Umer Hassan received NSF grant from Partnerships for Innovation - Technology Translation (PFI-TT)

Umer Hassan received an award from National Science Foundation (NSF) for the project “PFI-TT: Immuno-Dx: A Biomedical Platform Technology for Personalized Diagnostics”. This is a 2-year, single PI project with a total budget of $550,000.

 

The primary objective of this Partnerships for Innovation - Technology Translation (PFI-TT) project is developing a biomedical device capable of detecting and monitoring patient's ability to combat infections. The proposed technology will address the unmet need in emergency department settings of the hospitals where it can be used to monitor patients’ response to therapeutic treatments and identify high-risk patients. A minimal viable prototype (MVP) will be developed from proof-of-concept biosensing technology (called Immuno-Dx), which is centered around monitoring natural ability of blood cells to kill pathogens. Immuno-Dx can have applications in areas (i) to better understand immune system responses of patients to pathogenic infections, (ii) to develop new immunotherapy drugs by pharmaceutical companies, and (iii) to strategize patient treatments by physicians. Biosensing device will be able to provide information regarding patients’ ability to combat infection within 30 min from a drop of whole blood. This PFI-TT project will enable workforce development in spirit with the NSF mission of training next generation of scientists and engineers in technical and entrepreneurial skills, while creating a direct impact on national healthcare and aiding the US economy. The potential outcome of PFI-TT proposal will be the transition of Immuno-Dx technology from PI’s research laboratory to a commercial startup company.

 

More details on the project can be found at the NSF page https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329761&HistoricalAwards=false

Emina Soljanin received a new NSF award on Maximizing Coding Gain in Coded Computing

Emina Soljanin received a new NSF grant titled Collaborative Research: CIF: Small: Maximizing Coding Gain in Coded Computing. It is a collaboration between Rutgers and Texas A&M University. Rutgers is the project's lead institution, with a share of $375K. (Total award is $600K over three years.) 

Artificial intelligence and machine learning algorithms rely on parallel, distributed computing systems to efficiently carry out intricate, data-heavy tasks. A significant challenge in designing large-scale distributed computing systems is addressing the unpredictable variations in service times across multiple servers. Computing redundancy, such as task replication, is a promising powerful tool to curtail the overall variability in service time. This project focuses on the intelligent management of redundancy in distributed computing that will affect the execution efficiency of data-intensive algorithms in large-scale systems.

More information is available at

https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327509&HistoricalAwards=false

 
Congratulations to Emina!
 

Laleh Najafizadeh received a new NSF award on Uncovering Dynamics of Neural Activity of Subjective Estimation of Time

ECE Associate Professor Laleh Najafizadeh is the PI for a new NSF award, from the “Integrative Strategies for Understanding Neural and Cognitive Systems (NCS)” program, for her research project titled "NCS-FO: Uncovering Dynamics of Neural Activity of Subjective Estimation of Time”.  This is a three-year project with a total budget of $879,091.  The Co-PI on this project is Professor Tracey Shors from the Department of Psychology.

Research suggests that our experience of time can be influenced by various internal and external factors, yet little is known about the neural mechanisms that underlie such variability. To address this knowledge gap,  this project develops an innovative data-driven computational tool, designed specifically to find the differences in the dynamics of neural activity (in time and space) between two neural data matrices. The proposed approach utilizes matrix factorization, and deviates from most conventional analyses, which make stronger assumptions about the nature of the underlying neural processes (e.g., temporal or spatial adjacency). When combined with brain imaging and behavioral measurements, the proposed computational tool can localize specific neural activities that give rise to the observed differences, as well as identify when these activities occur. The project aims to develop this technique and utilize it to study the neural mechanisms underlying changes in an individual’s subjective estimation of time while three factors are manipulated (memory, sensory processing, and arousal). The outcome of this project is expected to result in a new approach for analyzing neuroimaging data, and advancing our scientific understanding of how the brain perceives time. The knowledge gained could also have implications for neurological and psychiatric disorders, such as Parkinson’s disease and attention-deficit/hyperactivity disorder (ADHD), for which changes in time perception are common.

 
 
Congratulations to Laleh!

Ivan Marsic received a continued grant from NIH/NBIB on Developing a Video-based Personal Protective Equipment Monitoring System

ECE Professor Ivan Marsic has received a continued grant from NIH/NBIB on the project "Development of a Video-based Personal Protective Equipment Monitoring System." 
 
The continued grant comes with an additional three years (09/01/2023 - 06/30/2026) to the amount of $1,511,833, of which Rutgers part is $487,687, and Ivan is the sole PI on Rutgers side. Last year the project received a one-year funding for this project that totaled $657,223 (Rutgers part: $202,000). Combined, this grant is funded for 4 years (09/01/2022 - 06/30/2026) to the amount $2,169,056, of which Rutgers funding for all 4 Years is $689,687. The lead institution is Children's Institute in Washington, DC, and subcontractors are Rutgers and Drexel Universities.
 
During the COVID-19 pandemic, healthcare workers (HCWs) have had a more than 11-fold higher infection risk than the general population. Several risk factors for COVID-19 infection among HCWs have been identified, including the lack of personal protective equipment (PPE) and inadequate PPE use. Among these factors, the inadequate use of PPE has been associated with a one-third higher risk of infection. Given the high incidence of infection, there is a critical need to address the challenges of monitoring and promoting adherence with appropriate PPE use among HCWs. The long-term goal of this research is to reduce workplace-acquired infections in HCWs by improving adherence to appropriate PPE use in settings at high risk of transmission. The overall objectives of this proposal are to design, implement, and test a system (Computer-Aided PPE Nonadherence Monitoring and Detection—CAPPED) that (1) tracks the team’s PPE adherence using computer vision and (2) highlights episodes of potential PPE nonadherence on a video-monitoring system. Our central hypothesis is that continuous monitoring of PPE use by multiple HCWs is a complex, cognitively demanding, and error-prone task unaddressed by current methods for monitoring PPE adherence. The rationale for this proposal is that enhanced recognition of PPE nonadherence is a requirement for reducing transmissible infections in HCWs.  Guided by preliminary data, the central hypothesis will be tested by pursuing two specific aims: (1) design and implement a computer vision system (CAPPED) for recognizing PPE nonadherence in a dynamic, team-based setting, and (2) compare human performance during simulated resuscitations using direct observation, basic video surveillance, and computer-aided monitoring (CAPPED system). For the first Aim, machine learning approaches will be applied to recognize the type of nonadherent PPE (headwear, eyewear, mask, gown, gloves) and the category of nonadherence (absent or inadequate). Under the second Aim, a customizable visual interface will be designed and evaluated for monitoring and spotlighting PPE nonadherence with a human-in-the-loop. The proposed research is innovative because it addresses the challenges of simultaneously identifying nonadherence with several types of PPE used by multiple individuals in a dynamic setting. This proposed research is significant because it is expected to reduce infection transmission to HCWs by tracking and eventually alerting them to nonadherent PPE use. The results of this research are expected to positively impact the workplace safety of HCWs by addressing the limitations of current approaches to PPE monitoring.
 
Congratulations to Ivan!

ECE Alumna Sennur Ulukus honored as 2023 Distinguished University Professor

ECE Alumna Sennur Ulukus is the first woman faculty member from engineering to be named a Distinguished University Professor at the University of Maryland. The title is the highest appointment bestowed on a tenured faculty member; it is a recognition not just of excellence, but of impact and significant contribution to the nominee’s field, knowledge, profession, and/or practice. Since joining Maryland in 2001, she has been named a UMD Distinguished Scholar-Teacher, received the named Anthony Ephremides Professorship in Information Sciences and Systems, served as the Department of Electrical and Computer Engineering (ECE)’s associate chair for graduate studies, and co-founded the Professional Masters Program in Machine Learning; since 2022, she has served as chair of ECE. Ulukus has been honored with an IEEE Marconi Prize Paper Award in Wireless Communications, NSF CAREER Award, IEEE Communications Society Best Tutorial Paper Award, IEEE Communications Society Women in Communications Engineering Outstanding Achievement Award, and IEEE Communications Society Technical Committee on Green Communications and Computing Distinguished Technical Achievement Recognition Award. She is a Fellow of IEEE.

Congratulations!

Umer Hassan received a new NSF grant from ECCS program

ECE Assistant Professor Umer Hassan received an award from National Science Foundation (NSF) for the project “A Medical Device Enabled by Portable Fluorescence Microscopy and Microfluidics for Monitoring Surgical Inflammation Biomarkers”. This is a three-year, single PI project with a total budget of $420,000.

Personalized immune response monitoring of patients to infections is critical not only for early diagnosis but also for determining effective therapy, thereby having a significant impact in patient’s health outcomes. Surgical site infection (SSI) is a common condition faced by patients post-surgical procedures. Early visual indications of SSI include inflammation and puss at the site of the wound, pain, fever, and discomfort. This is followed by a microbial culture which takes multiple days to get the results, leaving huge diagnostic gaps in the treatment pathway. Post-operative frequent quantification of clinically approved biomarkers on high-risk patients could provide an early indication of an SSI, however, their testing requires centralized lab facility, trained professionals and longer wait times to get the results. In this proposal, investigators envision a biomedical platform for inflammatory proteins quantification using only a drop of whole blood. The proposed innovation is based on integration of photonics, microfluidics, smartphone enabled optical sensing, and 3-D multi-layer microfluidic architectures on a single biochip with automated whole blood processing to provide clinical test results from patient samples. Sensors will be equipped with real-time measurement capability and machine learning models to train the sensors data and provide test results. Sensors will be benchmarked with patient samples collected from Robert Wood Johnson Medical Hospital. This transformative research will also open new educational initiatives to train the next generation of engineers and scientists.

More details on the project can be found at the NSF page https://www.nsf.gov/awardsearch/showAward?AWD_ID=2315376&HistoricalAwards=false.

Congratulations to Umer!

Hang Liu received a new NSF grant from CICI on Prompt, Reliable, and Safe Security Update for Cyberinfrastructure

ECE Assistant Professor Hang Liu received a new grant titled "CICI: TCR: Prompt, Reliable, and Safe Security Update for Cyberinfrastructure" from NSF. It is a collaboration between Rutgers and the University of Utah. There is only one institute per submission. Rutgers is the sub-awardee of the project, and its share is $300K. (Total award is ~$1.2 million for three years.)

Cyberinfrastructure plays a crucial role in the nation's critical infrastructures, driving advancements in science, engineering, education, and collaboration. However, cyberinfrastructure also becomes a perfect target for cyber attackers due to its high-value data (e.g., nuclear test results) and massive computing resources (e.g., supercomputers). As shown by data, cyber-attacks are happening to cyberinfrastructure more frequently, causing remarkable damage to the economy, environment, public health, and even national security. This project aims to transition recent cybersecurity advancements and techniques for enhancing cyberinfrastructure resilience under cyber threats. In particular, this project ensures that security updates, or patches, for software systems running on cyberinfrastructure, are adopted in a timely, reliable, and safe way, which can help eliminate up to 85 percent of targeted attacks, according to US-CERT. The research outcomes advance the scientific study of software patching under challenging conditions such as limited monetary resources, insufficient admin expertise, and highly diverse environments. The research outcomes are also projected to deploy to large-scale cyberinfrastructure platforms, including Utah CHPC (a 5,600-user platform) and many similar platforms (e.g., PNNL, ORNL, and the Rutgers Office of Advanced Research Computing).
 
More project information can be found at here: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319880.

Congratulations to Hang!

Yingying Chen received Rutgers Brain Health Institute Pilot Grant

ECE faculty Yingying Chen is a part of the team receiving the Rutgers Brain Health Institute (BHI) Pilot Grant in Neuroscience for Center and Program Project Grants. The proposal is titled as An Integrated Approach to Build Precision Medicine for Substance Use Disorders. The pilot project aims to establish the foundation for an NIH NIDA Center of Excellence focused on building precision medicine approaches for substance use disorders (SUDs). The project brings together expertise in neuroscience, genetics, neuroimaging, electrical engineering, computational psychiatry, digital phenotyping, and clinical research to create an integrated approach to developing more targeted treatments and interventions for substance use disorders, which continue to be among the most costly health challenges our society faces. This project tightens the collaborations between SoE and RBHS. The pilot grant is $125,000 for a one-year period.

Emina Soljanin won 2023-24 Urmila Agrawal Distinguished Visiting Chair Professorship at IISc

Professor Emina Soljanin has won the 2023/24 Urmila Agrawal Distinguished Visiting Chair Professorship at the Indian Institute of Science (IISc). This professorship is an institute-level award with a single recipient across all fields per school year. More information is available at https://odaa.iisc.ac.in/urmila_agrawal/.

 

IISc scientists are one of the leading groups in India in Emina's field of research. She will visit IISc Quantum Technology Initiative (IQTI) and the Centre for Networked Intelligence (CNI)  for four to ten weeks in the Summer of 2024.

 

Congratulations to Emina!

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