ECE Associate Professor Mehdi Javanmard and his research group have published a paper on sensors for measuring stress hormones that was featured in the journal Science Advances (AAAS). The article titled "Single-step label-free nanowell immunoassay accurately quantifies serum stress hormones within minutes" is featured on the cover of the latest issue of AAAS. 

Congratulations to Mehdi and his team!

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A Rutgers-led team of researchers has developed a microchip that can measure stress hormones in real-time from a drop of blood.

The study appears in the journal Science Advances.

Cortisol and other stress hormones regulate many aspects of our physical and mental health, including sleep quality. High levels of cortisol can result in poor sleep, which increases stress that can contribute to panic attacks, heart attacks and other ailments.

Currently, measuring cortisol takes costly and cumbersome laboratory setups, so the Rutgers-led team looked for a way to monitor its natural fluctuations in daily life and provide patients with feedback that allows them to receive the right treatment at the right time.

The researchers used the same technologies used to fabricate computer chips to build sensors thinner than a human hair that can detect biomolecules at low levels. They validated the miniaturized device’s performance on 65 blood samples from patients with rheumatoid arthritis.

"The use of nanosensors allowed us to detect cortisol molecules directly without the need for any other molecules or particles to act as labels,” said lead author Reza Mahmoodi, a postdoctoral scholar in the Department of Electrical and Computer Engineering at Rutgers University-New Brunswick.

With technologies like the team’s new microchip, patients can monitor their hormone levels and better manage chronic inflammation, stress, and other conditions at a lower cost, said senior author Mehdi Javanmard, an associate professor in Rutgers’ Department of Electrical and Computer Engineering.

“Our new sensor produces an accurate and reliable response that allows a continuous readout of cortisol levels for real-time analysis,” he added. “It has great potential to be adapted to non-invasive cortisol measurement in other fluids such as saliva and urine. The fact that molecular labels are not required eliminates the need for large bulky instruments like optical microscopes and plate readers, making the readout instrumentation something you can measure ultimately in a small pocket-sized box or even fit onto a wristband one day."

The study included Rutgers co-author Pengfei Xie, a Ph.D. student, and researchers from the University of Minnesota and the University of Pennsylvania. The research was funded by the DARPA ElectRX program.

Story by John Cramer, Rutgers Today

ECE Associate Professor Waheed Bajwa has been awarded a grant from the Army Research Office (ARO) for the project titled "Statistical learning for the modern datasets: Generalization bounds and near-optimal learning algorithms." The 3-year, $360,000 award will advance the state-of-the-art in statistical learning theory and lead to computationally efficient algorithms for machine learning. While the statistical learning framework has long played a central role in advancing our understanding of machine learning systems, there is an interest in looking afresh at the questions of generalization error bounds, fundamental limits, and near-optimal algorithms in the face of modern datasets that increasingly represent a ‘zoo’. Indeed, the classical statistical learning works typically focused on centralized datasets that often had Euclidean geometry. In contrast, many of today’s and tomorrow’s applications of machine learning involve non-Euclidean datasets that are non-centralized, with data often streaming at very high rates, some of which might be compromised due to either gross errors or actions of adversarial entities. Such modern datasets necessitate development of fundamentally new analytical tools and algorithmic techniques for statistical learning-based study of machine learning systems. It is in this regard that this project leverages tools from stochastic approximation, (centralized and distributed) optimization theory, concentration-of-measure literature, information theory, robust statistics, and tensor algebra to derive generalization error bounds, fundamental limits on sample complexity, and near-optimal learning algorithms for machine learning from modern datasets. The outcomes of this project are expected to not only advance the state-of-the-art in statistical learning theory, but they are also expected to lead to computationally efficient algorithms for machine learning that can be deployed in practical settings with the smallest number of training samples.

Congratulations, Waheed!

Dean Thomas N. Farris announced that Prof. Wade Trappe will assume the role of Associate Dean for Academic Programs. Prof. Trappe will fill the position previously held by Henrik Pederson who has become the Interim Dean of the School of Graduate Studies.

In undertaking his role as associate dean for academic programs, Dr. Trappe will partner with Dean Farris and senior leadership to advance overarching strategic direction, especially in graduate and international education and high-level programmatic direction that prioritizes SoE initiatives among academic departments. Dr. Trappe will, additionally, provide leadership, support, and mentoring for the school's assessment and accreditation activities (ABET) and provide oversight of master's student recruitment, education, retention, and outcomes activities.

In working to strengthen organizational effectiveness, Dr. Trappe will forge strong working relationships with programmatic and administrative leaders, including SGS, Rutgers Global, Career Exploration and Success, and the New Brunswick Chancellor-Provost's Office to ensure our academic programs are clearly represented.

Congratulations Wade!

ECE PhD graduate Bo Li (2017) and Distinguished Professor Athina Petropulu have won the 2017 M. Barry Carlton Best Paper Award for their paper “Joint Transmit Designs for Coexistence of MIMO Wireless Communications and Sparse Sensing Radars in Clutter” (Open Access), IEEE Transactions on Aerospace and Electronic Systems, 53(6),7953658, pp. 2846-2864. The M. Barry Carlton Award is an annual award recognizing the best paper published in the IEEE Transactions on Aerospace and Electronic Systems. To help assess impact, nominations are limited to the papers published in the calendar year four years before the award year. The award was presented at the 2021 IEEE Radar Conference Award Ceremony, on May 12, 2021.

In their paper, the authors address the ever-growing need for bandwidth that wireless devices face. By making use of spectrum that was previously reserved for radar, it is possible to share spectrum between radar and communication systems. To reap the advantages of the available spectrum, the interference between the two systems must be managed. While managing interference is a classic problem in the radar and communication community, prior to that work there had been very little work that jointly examined interference between these two different types of technologies. This paper (along with some earlier related papers by the authors) introduces a new line of research for cooperative design of the two systems that aims to control interference between radar and communication systems. The paper proposes a cooperative scheme for the coexistence of a multiple-input-multiple-output (MIMO) communication system and a matrix completion (MC) based, collocated MIMO (MIMOMC) radar. To facilitate the coexistence, and also deal with clutter, both the radar and the communication systems use transmit precoding. It is shown that when a random unitary waveform matrix is used the error performance of MC is guaranteed independent of the precoding matrix. The radar transmit precoder, the radar subsampling scheme, and the communication transmit covariance matrix are jointly designed in order to maximize the radar SINR, while meeting certain communication rate and power constraints. The joint design is implemented at a control center, which is a node with whom both systems share physical layer information, and which also performs data fusion for the radar. The paper provides efficient algorithms for the proposed optimization problem, along with insight on the feasibility and properties of the proposed design. Simulation results show that the proposed scheme significantly improves the spectrum sharing performance in various scenarios.

Congratulations to Bo and Athina!