Hang Liu received a new grant from the National Science Foundation titled "ExpandQISE: Track 1: Analog quantum simulation of non-Markovian dynamics of multi-qubit systems". It is a collaboration between Rutgers and the New York Institute of Technology. Our share is $195K. (Total award is $650K for three years.)

A multi-qubit system is used in various quantum technologies, including quantum communication, quantum sensing, quantum cryptography, and quantum simulation. Since any quantum system cannot be fully isolated from the environment, open quantum systems are introduced to model the evolution of a quantum system while considering the interactions between the quantum system and the environment. Depending on the strength and the type of this interaction, there are two types of open quantum systems dynamics - Markovian and non-Markovian, where the non-Markovian dynamics are more accurate. In this research, the project team will advance and promote the research on analog quantum simulation of non-Markovian dynamics of multi-qubit systems. In addition, this research will implement an investment and reward feedback loop for inspiring K-12 students and attracting, retaining, and educating undergraduate, female, and underrepresented minority students by exposing them to this quantum-related research. Further, this project broadens and strengthens the current quantum physics curriculum at the undergraduate level by enhancing existing courses and creating new ones.

More project information can be found here: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328948&HistoricalAwards=false.

This is the second NSF grant that Hang received in this NSF funding cycle. Congratulations to Hang!

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 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

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.

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!