Dr. Masudul Imtiaz’s research lab focuses on developing advanced ultra-low-power wearable sensor systems for real-time, non-invasive health monitoring. These sensors aim to provide accurate, continuous tracking of the wearer’s health status. Utilizing prior designs, including multi-sensor platforms, this project addresses the need for compact, personalized wearables that combine high performance with user comfort. The project’s objective is to enable edge comoputing capabilities directly within the sensor, optimizing performance within a constrained power budget. REU students will assist in designing AI prototypes for health sensing, collaborating with the Center for Advanced PCB Design and Manufacture (CAPDM) facility and AI Vision Lab to validate new sensor systems and assess their commercialization potential. The project includes creating a custom PCB for testing using ultra-low-power ARM processors with BLE communication. Students will evaluate sensor performance, durability, data quality, and reliability through human studies, supporting long-term health monitoring for proactive healthcare and enhancing the work of healthcare providers and educators.

Led by Dr. Stephanie Schuckers, director of NSF Center for Identification Technology Research (CITeR), this project develops advanced biometric sensors to enhance national security. Working alongside industry partners like the FBI, DHS, and DoD, this research focuses on next-generation biometric systems with increased robustness against spoofing and privacy vulnerabilities. REU students will participate in building flexible biometric sensors from COTS components and updating firmware to improve biometric authentication. Students will balance trade-offs among accuracy, computational cost, and memory footprint in applications such as wearable mID and fingerprint-enabled IoT devices. The project will also involve developing embedded processors on medical wearables, enhancing biometric recognition, and evaluating performance across diverse demographics.

Dr. Xiaocun Lu’s team explores biomechanics imaging for biomedical applications, focusing on the design and sensitivity optimization of near-infrared mechanical sensors. Students will work on the molecular design and synthesis of biomechanical sensors, enabling deeper detection within biological systems. REU participants will use computer simulations to evaluate molecular sensitivity, followed by synthesis and characterization of the sensors. They will optimize sensitivity and biocompatibility, enhancing sensor penetration in aqueous conditions. Students will thus gain experience in polymer synthesis, sensor design, and simulation techniques, contributing to the development of sensors tailored for biological applications.

Dr. Silvana Andreescu’s research group develops portable sensors for detecting environmental contaminants, including per and poly-fluoroalkyl substances (PFAS), funded by NSF, USDA, and the US Army. The project involves designing and fabricating sensors on PCBs with polymer recognition properties for PFAS and other contaminants. REU students will modify electrochemical sensors and calibrate them for specific contaminant detection. Students will perform data analysis and QC/QA to validate the sensors and apply them to detect contaminants in water samples from nearby Superfund sites and other sources, gaining expertise in molecular recognition, sensor fabrication, and environmental testing.

Indoor air quality, specifically the presence of airborne particles, significantly impacts human health. Led by Dr. Suresh Dhaniyala, this project develops sensors capable of detecting particles as small as 10 nm and assessing biological particle diversity in the air. REU students will explore sensor performance, optimize data representation, and develop new sensor modalities. Working within Clarkson’s Center for Air and Aquatic Resources Engineering and Sciences (CAARES), students will gain practical experience in environmental pollution management, leveraging advanced sensor technologies to monitor and analyze airborne contaminants.

Dr. Kevin Fite’s project focuses on the integration of multi-sensory technology into powered prosthetic limbs, enabling advanced control strategies for amputees. REU students will engage in sensory fusion research, combining measurements from wearable sensors with the prosthetic limb’s state data to facilitate user-driven control. This work supports the development of field-deployable prosthetic limbs, bridging the performance gap between prosthetic devices and natural limbs. Students will contribute to electronic architecture design, system integration, and the evaluation of the interaction mechanics, gaining practical experience in intelligent prosthesis control.

Walking re-education is an essential component in patient rehabilitation to improve ambulatory function, activities of daily living and re-integration of the individual into society. Rehabilitation specialists make several clinical decisions on walking re-education based on functional outcome measures such as the six-minute walk test (6MWT), ten-meter walk test (10MWT), time up and go test (TUG), dynamic gait index (DGI) and functional gait assessment (FGA). These valid and reliable measures help clinicians to design appropriate walking training strategies to improve cardiovascular and motor performance in diseased populations and monitor their progress and the effectiveness of the treatment. However, they lack the ability to assess the quality of limbs and body movements musculoskeletal forces, which are critical components of gait. Hence, a patient may complete, for instance, the 6MWT within an appreciable shorter distance but have poor walking indices and endurance that can delay the rate of gait recovery. Delay in gait recovery can result in increased disability, late resumption to work, loss of income and pose a negative impact on the economy. This sensor based reseach will be done at Prof Kwadwo Appiah-Kubi’s lab.

Dr. Abul Baki’s team is developing an AI-vision-based sensor to detect microplastics in aquatic environments, addressing the limitations of traditional monitoring methods. USB cameras interfaced with the computer and Deep Learning based object detection model track microplastic particles in a lab setup, while a Deep-SORT model determines their velocities. The next phase will enable real-time detection using a portable NVIDIA Jetson AGX processor. REU students will explore optimal processor and camera models, collaborating with Clarkson’s Center for Advanced PCB Design and Manufacturing (CAPDM) and AI Vision Lab to refine and test the system across various water bodies, developing firmware in a Linux environment for broader field applications.

Microbial populations play a crucial role in river ecosystems, and imbalances, such as those caused by cyanobacterial blooms, can pose serious risks to plant and animal life. Analyzing microbial populations using currently available techniques such as sequencing or quantitative PCR is often costly and labor-intensive, limiting their use in routine monitoring. Dr. Shantanu Sur’s group aims to address this challenge by building a prediction model of microbial abundance from the physicochemical properties of river water. These properties can be measured through existing sensor-based methods and, thus, would enable high-frequency, low-cost monitoring of river microbes. REU students will perform DNA extraction, sequencing, and bioinformatics analysis to assess the microbiome in the tributaries of the St. Lawrence River. They will also engage in modeling and data analysis to identify key predictors of microbial composition from sensor-derived measurements.

A compressed pharmaceutical oral solid dosage (OSD) form consists of a tightly bound network of particles, with its quality attributes such as disintegration, drug release, and hardness affected by its micro-scale properties. Ultrasonic evaluation offers a rapid, cost-effective, and non-destructive way to assess these properties, but extracting the micro-properties from the ultrasonic data is a complex mathematical challenge. In the REU project, a new machine learning approach, using Multi-Output Regression models and Neural Networks, will be introduced to extract these micro-properties directly from ultrasonic waveforms. Virtual tablet waveforms will be created to train and test these ML models. These models will then be used on real OSD tablets, successfully determining their micro-properties and demonstrating the method's potential for practical application.

This REU project focuses on leveraging FPGA technology for real-time AI-powered video processing and computer vision applications, particularly targeting scenarios where low latency and efficient power usage are critical. The project aims to develop optimized AI models that can be deployed on FPGAs for tasks such as real-time object detection, deepfake detection, and activity recognition. Unlike traditional CPU or GPU systems, FPGAs offer the advantage of customizable hardware that can be fine-tuned for specific AI workloads, providing high performance within a limited power budget.REU students will assist in developing and optimizing AI models on an FPGA platform using the Xilinx Kria KV260 Vision AI Starter Kit. They will explore techniques for accelerating convolutional neural networks (CNNs) and transformer models on FPGAs to achieve real-time processing speeds. The project also includes designing hardware modules, testing on real-world video datasets, and implementing efficient data transfer protocols between sensors and the FPGA board. By the end of the project, students with Dr. Masudul Imtiaz will have developed a comprehensive understanding of AI hardware acceleration and gained hands-on experience in deploying vision-based AI systems on FPGAs, making them well-equipped for careers in embedded AI and hardware design.

This REU project, supervised by Dr. Shafique Chaudhry, will focus on developing a Virtual Reality (VR)–based rehabilitation application integrated with haptic glove technology to support physical therapy and motor recovery. The goal is to create an immersive and interactive VR environment that guides users through therapeutic exercises in real time. During these sessions, haptic gloves and embedded sensors will provide tactile feedback while continuously capturing high-resolution motion and pressure data to monitor performance and progress. REU students will work on designing and programming VR exercises, integrating sensor data with the VR platform, and analyzing user performance in real time. Through this project, students will gain hands-on experience in VR development, sensor integration, and data-driven rehabilitation analytics, contributing to the next generation of intelligent and accessible rehabilitation technologies.

This REU project, supervised by Dr. Masudul Imtiaz, will focus on creating ultra-low-power wearable sensors for non-invasive monitoring of infant health. The goal is to develop flexible, comfortable sensors that can continuously track vital signs like heart rate, breathing patterns, and movement. REU students will work on designing sensor prototypes, integrating microcontrollers, and developing algorithms for real-time data processing. This project includes testing the sensors on a custom PCB, optimizing for accuracy and energy efficiency. Students will gain skills in sensor design, embedded systems, and data analysis, contributing to advancements in infant health monitoring technologies.