chemistry and will improve her/his laboratory skills.

Development of Electrode Materials for Neural Recording and Stimulation

Grand Challenge: Reverse engineer the brain (Health)

Faculty Mentor: Jonathan Viventi, Assistant Professor in Biomedical Engineering

The Viventi lab specializes in developing high-density electrode arrays and high-channel count electrophysiology systems. This project focuses on developing implantable electrodes with a novel surface coating, which will improve recording and stimulation performance. All of the electrodes developed pass a series of stress tests to ensure their safety before being chronically implanted. New materials and electrode designs will be evaluated as part of this project. This project will also include developing implantable electrodes with a novel surface coating, which will improve recording and stimulation performance. As part of this project, the REU will monitor electrode properties while the samples are heated, as heating accelerates the material aging process. Students working in this research project will develop an understanding on electrochemistry and biocompatibility concepts. Besides gaining an appreciation for the need of reliability and attention to detail required for advanced biomedical research, the REU participant will learn about implantable device safety and materials testing. An undergraduate student with interest in electrochemistry and biocompatibility is an ideal candidate for this project. The REU will work directly with a PhD student and postdoc working in this area.

Drones and the Design of Outdoor Public Spaces

Grand Challenges: Secure Cyberspace & Restore and Improve Urban Infrastructure (Security)

Faculty Mentor: Mary "Missy" Cummings, Professor in Mechanical Engineering and Materials Science

With recent regulatory changes allowing for the use of unmanned aerial vehicles, aka drones, in commercial enterprises, many such opportunities are emerging, especially in areas that involve public gatherings like filming live music events, unique coverage of sporting events, and creating powerful imagery of landmarks or monuments. Outdoor public space managers could benefit from design guidelines and technology recommendations for systems that could detect and potentially mitigate illegal drone incursions into their spaces. Students interested in this project will join a multidisciplinary team of engineers and landscape architects, in taking a systems approach to analyzing and addressing this problem. Work will include designing detection and alerting systems, testing them and interaction with stakeholders about the designs.

Genome Engineering for Gene Therapy, Regenerative Medicine, and Functional Epigenomics

Grand Challenge: Engineer better medicines (Health)

Faculty Mentor: Charlie Gersbach, Associate Professor in Biomedical Engineering

Gersbach Laboratory Project The Gersbach laboratory is dedicated to applying genome engineering, with technologies such as the CRISPR/Cas9 system, to the development of novel approaches to gene therapy, regenerative medicine, and understanding genome structure and function (refer to figure below). A central focus of this research involves engineering CRISPR/Cas9 systems that coordinate changes in cellular gene expression or genome sequence. This work includes molecular engineering, gene delivery, and gene sequence and expression analysis with the goal of coordinating complex changes that control cell behavior. One example of this work involves using CRISPR/Cas9 systems to engineer cell types, such as stem cells or neurons to regenerate diseased or damaged tissues or generate improved models of human disease. Another example involves uring the CRISPR/Cas9-based genome editing to correct the genetic mutations associated with hereditary diseases, such as muscular dystrophy and hemophilia.

In this project, the student will be challenged to design CRISPR/Cas9 systems with advisement from the advisor and graduate students. The student will then build the DNA sequences that encode these systems, including the appropriate gene expression system. If successful, the student will have the opportunity to test the activity of the engineered protein in cultured human cells. Through this work, the student will gain expertise in important laboratory methods, including CRISPR, plasmid DNA propagation and purification, molecular cloning, and DNA recombination techniques, electrophoresis, and mammalian cell culture including genetic engineering. Additionally, they will gain exposure to the fields of epigenomics, molecular medicine, gene therapy, and regenerative medicine.

Identification of Mental Health Disorders

Grand Challenge: Reverse engineer the brain (Health)

Faculty Mentor: Guillermo Sapiro, Professor in Electrical and Computer Engineering

The extremely interdisciplinary team in the Sapiro Lab at Duke University is working on big data for early screening of mental health disorders. The interested student will join this team and help to validate and analyze the data the team is producing via very unique technologies already incorporated in pediatric clinics. The student will apply his/her background in linear algebra, and gain knowledge in signal processing and machine learning.

Modeling and Simulation of Shock-Wave Lithotripsy

Grand Challenge: Engineer better medicines (Health)

Faculty Mentor: John Dolbow, Professor of Civil and Environmental Engineering

Shock wave lithotripsy (SWL) has proven to be a highly effective treatment for the removal of kidney stones. The technique works by attempting to break up a stone through the repeated application of a focused, high-intensity acoustical pulse. Ideally, the stone fractures into sufficiently small pieces with a minimum level of tissue damage. Pratt faculty are some of the leading experts in SWL technology. A current focus concerns obtaining a detailed understanding of the mechanisms behind kidney stone fragmentation, such that the technology can be improved along with patient outcomes. This project will focus on the use of high-performance computing and model based simulations to explore how acoustical signals 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.

Optimized Parameters of Brain Stimulation

Grand Challenge: Reverse engineer the brain (Health)

Faculty Mentor: Warren Grill, Professor of 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.

Shake table testing for inelastic structural dynamics

Grand Challenge: Restore and Improve Urban Infrastructure (Environment)

Faculty Mentor: Henri Gavin, Professor in Civil and Environmental Engineering 

Rapid assessment of the integrity of infrastructure systems after hurricanes, tornadoes, and earthquakes requires a method to translate measurements obtained during the damaging event to an assessment of the damage state.  In settings where damaged elements can be hidden from view, direct visual inspection is time-consuming, expensive, and potentially inconclusive.  Recent discoveries at Duke have led to a technique to use measurements obtained from a few sensors to develop a global picture of the loads sustained by the structure.  This ability can be incorporated into a triage framework to inform decisions on urban restoration.

In this project, REU students will design 3D shake-table models in metal and using 3D printing technology.  These models will be shaken on a three-axis shaking table and dynamic responses will be measured using MEMS accelerometers and strain gages.  The objective of the project is to test methods to predict strain measurements at the base of the structure (which indicate the dynamic loads sustained by the structure) from sparse measurements of the floor accelerations.   

All federal buildings in the US, and many more within the US and around the world are instrumented with a small number of acceleration sensors. Until now the utility of these sensors has been questionable because the data analysis methods needed to assess dynamic loads on the structure from only a few acceleration measurements have been missing.  Recent research at Duke has bridged this gap and we are now ready to test our results in dynamic shake table testing. Of course, this method has application in any context in which dynamic responses are measured on a sparse grid.  

The REU student in this project will learn how to break big data down into fundamental components in order to construct dynamic responses from unmeasured locations.  The student will also be asked to study structural dynamics, signal processing, data acquisition, and dynamic measurement techniques.

Simulating the dynamics of the Earth's crust over millions of years with Finite Element Methods

Grand Challenge: Develop methods for carbon sequestration (Environment)

Faculty Mentor: Guglielmo Scovazzi, Associate Professor in Civil and Environmental Engineering

This project is about the development of algorithms and computational method for the simulation of the motion of the Earth’s crust and instabilities of salt beds over millions of years. Over such long period of time, the mechanical behavior of geologic layers behave in between a fluid and a solid. Buoyancy phenomena and instabilities can develoip, resulting in complex geometric patterns. Their numerical study is an interesting topic per se in geophysics, but can also have important implication in the prospection and discovery of oil reservoirs. In our group, advanced finite element methods are developed to address these computational challenges, which, to date, are still extremely demanding to simulate.

Versatile Extraterrestrial Locomotion for a Rock Climbing Robot

Grand Challenge: Engineer the Tools of Scientific Discovery (Learning)

Faculty Mentor: Kris Hauser , Professor in Electrical and Computer Engineering 

Robots are the future of terrestrial space exploration, but are currently limited to static probes or rovers that can traverse slightly uneven terrain. Legged robots have the potential to explore rocky, steep terrain that might be found on the mountains of Mars, craters on the moon, or the surface of comets and asteroids. Along with JPL, Stanford, and UC Santa Barbara, Duke is developing capabilities for the Robosimian quadruped robot to climb steep terrain. It has the ambitious goal of enabling the robot to autonomously climb a 5.7 Yosemite Decimal Scale difficulty climbing route. To do so, new research will need to be conducted in computer vision, mapping, end effector design, and motion planning. The ideal candidate for this position will have experience in computer vision, robotics, AI, machine learning, or a related field.