Duke Electrical & Computer Engineering’s Research Experience for Undergraduates (REU) is a paid opportunity that brings undergraduates into our research laboratories for nine weeks in the summer.
Work full-time on interesting projects, led by members of the Duke ECE faculty. You'll experience thought-provoking seminars and workshops, participate in research lab life, and present your findings at a special poster symposium.
- Applications open in January and close in mid-February
- Applicants will be notified of final decisions in April
- 2024 program dates: To be announced
We'll bring you to Duke, provide a place to stay and Meals
- $600 per week stipend, paid at the start of the program
- Housing and meals on the Duke campus
- Travel reimbursement, up to $600
Are you Ready?
To be eligible, an applicant must be:
- An undergraduate student
- Enrolled in an accredited college or university
- A U.S. citizen or permanent resident
Duke ECE strongly encourages students from groups underrepresented in the practice of computer engineering and computer science to apply.
Submit Your Application
Complete our online application, which includes:
- Statement of purpose
- Contact information for a letter of recommendation
When activated, the 2024 application link
will appear here.
Browse 2023 Project Summaries
- Imaging Through Walls and Other Obstacles
The project involves radio frequency (RF) imaging through walls and other obstacles. Experimental studies on visualizing various objects and materials behind walls using millimeter waves will be pursued, as well as various techniques of reconstructing and optimizing images from mm-wave measurements.
What you'll do: Assist in data taking, reconfiguring measurement setups, and programming various instruments for automation
What you'll learn: Computational imaging and general radar techniques, and can get involved at a deeper level in reconstruction efforts.
What you should bring: Students should be strongly self-motivated and research-oriented. Strongly recommended: Experience with Python programming. Helpful, but not required: Any knowledge of electromagnetic wave propagation and experience with antennas or RF devices.
Principal Investigator: David Smith, James B. Duke Distinguished Professor of Electrical & Computer Engineering
- Retina-Like Semiconductor Devices for Image Processing and Object Identification
The optoelectronic synapse is a novel device. Like the retina of the eye, it can perform imaging and image processing using individual pixels. In this project, we are fabricating optoelectronic synapses using two-dimensional (2D) materials. We are optimizing the device performance through optical and electrical engineering. We are building pixel arrays to demonstrate basic image processing tasks, such as contrast enhancement, de-noising, and image memorization. Our devices are the only kind that can operate from ultraviolet (UV) to infrared (IR) wavelength ranges, because of the van der Waals heterostructures used in these devices. Learn more »
What you'll do: Characterize the optoelectronic synapse pixels. Electrical characterizations, such as Id-Vg and Id-Vd of transistors with and without light, will be measured. Optoelectronic synapse characterizations by pulsing electrical pulses in dark and under illumination, memory measurements, etc., will be performed by the undergrad.
What you'll learn: Transistor physics and device measurements.
What you should bring: Interests in transistor design and semiconductor physics, self-motivation, and ability in meticulous record-keeping. Students with a background in ECE 330L will be preferred.
Principal Investigator: Tania Roy, Assistant Professor of Electrical & Computer Engineering
- Intelligent Mobile Augmented Reality
Augmented reality (AR) is a rapidly developing technology area with the 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 an in-depth experimental understanding of the limitations of current augmented reality experiences and to establish how these limitations can be addressed. Learn more »
What you'll do: Experiment 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.
What you should bring: An experimental mindset and general software development skills. Preferred but not required: background in communications and/or networking, and familiarity with machine learning.
Principal Investigator: Maria Gorlatova, Nortel Networks Assistant Professor of Electrical and Computer Engineering
- Building and Testing a Digital Twin for Reconfigurable Networks
This project will focus on building, enhancing, and testing Mininet-optical, which is a prototype implementation of an emulator for packet optical software-defined networks. Mininet-optical enables end-to-end emulation of a software-defined network that includes optical network elements such as reconfigurable optical add/drop multiplexers (ROADMs), optical transceivers, fiber spans, packet software-defined networking (SDN) elements such as OpenFlow switches and Ethernet links, and SDN controllers to manage both packet and optical network elements.
What you'll do: Focus on building a digital twin of the physical COSMOS advanced wireless-optical testbed using the Mininet-optical emulator. Design and conduct measurements of various optical hardware and transceiver performance. Analysis and modeling of the physical hardware in COSMOS and incorporation of models into the Mininet-optical emulator. Comparison of the end-to-end system performance in reconfigurable networks between real-world measurements (using the physical testbed) and large-scale simulations (using Mininet-optical as the digital twin of the testbed).
What you should bring: Experience with Python is required. Familiarity with computer networks, software-defined networking (SDN), optical networking, and ML will be a big plus.
Principal Investigator: Tingjun Chen, Assistant Professor of Electrical & Computer Engineering
- Control System Components for Quantum Computing with Trapped Ions
A key feature of an ion trap quantum computer is control software which integrates laser modulation with externally-provided clock signals for high performance of quantum gates. In this project, the student will integrate and test control system components for quantum computing with trapped ions. Learn more »
What you'll do: Integrate multi-tone radio frequency (RF) control cards into our existing control architecture used to perform quantum gates. Develop modular code in ARTIQ, control software for quantum systems that can be used to control most aspects of trapped ion quantum computing from trapping fields to state detection. The goal of this is to control Acousto-Optic Modulators used to produce multi-tone laser light required for quantum gates on trapped ion qubits.
What you should bring: Familiarity with Python. Familiarity with the basics of analog and digital circuits. Interest in quantum computation and developing the interdisciplinary solutions required to experimentally control and manipulate trapped ion qubits. Interest in working with a team in a lab environment. Interest in building scientific and professional communication skills.
Principal Investigator: Crystal Noel, Assistant Professor of Electrical and Computer Engineering
- New Degrees of Freedom for Sculpting Optical Beams in Space and Time Beyond Conventionally Used Amplitude, Phase and Polarization
The rapid development of optical technologies, such as optical manipulation and trapping, data processing, optical sensing and metrology, enhanced imaging and microscopy, as well as classical and quantum computing and communications, necessitates fundamental studies of the new degrees of freedom for sculpting optical beams in space and time beyond conventionally used amplitude, phase and polarization. Topological particle-like objects in structured optical fields have emerged as one of the most promising candidates for such degrees of freedom. In this project, we will study “structured light and darkness,” or 2D vortex beams and 3D optical links, knots, and skyrmions. This research will focus on the generation, detection, and linear and nonlinear light-matter interactions of optical links, knots, and skyrmions in judiciously engineered optical media such as optical metamaterials and metasurfaces.
What you'll do: Work with our graduate students and post-doctoral researchers on the design, nanofabrication and optical characterization of optical metasurfaces
What you'll learn: How to fabricate all-dielectric optical nanostructures using electron beam lithography, build optical setups, and characterize the intensity and the phase profiles of structured light beams
What you should bring: Interest in optics and photonic devices, and their applications
Principal Investigator: Natalia Litchinitser, Professor of Electrical & Computer Engineering
- Improving the Morphology and Interfaces for Printed Nanomaterial-Based Electronics
Many new electronic applications can be made possible with more affordable and readily customizable circuits. These circuits do not necessarily need to exhibit the high performance achieved with silicon-based Complementary Metal-Oxide Semiconductor (CMOS) chips, but they do need to offer diverse functionality and moderate performance at a low fabrication cost. Printing electronics is a viable approach for enabling this new electronics era. For decades, organic materials have been studied for their use in printed electronics, but they suffer from compatibility issues for many applications and considerable performance limitations. A more recent option has been to use nanomaterials printed into thin films. Nanomaterials offer superior electronic properties to bulk materials, including organic polymers, and are able to be dispersed into a variety of inks for printability. Further, nanomaterials are robust to extreme environments and highly compatible with a nearly endless variety of integration schemes. Entirely new applications, from highly sensitive biomedical diagnostics to sensors for harsh environments, can be enabled with a printed nanomaterial-based electronics technology. This project will involve the exploration of custom ink formulations consisting of nanomaterials that can be printed with an aerosol jet printer into thin-film transistors. Learn more »
What you'll do: Work directly with a PhD student to optimize nanomaterial-based inks, and print them into functional transistors and biosensors (e.g., electronic immunoassays or bioFETs). You'll print the optimized inks in various types of carbon nanotube thin-film transistors. A series of electrical (I-V, C-V sweeps) and physical (SEM, Raman) measurement tools will be used to evaluate the electrical and structural performance of the printed films and devices. The printing process requires training and customization of several key steps, including printing conditions, film curing, and integration sequence. The student will also be expected to take part in discussions where results will be analyzed and new ideas potentially formulated for inclusion in the project.
What you should bring: An ideal candidate 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 (or confident and willing to learn) 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.
Principal Investigator: Aaron Franklin, Addy Professor of Electrical & Computer Engineering
- Using AI and ML to Mitigate the Impacts of Reverberation and Noise on Speech Recognition for Individuals With Cochlear Implants
Cochlear implants (CIs) can restore hearing to individuals with severe hearing loss. Most cochlear implant users have good speech recognition in quiet conditions; however, they struggle to understand speech in challenging listening environments with noise and reverberation. While commercially available CIs have incorporated technical solutions to reduce background noise, there is currently no effective solution that directly addresses reverberation in CIs. The main challenge in developing a solution to mitigate reverberation is the task of distinguishing wanted speech from unwanted speech with similar characteristics: reverberant speech reflections are echoes—attenuated and delayed copies—of the target speech that a listener is trying to understand. Recent years have seen successful applications of artificial intelligence/machine learning for smart voice assistants that rely on the predictability of speech for automatic speech recognition of words to execute voice commands. Based on a similar concept of a “smart” CI, this project will leverage real-time automatic recognition of phonemes—the smallest unit of speech—to improve reverberant speech enhancement in CIs.
What you'll learn: Facts about reverberation, sound processing in a CI, an acoustic model to simulate CI processing, and training machine-learning models for speech enhancement within the CI processing pipeline. Students will explore the utility of framewise phoneme predictions to improve reverberant speech enhancement in CIs.
What you should bring: Communication, interpersonal and analytical skills. Interest in biomedical application/multidisciplinary project. Some experience with digital signal processing and machine learning. The scope of the project will be tailored to the student’s background. Experience with MATLAB and Python is preferred.
- Leslie Collins, Professor of Electrical & Computer Engineering
- Boyla Mainsah, Assistant Research Professor of Electrical & Computer Engineering