Summer Research Experiences for Undergraduates (REU)

Primary Investigator

Yiran Chen
John Cocke Distinguished Professor of Electrical & Computer Engineering
Bio | lab website

Overview

This project focuses on utilizing Content-Addressable Memories (CAMs), Hopfield Network and other associative memory circuits to accelerate AI workloads by reducing computational complexity. Specifically, CAMs can reduce the complexity of AI tasks, such as attention mechanisms, from O(n²m) to O(m) through efficient XNOR-based parallel search operations, eliminating the need for traditional MatMul operations.

Novel CAM cell designs, architectures, and matching operations will be explored for novel applications. The student will work on exploring CAMs for in-memory computing, leveraging their potential for neuromorphic computing applications to enhance both speed and energy efficiency in AI models.

The project involves algorithm development, circuit design, and evaluating the performance of CAM-based architectures in tasks that typically demand high computational resources. 

Student Work Summary

The student will investigate high-performance accelerators which combine CMOS and emerging ReRAM device technologies (or memristors) for computing applications including network security, machine learning, neuromorphic computing, finite automata, and other novel computational models.

The work can span a range of activities including the design of prototype systems and/or integrated circuits; the invention of new architectures, and/or circuits to take advantage of physical hardware systems for acceleration of target computations; the operation of existing hardware platforms; and performance evaluations with competing systems. 

Qualifications & Interests

• Experience in circuit design 
• Experience in Machine Learning 
• Self-motivated, pro-active, with leadership qualities 
• Ability to work with ambiguity 
• Demonstrated effective communication and collaboration skills.  

Preferred Skills (But not required) 

• In-memory computing 
• SPICE 
• Python 
• PyTorch 
• Accelerator simulator building experience.  
• Familiarity with memory architectures (SRAM, DRAM)  
• Demonstrated ability to generate, frame, and carry out leadership research as shown, for example, by papers published in top-tier conferences or journals 

Primary Investigator

Tingjun Chen
Assistant Professor in the Department of Electrical & Computer Engineering
Bio | lab website

Overview

This project will focus on the development of scalable wireless digital twins (DTs) using 3D scene modeling tools, worldwide geographic databases, and modern ray tracing tools (e.g., Sionna RT and OptiX from NVIDIA). The developed wireless DTs will be validated using in-field measurements and will be used to potentially address the important challenges associated with spectrum sharing and co-existence in these bands. 

Student Work Summary

A wireless digital twin (DT) has three components: the real-world model or 3D scene representing the physical world, practical hardware radio models, and a wireless ray tracing (RT) engine that analyzes the signal propagation as electromagnetic waves interact with real-world objects (i.e., 3D meshes in the scene).

This project will focus on constructing high-fidelity real-world models using publicly available worldwide geographic databases and NVIDIA’s Sionna RT and OptiX ray tracing tools, and investigate methods for efficient DT generation and systematic approaches for validating the generated DT. 

Qualifications & Interests

• Programming skills with Python 
• Experience with C++ and CUDA will be a big plus 

Primary Investigator

Emily Wenger
Assistant Professor of Electrical & Computer Engineering
Bio

Project Overview

Modern crypto-systems rely on “good” random number generators (RNGs) for their security. Extensive prior work has demonstrated that using “bad” (e.g. poorly or maliciously generated) random numbers in cryptographic systems can cause serious security vulnerabilities.

To prevent the use of bad random numbers, the National Institute of Standards and Technology (NIST) has established a list of randomness tests that RNGs must pass to be used in real-world systems. However, generative AI models can now produce realistic content in a variety of settings. This suggests the question: Could generative AI models serve as “bad” random number generators that look “good,” according to standardized tests?

This project will explore the possibility of AI-generated malicious randomness and its implications for modern crypto-systems. If time allows, it will also explore whether AI models can detect bad randomness. 

Student Work Summary

Students will work with professor Wenger to build a generative AI model that passes NIST’s established randomness tests despite producing “bad” random numbers. This work will involve a review of relevant literature, construction and training of a domain-appropriate generative AI model, and the implementation/evaluation of randomness tests.

Students will also work with professor Wenger to identify crypto-systems where such AI-generated randomness could cause serious problems and potentially develop novel attacks for these (we are already aware of one such system).

Finally, assuming the work is successful, students will also develop mitigations against AI randomness attacks. 

Qualifications & Interests

• Taken classes in machine learning or statistics and cryptography or computer security
• Fluent in Python and familiar with Pytorch  
• Has trained generative AI models (preferably)

Primary Investigator

Michael Gehm
Professor of Electrical & Computer Engineering
Director of ECE Graduate Studies
Bio

Overview

We have developed a tabletop method for simply and inexpensively measuring the refractive index of materials at X-ray energies—greatly simplifying the synchrotron-based methods that are most common today.

This project will continue the development and application of the refractometer—improving its operation and using it to measure the index of an increasing number of materials. 

Student Work Summary

Student work will focus on a mix of X-ray experiments, hardware development/refinement, algorithm and data science, as well as overall laboratory skills. 

Qualifications & Interests

The ideal candidate would have: 

• Some physics/optics knowledge (at the level of second-semester physics or above)
• Some algorithmic/coding skills (MATLAB or python sufficient to do basic data manipulation)  
• A willingness to work with their hands in an experimental lab, and the tenacity to deal with the daily ups and downs of experiment 

Primary Investigator

Boyla Mainsah
Assistant Research Professor in the Department of Electrical & Computer Engineering
Bio

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 environments contain varying combinations of background noise and reverberation. Thus, there is a need for robust speech enhancement algorithms for diverse acoustic environments.

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 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. 

Qualifications & 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 

Primary Investigator

Natalia Litchinitser
Professor of Electrical & Computer Engineering
Bio

Overview

This project is focused on the design and demonstration of optical metasurfaces for Imaging, edge detection, and sensing in complex environments such as atmospheric or undersea turbulence or biological media, with the goal to realize low size, weight, and power flat optics-based optical devices. 

Student Work Summary

Students will participate in design, modeling, nano-fabrication and optical characterization of flat optics devices and their integration with other system components. 

Qualifications & Interests

• We are looking for students interested in research at the interface of optical physics, device engineering, and imaging
• Any experience in optical setup, robotics or computer simulations is a plus 

Primary Investigator

Huanqian Loh
Assistant Professor of Electrical & Computer Engineering
Bio | Lab website

Overview

Arrays of single atoms trapped in optical tweezers represent a fast-moving platform for pushing the frontiers of quantum science. Atom arrays have been highly successful in simulating complex problems in quantum many-body dynamics, which are important for understanding advanced materials and developing tailored quantum sensors.

The starting point of such a quantum simulator is a defect-free atom array, which can be realized by loading single atoms stochastically in an array of optical tweezers and then shuttling the atoms in real time to remove defects.

The goal of this project is to design and generate reconfigurable optical tweezer potentials that can be used to shuttle atoms around in the quantum simulator with low latency. 

Student Work Summary

Extensive programming is involved, alongside optical device characterization and radio-frequency analysis. The student will gain valuable research experience in working in a team that is setting up a brand-new quantum simulation apparatus. 

Qualifications & Interests

• The project is best suited for students interested in pursuing quantum science in the laboratory
• Prior programming skills in Python are required
• A background in communications and networking is preferred but not required

Primary Investigator

Hai “Helen” Li
Marie Foote Reel E’46 Distinguished Professor
Chair of Electrical & Computer Engineering
Bio | lab website

Overview

The nature of the project is to accelerate DNA sequence alignment. With the advent of next generation sequencing (NGS) technologies, an explosion of genomic data has occurred that has far outpaced Moore’s Law. This genomic data has enabled new use cases such as personalized medicine and population-scale sequencing; however, these goals are still bottlenecked by the energy-intensive task of sequence alignment.

Current solutions largely ignore the long and noisy nature of NGS reads and focus on accelerating short and/or highly accurate reads. Furthermore, it is precisely these long and noisy type NGS reads that are produced by field devices on the edge. Therefore, to enable sequence alignment at the edge an algorithm-hardware co-design is needed. In this project, a novel algorithm for noise robust seed-and-extend based sequence alignment is proposed along with an energy-efficient processing-in-memory architecture. 

Student Work Summary

The student will participate in evaluation of novel fuzzy seeding algorithms, evaluation of novel architectures to accelerate algorithms, and design space exploration of the hardware-software co-design. The student will work closely with PhD students and attend bi-weekly research meetings with the lab group.

The undergraduate students will have opportunities to interact with graduate students in various labs and engage in algorithm and architecture codesign projects as well as experimental protocol development and execution. This research experience will enrich their undergraduate education and deepen their comprehension of electronics and hardware systems. 

Qualifications & Interests

• Fundamental training in algorithm and computer architecture 
• Strong coding skills  
• Good communication skills 

IMPORTANT NOTE

This project is limited by funding requirements to US citizens and permanent residents.

Primary Investigator

Maria Gorlatova
Nortel Networks Assistant Professor of Electrical & Computer Engineering
Bio | lab website

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, particularly 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. 

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 2, Microsoft HoloLens 2, or Apple Vision Pro headsets.  

Qualifications & 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
• Experience working with game engines (Unity or Unreal) is preferred but not required
• A background in communications and/or networking is preferred but not required  
• Familiarity with Machine Learning is preferred but not required

Primary Investigator

Haozhe “Harry” Wang
Assistant Professor of Electrical & Computer Engineering
Bio | lab website

Overview

This research project explores the application of machine learning techniques to optimize and automate scientific experimentation processes. By developing intelligent algorithms that can learn from experimental outcomes, we aim to create a system that can autonomously execute experiments while minimizing resource consumption and maximizing scientific discovery. This work has the potential to accelerate research across multiple scientific domains by reducing the time and resources required for experimental optimization. 

Student Work Summary

The undergraduate researcher will collaborate with faculty and graduate students to develop and implement machine learning models for experimental design optimization. Key responsibilities include data preprocessing, implementing and testing ML algorithms, analyzing experimental results, and contributing to the development of an automated experimentation platform. The student will also assist in documenting the research process and preparing results for publication. 

Qualifications & Interests

• The ideal candidate should be pursuing a degree in Electrical & Computer Engineering, Computer Science, Data Science, or a related field, with a strong foundation in programming (particularly Python) and experiments.  
• Experience with machine learning frameworks (e.g., PyTorch, TensorFlow) is highly desirable.  
• We seek a self-motivated individual with strong analytical skills, attention to detail, and a genuine interest in scientific research. The ability to work independently while effectively communicating within a team environment is essential.  
• Prior research experience is not required, but enthusiasm for learning and problem-solving is crucial. 

Primary Investigator

Aaron Franklin
Addy Professor of Electrical & Computer Engineering
Bio | lab website

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. 

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. 

Qualifications & 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 
• Competence in (or confidence and willingness to learn) operating complex tools 
• Self-motivation and a strong work ethic in terms of commitment and follow-through. A collaborative team player is a must. 

IMPORTANT NOTE

This project is limited by funding requirements to US citizens and permanent residents.

Primary Investigator

Tania Roy
Assistant Professor of Electrical & Computer Engineering
Bio | lab website

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

Students will be expected to take training from the graduate students, perform electrical characterization in the lab, and record and plot data. They will also be expected to present this data in front of the group in the weekly meetings. 

Qualifications & Interests

• Basic understanding of semiconductor devices–resistors, diodes and transistors
• Must have completed at least one semiconductor device course