Summer Undergraduate Research Program

Duke ECE research experience for undergraduates (REU) brings students from around the world into the research laboratories of the Department of Electrical and Computer Engineering.

These students work with a faculty member and their research group to tackle an innovative research project. Students admitted to the program receive a competitive monthly research stipend as well as arranged on-campus housing and a travel allowance.

How to Apply

Applications for the Duke ECE Undergraduate Summer Research Program 2020 will open in late fall 2019. 


Domestic and international* students are invited to apply. The program is designed for students who are juniors in the spring before their REU summer; exceptional sophomores may receive consideration. Students should be majoring in ECE or a related discipline relevant to their area of research interest.

*Accepted international applicants must be issued an appropriate visa to participate and should be aware of requirements and policies associated with the visa. Applicants requiring J-1 visa entry can find more about the J-1 policies, including the possibility of a 2-year home-country physical residence requirement, through their university visa offices, on the Duke Visa Services webpage, or from the US Department of State.

Dates and Stipend

  • Program Dates: Late May through July (9 weeks)
  • Stipend: $500/wk
  • Travel Reimbursement: Up to $400 for domestic travel, up to $800 for international travel
  • Housing: Shared rooms at the JB Duke Hotel on Duke Campus

Research Opportunities

The following projects were available in summer 2019. 

Autonomous Early Detection of Crop Diseases Using Heavy Lift Drones

We will develop autonomous open source systems intended for low cost per acre use in early detection of crop plant pests and diseases over large areas.  Both a long range drone with multi-sample plant tissue and soil collection capability and specialized ground based pest trap units will be developed.

We have an open source hovering drone design that uses low cost PLA 3D printed parts and hobby components, but has a range of 100 km using a gasoline-electric hybrid drive.  This drone can carry a significant payload (20lb) and so can be equipped with sampling and storage devices designed to autonomously collect plant and soil samples from locations in a 100 km radius.  The drone uses AI image processing software to locate objects it knows and position the drone precisely over them for collection.

 Students can work on projects ranging from vision systems to mechanical design depending on their preferences.

Faculty Advisor: Prof. Martin Brooke (

Low-Cost Deep Water SONAR for 3D Ocean Maps

We have developed a low cost open source SONAR mapping technology for use in deep ocean water.  We would like to investigate using the SNOAR for creating 3D maps of the ocean water column including sea life.

 Students can work on projects related to the SONAR hardware and software or on the SONAR signal processing to generate 3D maps.

Faculty Advisor: Prof. Martin Brooke (

Advances in Testing and Fault Tolerance for Emerging Technologies

The slowing down of Moore's law has driven semiconductor research beyond traditional traditional integrated circuits. New materials and emerging devices are being researched  to meet the increasing demand for information processing. The Big Data explosion has necessitated advanced processors and memories for accelerating time-critical computations and expanding storage and communication bandwidth. Advanced test and fault tolerance solutions are therefore necessary for building resilience against defects in these new systems---from devices to architectures. The primary research interests of our group are in fault modeling, test, and fault tolerance of such state-of-the-art computing and memory platforms including neuromorphic (bionic) processors, ML/AI chips implementing spiking neural networks, monolithic 3D ICs, and microfluidic biochips. Research is also being carried out in the standardization of analog (mixed-signal) fault modeling and test, which is also on the industry roadmap. Eclectic research areas are driving innovation in the group, leading to the development of smart and pragmatic techniques for reliable hardware design.

Faculty Advisor: Prof. Krish Chakrabarty (

Developing an Agent-Based Model for Emergency Room Process Analysis

Emergency room operations are characterized by fluctuating demand, limited resources, and significant uncertainty across both patients and providers. In this project, students will adapt an existing process simulation to represent emergency room dynamics, including patients, doctors, nurses, technicians and the limited resources like MRIs and beds. The goal is to replicate current emergency room delays and treatments times, to better understand where and how process improvements could be made. A background in JAVA is required. 

Faculty Advisor: Prof. Mary "Missy" Cummings (

Designing an Electrical Biosensing Platform for Nanomaterial-based Devices

In our lab, we print electronic biosensors from nanomaterials. One sensing system we are currently working on is for monitoring prothrombin time (i.e., blood clot time) to help patients that require long-term use of blood thinning agents. In this project, a handheld electrical system will be designed for operating the biosensors. This includes the needed hardware (for which an example/starting platform will be provided), integration of components, design and 3D printing of enclosure, and software for controlling the unit, including the graphic user display. The student should have interest in learning about basic electronics hardware design, programming for a custom application, and biomedical technology.  Further, a student interested in the use of printed electronics and nanomaterials for biomedical sensing applications would be able to also contribute in these aspects of the project.  If successful, the handheld system may be tested on patient samples in the Duke clinic. 

Faculty Advisor: Prof. Aaron Franklin (

Exploring New Electrical Contact Interfaces to Nanomaterials 

Nanomaterials are attractive for future electronic device applications owing to their atomic thinness and unique electrical properties.  These include 1D carbon nanotubes, 2D graphene, and many other 2D crystals that are semiconducting.  One of the foremost challenges for nanomaterial-based devices is the inconsistency and relatively poor performance of the contact interfaces.  While there has been some progress in improving the metal-nanomaterial contacts, much work remains and this project will explore new approaches to establishing electrical interfaces with a variety of nanomaterials.  In this project, the student will perform work in the cleanroom here at Duke, learning the basics of nanofabrication and electrical characterization. Several new contact structures will be studied by fabricating transistors from the nanomaterials and then characterizing the resultant properties via electron microscopy, atomic force microscopy, and spectroscopy techniques.  These material characterization results will then be correlated with electrical characterization of the devices.  The student will be an active contributor to this research and also be expected to take part in discussions where results will be analyzed and new ideas potentially formulated for inclusion in the project.

The student will learn: 1) the ins and outs of characterizing semiconductor devices, 2) the basics of vacuum systems, and 3) data analysis skills related to understanding nanomaterial interfaces.  An ideal candidate for this project would have some previous knowledge and experience in solid-state physics including carrier transport in semiconductors, previous knowledge and/or interest in electronics, and be competent in operating complex tools.  They should also be self-motivated and maintain a strong work ethic in terms of commitment and follow-through.  A collaborative, team player is a must.

Faculty Advisor: Prof. Aaron Franklin (

Investigation of Broadband X-ray Phase Imaging 

Phase-imaging methods are of interest in the x-ray domain because, unlike in conventional absorption imaging, the contrast mechanism is the interference between neighboring ray-paths. Use of this mechanism increases sensitivity to low-absorption materials and expands the application areas in which x-ray-based methods have utility. Major existing approaches to x-ray phase imaging either utilize monochromatic energy sources and spatial filtering to yield a coherent illumination source, or require a complicated set of diffraction gratings to induce the required coherence. In this project, we propose to investigate to what extent the use of energy-resolving detection can allow for broadband x-ray phase imaging without the need for additional complicated elements. The project will involve experimental, analytical, and simulation-based methods.

Faculty Advisor: Prof. Mike Gehm (

Fluorescent Imaging of Neural Activity in Live Animals

Our lab is focused on using novel optical and protein tools to understand how the brain works. Recent developments in optogenetic technology has allowed researcher to record from hundreds to thousands of neurons in awake-behaving animals. For example, our have published on voltage sensors that read the spiking activity of live animals (Gong et. al. Science, 2015). We are also currently improving the fluorescent microscopes in our lab to record more neurons from deeper brain structures faster. These tools will help us better understand how collections of neurons fire and wire together to support complex behaviors. We are able to investigate these connections between neural activity and behavior in a variety of model organisms such as mice, flies, and fish.

For this position, our lab is looking for a student to help us construct new microscopy modalities that probe the brain with higher spatial and temporal resolution. Tasks will involve advanced optical engineering, signal processing, and live animal imaging experiments. These tasks will generate training in physics, data science, and sufficient biology to bring experiments into publication.

Faculty Advisor: Prof. Yiyang Gong (

Edge Computing Platforms for the Internet of Things

Over the last several years, multiple platforms that allow easily connecting low-end Internet of Things devices to the cloud have become available --  they include, for example, EdgeX, AWS Greengrass, and Microsoft Azure Edge. Such platforms hold the promise of enabling true scaling of the IoT by making IoT nodes easy to set up and manage, and by mitigating some of the disadvantages of the cloud in cloud-node interactions. We seek to understand the advantages, performance, and the limitations of these promising novel systems in enabling the next generation of the Internet of Things.

In these projects, students will deploy one or more of the existing edge computing platforms (e.g., AWS Greengrass) to connect several small Internet of Things devices to the cloud. The students will examine the performance of these systems in terms of latency, energy consumption, level of operational intelligence and independence, and network loads. The students will demonstrate strengths and weaknesses of these systems, and recommend, design, develop, and implement solutions that address the weaknesses.

Preferred skills/interests include: software development, distributed systems,  communications, networking, mobile systems.

Faculty Advisor: Prof. Maria Gorlatova (

Intelligent Environmentally-Aware Mixed Reality

We are investigating how modern mixed reality experiences -- e.g., experiences generated by Microsoft HoloLens sets -- can be improved via additional intelligence and via integration with sensor-based "smart objects".

This project involves the integration of new remote sensing capabilities with existing mixed reality experiences, and the demonstration of the effectiveness of these techniques in creating environmentally-aware mixed reality experiences.

The project requires setting up a multi-sensor system that collects environmental data, and an ARCore-based and/or a Microsoft Hololens-based system that uses sensor information to change the properties of generated holograms. The project involves experimental evaluation of the developed system.

The project requires general software-development skills. Experience with Unity, C#, and/or Android-based app development would be potentially advantageous, but is not required.

Faculty Advisor: Prof. Maria Gorlatova (

Networking and Communications for Augmented Reality

We are investigating network and communication patterns of modern augmented and virtual reality (AR/VR) applications. Towards this understanding, we are looking to conduct experiments that will elucidate how modern AR/VR systems communicate with each other, and how modern communication systems limit AR/VR experiences.

Students involved in this work will experiment with different augmented reality applications, such as Microsoft HoloLens and Google ARCore applications, to understand the traffic loads associated with different experiences, and to recommend corresponding optimizations and improvements.

Preferred skillsets: coursework in communications and networking, general software development skills.

Faculty Advisor: Prof. Maria Gorlatova (

From Cancer to Explosives Detection: The Data and Physics of X-ray Diffraction

X-ray diffraction is a well-established technique for determining the molecular structure of a material. It’s ability to tell explosives from non-threat materials in luggage or malignant from cancerous tissue in tumors is driving its development as a diagnostic, imaging technique. In this project, the student will acquire experimental data and apply data mining and data processing (e.g. machine learning) techniques to understand how the statistical properties of the diffracted X-rays the statistical properties of the scattering material. Understanding this relationship will improve the performance of X-ray diffraction based systems, as well as potentially provide insight into visible-optics systems such as a holographic imaging and imaging through thick scattering media.  Experiment, simulation, and basic theory will be involved in the project - some coursework in the areas of optics/electromagnetism and/or signal processing is preferred (although all interested students are welcome to apply).

Faculty Advisor: Prof. Joel Greenberg (

Developing Computer Memory Systems with New Technologies

Computer systems have historically used DRAM and either disks or Flash-based solid-state drives (SSDs) to hold their information.  Recently, however, many new storage technologies have emerged, including phase change memory (PCM), Intel/Micron’s Xpoint, 3D DRAM, STT-RAM, and Racetrack memory.  All of these new technologies have interesting advantages (e.g., greater storage density) and quirky challenges (e.g., some can only be written a given number of times before failing).  This project seeks to incorporate these new memory technologies into computer systems, and the key to this integration is how we encode the data that we write onto them.  Different encodings can greatly improve performance, reliability, and power consumption.  This project is an interdisciplinary collaboration between computer architects and information theorists.

In this project, the student will perform a subset of the following tasks, and this subset will depend on the student’s background and interests.  Tasks include: developing new codes, experimentally evaluating codes, implementing new codes on real hardware, and studying the unique features of new memory technologies.  

Faculty Advisor: Prof. Dan Sorin (

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

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.

Faculty Advisor: Prof. Adrienne Stiff-Roberts ( 

A Wearable Benefiting People with Sensory Needs

This is an open-ended project that involves designing and building a working prototype of a wearable system that can help mitigate some sensory problems that people might have, such as those diagnosed with Generalized Anxiety Disorder (GAD) and Autism Spectrum Disorder (ASD). One example of such systems could be a compression garment that gets automatically triggered when the person is anxious. The student should have some experience in embedded systems. Experience in machine learning is a plus.

Faculty Advisor: Prof. Rabih Younes (

Student-Proposed REU Project

Prospective Duke ECE REU students are invited to propose a project to work on under the advisement of one of our Duke ECE faculty members. On the application form, in addition to uploading your resume and statement of interest, please use a format similar to the project descriptions above to upload a proposal including the following:

  • The name of the faculty member(s) you propose to work with
  • The description of a project that can be completed within or reach a reasonable stopping point at the end of a nine-week period
  • An explanation of an expected product or outcome from the project
  • A list of any necessary supplies or materials

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