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 December 15 and close January 31
- Applicants will be notified of final decisions by March 15
- 2024 program dates: May 25, 2024 - July 27, 2024
Support
- $5400 stipend ($600 per week)
- Housing and meals on Duke campus
- Travel reimbursement, up to $600
Are you Ready?
To be eligible, an applicant must be:
- Sophomore or Junior status
- Enrolled in an accredited college or university
Duke ECE strongly encourages students from groups underrepresented in the practice of electrical engineering, computer engineering and computer science to apply.
Submit Your Application
Complete our online application, which includes:
- Statement of purpose
- Résumé
- Contact information for a letter of recommendation
When activated, the 2024 application link
will appear here.
Browse 2024 Project Summaries
- Exploiting Natural Language Processing for Speech Enhancement in Cochlear Implants
Project Overview: 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. Everyday listening environment contain varying combinations of background noise and reverberation. Thus, there is a need for robust speech enhancement algorithms for diverse acoustic environments. Recent years has 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 speech enhancement for CI users in challenging listening conditions with noise and reverberation.
Student Work Summary: Students will learn 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 speech enhancement in Cis under various conditions of noise and reverberation.
Qulifications & Interests:
- Interest in a biomedical application/multidisciplinary project
- Experience with signal processing and machine learning. Scope of project will be tailored to student’s background.
- Experience with MATLAB and Python (preferred).
- Analytical, communication and interpersonal skills.
Principal Investigator: Dr. Boyla Mainsah, Assistant Research Professor in the Department of Electrical and Computer Engineering
- Making Next-Generation Transistors Better
Project Overview: Amorphous oxide semiconductors, such as Indium Tin Oxide (ITO) is being considered for high performance computing at the back-end of silicon because it requires low temperatures for processing. These transistors meet the technological specifications, but have issues in reliability. Our goal is to develop gate-all-around ITO short-channel transistors and make them reliable. Learn more >
Student Work Summary: The student will be expected to perform thorough electrical characterization of transistors (fabricated by graduate students) in the lab. The student will also record and plot all data, and present them in weekly meetings to the group.
Qulifications & Interests: Basic understanding of semiconductor devices - resistors, diodes, transistors.
Principal Investigator: Dr. Tania Roy, Assistant Professor of Electrical and Computer Engineering
- RETINOS: Retina-Like Semiconductor Devices for Image Recognition
Project Overview: We will fabricate nanoscale semiconductor devices in the cleanroom and characterize them in the lab. The goal is to obtain arrays of devices which will function like the retina of the eye, and perform image processing and image recognition without complicated circuitry.
Student Work Summary: You will be expected to take training from the graduate students, perform electrical characterization in the lab, and record and plot data. You will also be expected to present this data in front of the group in the weekly meetings.
Qulifications & Interests: Basic understanding of semiconductor devices - resistors, diodes and transistors.
Principal Investigator: Dr. Tania Roy, Assistant Professor of Electrical and Computer Engineering
∗ Due to funding requriments, this project is offered to applicants who are US citizens or permanent residents only.
- Individually Customized Neural-network Speech Enhancement for Hearing-Aids
Project Overview: Our goal is to develop a concept for neural-network-based signal processing which can be trained to improve speech intelligibility based on feedback from the hearing-aid user. Current hearing aid signal processing consists of dynamic range compression and graphic audio signal equalization based on audiograms which only obliquely relate to speech intelligibility. Neural networks have been used to classify noise environments and facilitate noise suppression but do not consider the hearing impairment of the user. As a result, in many situations and for many users, hearing aids do not improve speech intelligibility. This project will investigate concepts for training a neural network-based filter based on speech recognition scores of the hearing-aid user. The idea is to train the NN by having the hearing-aid user listen to spoken words, having them repeat the words they hear, having a conventional speech recognition system score the human-spoken words as correct or in error, and adjust the NN-based audio filter parameters to minimize word recognition error. In this study, simulation of artificial hearing impairments will be used to first develop and test the concept. A survey of related literature will be conducted.
Student Work Summary: Implement a neural-network to undo known audio distortions that mimic hearing impairment.
Qulifications & Interests: Knowledge of basic digital signal processing (junior level undergrad course) and some experience with PyTorch for Neural network implementation.
Principal Investigator: Dr. Jeffrey Krolik, Professor of Electrical and Computer Engineering
∗ Due to funding requriments, this project is offered to applicants who are US citizens or permanent residents only.
- Fabrication of MXene Thin Films
Project Overview: MXene materials, which are metal carbides and nitrides, represent a novel category of materials with significant potential in fields like robotics, energy storage, and AI hardware. In this project, the student will: 1. Master the chemical exfoliation technique to produce titanium carbides, one of the most researched MXenes. 2. Examine the produced MXene flakes through an optical microscope. 3. Analyze the resulting thin film to understand its characteristics. 4. Explore the connection between synthesis methods and the quality of the resultant thin films. All tasks will be carried out in-person at The Wang Lab. Regular weekly meetings with the Principal Investigator (PI) are planned. Learn More>
Student Work Summary: During the summer, the student will:
- Complete safety training to work in lab space
- Use HCl + LiF solution to exfoliate MAX solids
- Utilize centrifuge to obtain exfoliated MXene solution
- Pipette small quantities of MXene onto Si wafer
- Try the silicon wafer on hot plate
- Use optical microscope to observe the MXene
- Analyze optical microscope data to optimize the fabrication process
- Gain research experience in a lab environment
Qulifications & Interests: 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: Dr. Haozhe "Harry" Wang, Assistant Professor of Electrical and Computer Engineering
- Making van der Waals Heterostructures for Quantum Electron Transport Experiments
Project Overview: Background: Since its discovery in 2004, graphene research has been one of the main focuses of solid state research, culminating in the 2010 Nobel Prize. In recent years, research interest began to shift to the van der Waals heterostructures—stacks of two-dimensional materials other than graphene, held together by van der Waals forces. By stacking atomically thin layers of materials, it is possible to create electronic structure with properties not commonly found in nature. This enables the creation of so-called “designer materials” whose characteristics are limited only by the creativity of the researcher. Of particular interest to our laboratory are the superconducting samples that may host exotic quasiparticles such as the Majorana fermions. Project description: The participants will learn to exfoliate van der Waals materials, assemble them into heterostructures, characterize and pattern them into testable devices at the Duke’s Shared Materials Instrumentation Facility (SMIF). These devices will be measured in our lab’s state of the art measurement facility, including dilution refrigerators capable of reaching temperatures of tens of mK above absolute zero, and magnetic field up to 12 Tesla. Learn More>
Student Work Summary: Students will work closely with graduate students who have previously trained several undergraduates in the lab. This will enable students to learn established techniques in fabrication, material characterization, and device design. Students will additionally be guided through reading seminal papers and texts that will familiarize them with the groundbreaking results relevant for their devices.
Qulifications & Interests: Ideal participants will be inquisitive, creative, and focused. Students should be interested in pursuing research in nanoscience, quantum engineering, or related fields. No prior lab skills or experience are required.
Principal Investigator: Dr. Gleb Finkelstein, Professor of Physics & Electrical & Computer Engineering
- Nanomaterial Inks for Printed Electronics
Project Overview: 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 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 into thin-film transistors. Learn More>
Student Work Summary: In this project, both an aerosol jet printer and a capillary force printer will be used to improve the morphology of various nanomaterial inks, thus increasing their usefulness in printed electronics. The student will work directly with a PhD student to optimize nanomaterial-based inks and print them into functional transistors and sensors (e.g., temperature and humidity sensors, bioFETs). The student will 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.
Qulifications & Interests: An ideal candidate for this project would have some previous knowledge and experience in solid-state physics and semiconductor devices, 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: Dr. Aaron Franklin, Addy Professor of Electrical and Computer Engineering
- Using Electromagnetic Energy to Replace Pesticides and Reduce Fertilizers
Project Overview: Our group is developing a sustainable novel approach to eradicate weeds and plant-parasitic nematodes. The method uses electromagnetic pulses to inhibit weed growth and kill eggs/infectious juveniles of PPN. In collaboration with NCSU we are developing and test on the sweet-potatoes/tomatoes fields the next generation prototype that consist in an automated cart (Roomba-like) with an optimally designed lensing antenna and a solid-state source.
Student Work Summary: Students will contribute (together with other graduate students and faculty) to several of the development/testing steps, including learning multiphysics modeling to optimize the treatment procedure, construction of the automated cart and developing the control/monitoring software.
Qulifications & Interests: Ideal applicants will have a basic knowledge of physics/electromagnetic waves, and be creative and interested in working for sustainable technologies. Some experience in softare programming (Matlab or Labview or any other programming software) is useful.
Principal Investigator: Dr. Paolo Maccarini, Associate Research Professor in the Department of Electrical and Computer Engineering
- Intelligent Augmented Reality
Project Overview: 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. Learn More>
Student Work Summary: 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 headsets.
Qulifications & Interests: 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 with machine learning (preferred but not required).
Principal Investigator: Dr. Maria Gorlatova, Nortel Networks Assistant Professor of Electrical and Computer Engineering
- Spectrum Monitoring and Sharing in the CBRS Band
Project Overview: The excellent propagation and low deployment costs in the sub-7 GHz band have motivated expanded spectrum allocations from 3–5 GHz, especially the Citizens Broadband Radio Service (CBRS) band in 3.55–3.7 GHz. The use of the CBRS band in 5G/beyond-5G creates coexistence challenges between high-power DoD shipborne radars, fixed satellite services ground stations, and commercial mobile broadband services such as the Long-Term Evolution (LTE) networks. This project will focus on the measurements and technologies that can potentially address the important challenges associated with spectrum sharing and co-existence in these bands.
Student Work Summary: In this project, we will focus on extensive spectrum measurement campaigns for dataset collection in different geographical areas using both commercial-grade smartphones and software-defined radios. In particular, as part of the efforts in the first major component of the project, we are currently working with the Duke Office of Information Technology (OIT) and RF Connect (a premier wireless and mobile solutions provider) on a pilot deployment of four LTE cells using the CBRS frequency (band 48) on the roof of the Davison and Clocktower building on the Duke University West Campus. The student will work on collecting a variety of measurement datasets using commercial smartphones for characterizing the performance of the CBRS LTE network deployed at Duke, and on the corresponding data post-processing and analysis. The smartphone-based measurements provide LTE cell information such as the Physical-layer Cell Identifier (PCI), frequency band, Reference Signal Received Power (RSRP), and Reference Signal Received Quality (RSRQ), which are important information for data post-processing and analysis. We will also investigate the use of software-defined radios (SDRs) such as the USRP B210/X310 radios with customized software for LTE network monitoring and telemetry analysis.
Qulifications & Interests: Experience with Python and basic networking tools (e.g., iperf/iperf3) is required. Familiarity with wireless networking, signal processing, and ML will be a big plus.
Principal Investigator: Dr. Tingjun Chen, Assistant Professor of Electrical and Computer Engineering