Two ECE students receive the Ashok and Yohavalli Sethu Electrical and Computer Engineering Annual Scholarship 2021-2022

Roya Abdulrazeq is a junior studying Electrical and Computer Engineering with a concentration in Computer Engineering. She graduated from Middlesex Community College with the Degree of Associate in Science with a concentration in Engineering Science in May 2021. She transferred to Rutgers this fall. She is currently a member of the Society of Women Engineers and Phi Theta Kappa Society. She is passionate about computer engineering because she likes coding and solving problems.

Eoin O'Hare is a junior majoring in Computer Engineering with a minor in Computer Science. Over the summer, he was a Technology Intern at Comcast, and worked on writing scripts in various coding languages to handle account information. Outside of classes, he likes to play flag football in the intramural league, is the Build Manager for Rutgers Habitat for Humanity, and is a member of Theta Tau, the professional engineering fraternity. After graduating, Eoin plans to write code in meaningful projects that can make people's lives better and easier.

Laleh Najafizadeh receives NIH Grant for developing Networked Neuroprosthesis for Spinal Cord Injuries

ECE Associate Professor Laleh Najafizadeh is the recipient of a new National Institutes of Health (NIH) award for the research project titled "Augmenting Implanted Neuroprosthetics with Targeted Health Monitoring for Spinal Cord Injury - the LIFELINE" through the National Institute of Biomedical Imaging and Bioengineering.  This 4-year collaborative R01 grant led by Case Western Reserve University is funded at $2.6 million with Rutgers share being $262,560. 
 
In this project, Dr. Najafizadeh will work with her collaborators to develop an implanted health monitoring device, called the "Lifeline" device, which senses health-related parameters, including temperature, electrocardiogram, photoplethysmogram, inertial measurement, and acoustic signals.  The Lifeline device will be designed as an integral component of the "Networked Neuroprosthesis" (NNP)  for patients with spinal cord injury (SCI). The NNP system provides a platform for implementing and evaluating the benefits of implanted health monitoring while minimizing the development costs and regulatory hurdles.  An exciting outcome of the Lifeline-enhanced NNP system is the capacity to provide advanced warning regarding the top causes of increased mortality in individuals with SCI, enabling earlier detection and medical intervention that may ultimately increase the overall life expectancy of patients. The causes of early mortality include pneumonia, urinary tract infection (UTI), pulmonary embolism (PE), and autonomic dysreflexia (AD), which are unique to, or more prevalent in, people with SCI (particularly tetraplegia). The addition of the Lifeline device to the NNP system is the first step towards an implantable "life-saving neuroprosthesis". The completion of this project will result in a single modular system that will be capable of providing both improved health and improved function for anyone with SCI, thus prolonging life while, at the same time, increasing independence and quality of life.
 
More details on the project can be found here.

 

Congratulations, Laleh!
 

Salim El Rouayheb receives ARL Grant to Design Machine Learning Algorithms for Tactical Edge Communication Networks

ECE Associate Professor Salim El Rouayheb is the recipient of a new award from the Army Research Lab (ARL) for the project titled "Machine Learning Algorithms and Optimizations for Resource-Constrained Tactical Edge."  Dr. El Rouayheb is the Rutgers PI on a three-year $1.2 million collaborative effort with the University of Illinois at Chicago (UIC), with Rutgers share of the award being $497,000.
 
In this project, Dr. El Rouayheb and his collaborators aim to improve the speed and accuracy of AI-based edge solutions through a  model-distributed framework for faster and lightweight AI/ML inferencing. The focus will be on understanding the potential and impacts of model distributed inferencing for resource-constrained tactical edge networks. The project will first investigate early exit mechanisms based on E2CM, a novel Early Exit technique based on Class Means of samples.  Moreover, the project will study decentralized learning algorithms, for real-time learning and inferencing. The security of these algorithms against passive and adversarial attacks will be investigated using secure coded computation. Finally, the project will develop a heterogeneous distributed computing testbed consisting of commodity end devices and edge servers to implement and validate the obtained algorithms.

 
Congratulations, Salim!

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