2025 SURE/SROP Research Projects in ECE

Faculty mentors:
Anthony Grbic
[email protected]

Steve Young
[email protected]

Research area(s):
Electromagnetics

Course format:
In person

Prerequisites:
Required: EECS 230
Preferred: EECS 330 or EECS 334

Student should have knowledge of time-harmonic electromagnetic fields (plane waves).

Description:
Space-time modulation has attracted strong interest within the fields of radio frequency (RF) circuits, applied electromagnetics, and optics in recent years. Progress in the availability and performance of tunable semi-conductor devices as well as electro-/magneto-optic, phase change, and 2D materials have drawn researchers to examine the modulation of electronic circuits and electromagnetic/optical devices in both space and time. Space-time modulation enables filtering (n-path circuits), frequency conversion, parametric effects, and more recently non-reciprocity (one-way transmission).

The student will investigate electrically-small (miniature) antennas whose properties vary as a function of time and space. Specifically, the student researcher will explore how modulation in time and space can be leveraged to enhance the bandwidth-efficiency product of small antennas in order to overcome fundamental bounds on standard linear, time-invariant antennas. The unique approach involves modulation of the antenna properties, via tunable circuit elements, to couple a radiative antenna mode to non-radiative antenna modes. Out-of-band non-radiative modes are used to as low-loss tanks/resonators in parametric gain and bandwidth-broadening processes. The student will simulate, build, test and characterize these antennas in the laboratory.

Knowledge of electromagnetic fields is required, and experience with microwave instruments (such as signal generators, network analyzers, spectrum analyzers) is an asset. Programming experience using MATLAB or Python is also desirable.

The student will gain marketable skills: a working knowledge of
space-time RF circuits, antennas and radiation, microwave measurement techniques, industry-standard RF simulators, and instrument automation.

Faculty mentor:
Jiasi Chen
[email protected]

Research area(s):
Computer Vision
Embedded Circuits

Course format:
In person

Prerequisites:
Required: EECS 280
Preferred: EECS 281

Experience with Unity is desirable but not required.

Description:
This project is on physical rehabilitation games using augmented reality (AR) technology to teach middle schoolers about coding and inspire them to explore STEM careers. The project is based on an ongoing research project in my group in collaboration with biomedical engineers, which creates engaging games for children’s physical rehabilitation. An example is a “bubble popping” game, developed by last year’s SURE student in 2024, where the player walks around and practices upper motor control to pop virtual bubbles. The simplified games developed in this project will serve as interactive platforms where middle schoolers can learn basic coding concepts while exercising their creativity to develop engaging therapeutic exercises. By integrating coding elements into the game mechanics, middle schoolers will gain hands-on experience in programming, fostering problem-solving skills and computational thinking. The student participating in this project will gain experience in AR/VR app development through Unity, STEM education concepts, and systems development. The developed games may be deployed with our outreach partners at their Detroit center that regularly serves local middle and high schools.

Faculty mentor:
Jiasi Chen
[email protected]

Hun-Seok Kim
[email protected]

Research area(s):
Computer Vision
Embedded Circuits

Course format:
In person

Prerequisites:
Required: EECS 280
Preferred: EECS 281

Experience with Unity is desirable but not required.

Description:
iGYM is a spatial augmented reality (AR) system for inclusive play and exercise. This project focuses on improving the tracking accuracy and responsiveness of iGym to enhance the user experience. Currently, iGYM uses a single ceiling-mounted camera to track player movements and a peripersonal circle interaction feature to translate those movements into gameplay. The student will measure current latency bottlenecks in the system and and implement solutions with the goal of minimizing delays between player actions and system responses, ensuring a smoother and more realistic gameplay experience. Potential approaches include re-architecting the current publish/subscribe communication protocol or adding multi-threaded processing. The student will also investigate the accuracy of the player tracking algorithm and design approaches to improve accuracy and reduce jitter of the displayed peripersonal circle as the user moves around the scene. Potential approaches include Kalman filters or linear regression. The student participating in this project will gain experience in systems development, computer vision, ROS, and augmented reality.

Faculty mentor:
Mohammed Islam
[email protected]

Research area(s):
Computer Vision
Signal Processing
AI/ML
Optics & Photonics
Biomedical Engineering

Course format:
In person

Prerequisites:
Required: Software programming (Python) to process 3D camera output

Description:
Three-dimensional (3D) cameras are becoming commodity items, as they are being used in smartphones, tablets, and AR/VR/mixed reality headsets for various applications. Some of the 3D cameras we are using include indirect time-of-flight cameras, direct time-of-flight cameras co-registered with infrared cameras, and structured light cameras. Using these cameras, we are looking at features on a person’s face, such as facial blood flow, physiological parameters (e.g., heart rate and respiratory rate), and we are now extending to other parameters such as blood oximetry and blood pressure. In a hospital setting, instruments are used to measure a patient’s heart rate, respiratory rate, blood oximetry and blood pressure, so we want to obtain all of these parameters in a contactless, non-intrusive manner. Ambient light sensitivity is minimized by using active illumination from LEDs or lasers, and motion artifacts are compensated by using the depth information from the 3D cameras as well as using AI-based face tracking. Machine learning will also be used to determine personalized baselines for an individual, and then anomalous occurrences will be detected using algorithms such as anomaly detection, generative adversarial networks, or auto-encoders. The first task in the project is to improve the software processing for facial blood flow and what is known as remote photo-plethysmography (rPPG). Then, the second task will be to extend the measurements to include blood oximetry and blood pressure by comparing visible and near-infrared wavelength signals as well as different parts of the body, such as the hand and facial blood flows. We are looking to collaborate with various hospital systems to use the contactless vital sign monitoring systems in intermediate care rooms, rehabilitation facilities, and even extending to nursing homes and self-monitoring in the home.

Faculty mentor:
Necmiye Ozay
[email protected]

Research area(s):
Control Systems

Course format:
Hybrid (combination of online and in person)

Prerequisites:
Knowledge of linear algebra and control of linear dynamical systems. Knowledge of linear programming and probability is a plus. Familiarity with coding (matlab, python, or julia).

Description:
This project will investigate several invariance-based control algorithms for ensuring safety of dynamical systems. While worst-case methods have strong safety guarantees, they can lead to quite conservative behaviors (e.g., if we assume the worst-case for a vehicle, maybe the safest thing is not to operate the vehicle at all).

The question that will be investigated is how to enforce safety when making different assumptions on the unknowns in the system. We aim to develop a new class of safety controllers that can be adjusted to be more optimistic or pessimistic. If time permits, we will also investigate probabilistic notions of safety and algorithms for learning uncertainty models. There is also an opportunity to implement the developed algorithms on small drones in the lab.

For some background results from last year’s SURE project, please see: https://ozay-group.github.io/OppSafe/

Faculty mentor:
Necmiye Ozay
[email protected]

Research area(s):
Control Systems

Course format:
In person

Prerequisites:
Required: Familiarity with coding (matlab, python, or julia)
Required: Knowledge of linear algebra and Markov Decision Processes or RL.

Knowledge of linear programming and probability is a plus.

Description:
In this project we will investigate how Large Language Models (LLMs) can be used to improve autonomous systems (e.g., autonomous vehicles). Two potential areas that will be investigated are 1) safe personalization of behaviors of systems based on human preferences, given in natural language, 2) counterfactual reasoning to recover from failures and to improve resilience.

Faculty mentor:
S Forrest

Contact:
Sritoma Paul
[email protected]

Research area(s):
Electrical Engineering
Materials Science
Physics

Course format:
In person

Prerequisites:
Semiconductor device course or equivalent

Description:
High efficiency blue OLEDs are the “weak link” of all OLED displays used in mobile phones, IT appliances and TVs. They wear out faster than the other color pixels (i.e. red and green). In this project we tackle this problem by fabricating very high efficiency blue emitting OLEDs, and find ways to increase the radiative rate of the emitting molecules. This avoids annihilation of excited states that results in a very high energy destructive process that destroys the molecules. The student will have a chance to make and analyze the properties of blue OLEDs in a highly advanced fabrication laboratory, and to assist a graduate student or post-doc in finding ways to increase their operational lifetime.

Faculty mentor:
Paul Campbell

Research area(s):
Optics & Photonics

Contact:
Elizabeth Oxford
[email protected]

Course format:
In person

Prerequisites:
None

Description:
The ZEUS laser facility at Michigan is an NSF funded user facility operating on North Campus. It is presently the highest power laser in the US and will soon operate at 3 PetaWatts peak power. This SURE project involves development of liquid droplet and liquid jet targets to be used as high repetition rate targets for experiments in the ZEUS laser facility. These experiments will address compact particle acceleration techniques, neutron generation as well as the production of x-ray radiation.

Faculty mentors:
John Nees

Contact:
Elizabeth Oxford
[email protected]

Research area(s):
Optics & Photonics

Course format:
In person

Prerequisites:
Recommended: EECS 334

Description:
The ZEUS laser facility at Michigan is an NSF funded user facility operating on North Campus. It is presently the highest power laser in the US and will soon operate at 3 PetaWatts peak power. This SURE project involves development of diagnostic techniques and control systems for the laser system towards improvements of the pulse duration and laser beam quality.

Faculty mentor:
Liyue Shen
[email protected]

Course format:
Hybrid (combination of online and in person)

Prerequisites:
Machine Learning, Computer Vision, Python, PyTorch

Description: This project focuses on developing a robust and generalizable framework for multimodal learning by leveraging cross-modal fusion and a carefully designed latent embedding space that combines modality-specific and modality-shared representations. The research aims to address key challenges in multimodal data integration and provide foundational insights into designing models adaptable to diverse medical modalities and tasks. The outcomes of this study promise to enhance the capabilities of multimodal learning systems, particularly in biomedical applications, setting new standards for innovation in the field.

Faculty mentor:
Samet Oymak

Research area(s):
Machine Learning

Course format:
In person

Prerequisites:
Required: Experience in using PyTorch or Jax
Required: Basic machine learning, linear algebra, and statistics background Preferred: Previous experience with language model training or use of language model APIs (e.g. OpenAI, Claude, Gemini)

Description:
The objective of this project is using large language models (LLMs) to rank a set of items. Consider the following applications: (1) We want an LLM to generate high-quality reviews/scores for academic papers. (2) We want to use the LLMs to decide on the best item (such as car) to buy given a consumer’s context. (3) We want to compare and rank how well several companies are doing for investment purposes. All these applications require a strong degree of “reasoning about context” where modern LLMs can assist. In this project, you will explore the use of state-of-the-art LLMs to accomplish such tasks under the generic problem umbrella of “LLMs as rankers”. This project is expected to introduce novel engineering challenges such as prompting, retrieval augment generation, fine-tuning as well as statistical considerations such as uncertainty quantification. The undergraduate student researcher is not expected to solve the whole problem by themselves, they will also be mentored and supported by a graduate student researcher.

Faculty mentor:
Leung Tsang

Contact:
Jongwoo Jeong
[email protected]

Research area(s):
Applied Electromagnetics

Course format:
In person

Prerequisites:
EECS 330, Math 216, and Linear Algebra (e.g., Math 214) or equivalent courses

Description:
Numerous microwave remote sensing satellites have been launched with many more planned for launch in the next decade. Remote sensing of the Earth’s atmosphere, ocean, land surfaces, forests, and cryosphere have become crucial for monitoring our planet’s resources, managing disasters, and for the study of global climate change. Satellite remote sensing is the tool that provides global monitoring at frequent update rates. Microwave sensors include Synthetic aperture radar, radar interferometry, passive radiometry, and GNSSR reflections. Understanding the signals and performing data analysis require the knowledge of scattering of microwaves by geophysical environment such as vegetation, crops, forests, snow, ice, soil surfaces and ocean surfaces. The wave interactions are governed by Maxwell equations. This project aims to simulate the solutions of 3D Maxwell equations for these scattering problems. The project will use commercial electromagnetic simulation tools such as FEKO and HFSS in combinations with in-house wave simulation tools. The simulation results will also be compared with satellite, airborne and ground based measurements that have been collected by satellite missions. Through this research, the student will acquire the skills of using FEKO and HFSS, knowledge of electromagnetic theory, radar scattering, passive radiometry, and earth science applications. This opportunity will be a cornerstone of your research career in microwave engineering. Our graduate students are dedicated to providing full support to participants.

Faculty mentor:
Lei Ying
[email protected]

Research area(s):
Network, Communication & Information Systems

Course format:
Hybrid

Prerequisites:
Familiarity with reinforcement learning algorithms, pytorch, communication systems

Description:
Improve the performance of reinforcement learning algorithms for communication systems/networks.