Engineering Meets Medicine Meets Global Health

Engineers are essential to our health. From water purification and flood mitigation to surgical equipment and chemotherapy, they contribute across many specialties.

In countries around the world, severe resource limitations can get in the way of health and health care delivery—and the role engineers play can be even more crucial. A pace-setter in this evolving field is Umer Hassan, a new faculty member in the School of Engineering with a joint appointment at Rutgers Global Health Institute. He is pursuing an interdisciplinary set of research initiatives at Rutgers that share a common goal: saving lives, particularly in vulnerable communities.

Engineering Meets Medicine Meets Global Health

Hassan, an assistant professor, is working with colleagues universitywide to apply his electrical and computer engineering expertise—which incorporates personalized medicine, predictive prognostic systems, and infectious disease—to solve pressing global health challenges. One such challenge: sepsis, a life-threatening condition caused by the body’s response to infection. Any kind of infection can potentially lead to sepsis—urinary tract infection, strep throat, influenza—and even routine surgeries create a risk. The human body’s complex response in sepsis can cause tissue damage, organ failure, and death. Sepsis survivors have described their pain as feeling like they were going to die.

In 2017, the World Health Organization identified as a global health priority the urgent need to improve the prevention, diagnosis, and management of sepsis. Hassan is designing a medical device that would bring to a patient’s bedside the capacity to diagnose sepsis—and do so quickly, accurately, inexpensively, and with minimal training required for health care providers.

Inexpensive Device Detects Sepsis Quickly and Accurately

WHO estimates that sepsis causes 6 million deaths worldwide every year, most of which are preventable. In lower- and middle-income countries, there may not even be a trained clinician available to draw a blood sample to test for sepsis, let alone a physician specialist capable of managing such a dynamic, life-threatening condition.

Intervention, by way of innovation, is desperately needed.

Hassan is actively engaged in the global effort to combat sepsis. “We are building an automated device that would cost less than $10 a test and is simple to operate. Many countries’ health systems cannot support large equipment and expensive technologies that require advanced training and knowledge to use,” he says.

Time and accurate diagnoses are critical factors in managing sepsis. Hassan is working across disciplines at Rutgers and with industry partners to identify new biomarkers and create machine-learning algorithms—essentially, artificial intelligence systems—that will “dramatically improve clinicians’ abilities to diagnose as well as predict sepsis,” he says. “Not only in resource-limited settings, but everywhere.”

Hassan’s findings were recently published in the journal Nature Communications, in a co-authored paper titled “A point-of-care microfluidic biochip for quantification of CD64 expression from whole blood for sepsis stratification.” This spring, he will teach an engineering course in which students will learn to develop applications for global health settings.

Story by Lori Riley for Rutgers Global Health Institute

Photography by Nick Romanenko

Story posted on

Yingying Chen receives NSF Grant

Professor Yingying Chen has won a new National Science Foundation (NSF) award for the project titled " Security Assurance in Short Range Communication with Wireless Channel Obfuscation." This is a one year project of $170,000 in collaboration with Indiana University. The Rutgers share is $85,000.

As the prevalence of mobile computing technologies and applications, short-range communication over emerging aerial acoustic and visible light channel is undergoing a fast rate of expansion with many promising benefits including low power and peer-to-peer communication, without incurring complex network infrastructure. Unlike traditional cryptographic methods that rely on central security management infrastructure to secure wireless links, Yingying and her team propose to obfuscate the transmitting wireless signals by incorporating random channel dynamics to defend against eavesdroppers. Specifically, based on specific wireless channel characteristics, different channel obfuscation schemes are to be developed to achieve information-theoretic secrecy with respect to aerial acoustic and visible light channels. For acoustic channels, this project will develop the channel obfuscation scheme relying on self-jamming signals emitted by the legitimate receiver to secure short-range communication. Moreover, a novel communication protocol based on time-difference-of-arrival modulation is introduced to achieve accurate and robust data transmission. For visible light communication, a channel obfuscation scheme will be developed for screen-to-camera channels to realize a secure secret key distribution leveraging the color shift property of Liquid Crystal Display and Light-Emitting Diode screens.

You can find more details on the project at the NSF page here.

Congratulations Yingying!

Faculty Awards Reception 2018

On September 20, 2018 Dean Farris recognized SOE faculty including ECE professors:

Prof. Anand Sarwate
A. Walter Tyson Assistant Professorship
Prof. Marco Gruteser
Peter D. Cheresia Faculty Scholar
Prof. Peter Meer
Promotion to Distinguished Professor
Prof. Hana Godrich
Promotion to Associate Teaching Professor
Prof. Laleh Najafizadeh
Promotion to Associate Professor with tenure

Kristin Dana Publishes New Book

Professor Kristin Dana has just published a short book as part of Morgan Claypool's Synthesis Lectures on Computer Vision edited by Gerard Medioni and Sven Dickinson.


Dana, Kristin J. "Computational Texture and Patterns: From Textons to Deep Learning." Synthesis Lectures on Computer Vision 8.3 (2018): 1-113.


Abstract: Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance - to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adapting to new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.

Research Team led by Ivan Marsic receives NSF Grant

Professor Ivan Marsic has won a new NSF award for the project titled "Activity Recognition for Reducing Delays in Fast-Response Teamwork.” This is a four-year $1.2M collaborative effort led by Rutgers University (Ivan Marsic, PI) with Drexel University and Children's National Medical Center in Washington, DC. Rutgers' share of this award is $700,000.

Human performance in time-critical teamwork settings relies on appropriate and timely task completion. Time perception, a critical cognitive function that influences team performance, is often skewed in these settings and is impacted by cognitive workload. This project will address timeliness errors by automatically and unobtrusively modeling and tracking cross-disciplinary task performance through analysis of verbal communication and the use of information artifacts. The resulting model will be used to display alerts about timeliness of critical tasks in a way that supports team members' information needs without increasing their cognitive workload. Novel techniques for activity recognition in fast-paced and crowded collaborative settings will be based on passive RFID, speech recognition, and computer vision, supplemented by other sensors and digital devices. The proposed research will develop (1) temporal models of verbal communication and digital document interaction during complex activities in fast-paced teamwork for the purpose of automatic activity recognition; (2) approaches for real-time recognition of over a hundred different activities in the presence of up to a thousand RFID tags; and (3) approaches for displaying delay information for rapid assimilation under high task load that learn from workers' responses to improve their usefulness to the team. Together, the work will provide building blocks for computerized support of teams not just in the trauma domain, but in other domains with complex, interleaved tasks such as surgery, traffic control, and disaster management. 

You can find more details on the project at the NSF page here:

Congratulations Ivan on this exciting collaborative effort!

Mehdi Javanmard on Rutgers Team that receives NSF MRI Award for X-ray Computed Tomography

Assistant Professor Mehdi Javanmard is a co-PI on a Rutgers team from the Molecular Imaging Center that won a NSF MRI grant for the project "Acquisition of a High Resolution X-ray Computed Tomography Instrument for a Multi-User Imaging Facility." This $399,969 award will enable acquisition of a high-resolution scanner that can obtain detailed 3D images at the micron or sub-micron level of a broad range of samples (ranging from biological specimens to biomaterials to 3D printed objects to soil samples) without destroying or altering the sample. This investment supports some research communities in natural and biological structures, and advanced multi-user research in several departments. Projects are being carried out with the instrument in the areas of biology, chemistry, geology and cybersecurity for 3D manufacturing. The resources will be shared by researchers from the university and local small biotech and pharmaceutical industries.

More details can be found at the NSF awards page  here.

Shantenu Jha wins 2018 IEEE SCALE Award

Professor Shantenu Jha and his collaborators have won the 2018 IEEE International Scalable Computing Challenge (SCALE) award. Each year, the IEEE recognizes a project that advances application development and supporting infrastructure to enable the large-scale, high-performance computing needed to solve such problems. This year’s winner, “Enabling Trade-off Between Accuracy and Computational Cost: Adaptive Algorithms to Reduce Time to Clinical Insight,” is the result of a collaboration between Professor Jha and his collaborators at Rutgers University, U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, and University College London. The team members were honored at the 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing held in Washington DC.

Please see a nice article on Shantenu's accomplishment in HPCwire  here.


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