Yuqian Zhang has joined the ECE Department as a new Assistant Professor. Yuqian was a postdoctoral scholar at the Department of Computer Science and the TRIPODS data science center at Cornell University.
Yuqian received her PhD in Electrical Engineering from Columbia University in 2018, and her BS in Electrical Engineering from Xi’an Jiaotong University in 2011. Her research leverages physical models in data driven computations, convex and nonconvex optimization, solving problems in computer vision, machine learning, signal processing. She has been invited to participate in the EECS Rising Stars Workshop at Stanford University, and the Young Investigator Lecture Series at California Institute of Technology.
A team of ECE faculty members led by Assistant Professor Umer Hassan (PI) have received a new NSF award for the project titled " Multi-Modal Data-Driven Platform for Multiplexed Cellular Antigen Classification using Nano-electronic Barcoded Particles for Whole Blood Applications."
This project includes Associate Professor Mehdi Javanmard as a co-PI. The total award amount for this three-year project is $500,000. The project seeks to develop the next generation biosensing platform equipped with multi-modal sensing and novel nano-barcoded particles to perform reconfigurable biomarker selection in whole blood samples. Human blood cells play a critical role in immune system activation in response to infections. Concentration of these immune cells in whole blood and their membrane receptor densities may change in different diseases and their pathogenesis. The heterogeneity of the cellular classification needs to be quantified to provide a personalized diagnostics and patient monitoring system in hospital settings. The biosensing platform will be integrated with multi-modal sensing including electrical and optical which will allow to correct for inherent device-device variation to improve the sensor performance. Immune cells conjugated with functionalized nano-barcoded particles will be quantified in sync by impedance detector and smartphone attachment. Further, the proposed biosensor will be equipped with real-time data analysis using machine learning to enable a reconfigurable system for resource optimization and biomarker selection. The proposed sensor will enable multiplexed cellular antigen classification from a drop of whole blood with time to result (TOR) of less than 30 minutes. Sensors will be benchmarked with patient samples collected from the Robert Wood Johnson Medical Hospital. This cross disciplinary project will train undergraduate and graduate students in areas of sensors, systems and bionanotechnology.
More details on the project can be found at the NSF page here
Congratulations to Umer and Mehdi!
A team of ECE faculty members led by Assistant Professor Umer Hassan (PI) have received a new NSF award for the project titled " Multi-Modal Data-Driven Platform for Multiplexed Cellular Antigen Classification using Nano-electronic Barcoded Particles for Whole Blood Applications." This project includes Associate Professor Mehdi Javanmard as a co-PI. The total award amount for this three-year project is $500,000.
The project seeks to develop the next generation biosensing platform equipped with multi-modal sensing and novel nano-barcoded particles to perform reconfigurable biomarker selection in whole blood samples. Human blood cells play a critical role in immune system activation in response to infections. Concentration of these immune cells in whole blood and their membrane receptor densities may change in different diseases and their pathogenesis. The heterogeneity of the cellular classification needs to be quantified to provide a personalized diagnostics and patient monitoring system in hospital settings. The biosensing platform will be integrated with multi-modal sensing including electrical and optical which will allow to correct for inherent device-device variation to improve the sensor performance. Immune cells conjugated with functionalized nano-barcoded particles will be quantified in sync by impedance detector and smartphone attachment. Further, the proposed biosensor will be equipped with real-time data analysis using machine learning to enable a reconfigurable system for resource optimization and biomarker selection. The proposed sensor will enable multiplexed cellular antigen classification from a drop of whole blood with time to result (TOR) of less than 30 minutes. Sensors will be benchmarked with patient samples collected from the Robert Wood Johnson Medical Hospital. This cross disciplinary project will train undergraduate and graduate students in areas of sensors, systems and bionanotechnology.
More details on the project can be found at the NSF page here
Congratulations to Umer and Mehdi!
Batoul Ahmad Taki has been selected to attend the Computing Research Association Committee on Widening Participation workshop to be held on April 16-18 in New Orleans. Batoul will participate in the Grad Cohort for Women workshop. CRA will fully support travel and related expenses.
At the Grad Cohort for Women, attendees will spend two days interacting with about 20 senior female computing-related researchers and professionals, who will share pertinent information on graduate school survival skills, as well as more personal information and insights about their experiences. The workshop will include a mix of formal presentations and informal discussions and social events. By attending Grad Cohort attendees will be able to build mentoring relationships and develop peer networks that will form the basis for ongoing activities during their graduate career and beyond.
Batoul Taki received a B.S. (with High Distinction), in Computer and Communications Engineering, from the American University of Science and Technology, Beirut, Lebanon. She is currently a graduate student with the Department of Electrical and Computer Engineering, Rutgers University. Her research interests include statistical tensor-based signal processing, machine learning, and data recovery. She was awarded ECE's Best TA Award for Fall, 2019
Assistant Professor Umer Hassan has received a Defense University Research Instrumentation Program (DURIP) Award for his project titled "MEDIUM: Monitoring and Engineering Decompression Illness during Undersea Missions." The DURIP instrumentation awards provide the unique means through which the Depart of Defense supports universities in the acquisition of essential laboratory equipment, usually out of reach for most research grants. As part of this project, Dr. Hassan has received a $84625 award from the Office of Naval Research (ONR) to build the MEDIUM platform for personalized testing, monitoring and engineering divers’ innate ability to prevent decompression illness, one of the most prevalent medical conditions experienced by the divers during underwater diving missions. Current diagnosis is highly non-specific and relies on symptoms such as joint pains, paralysis, fatigue, memory loss, and shortness of breath. Decompression usually results in a blocked blood vessel caused by air or gas bubbles and subsequently triggers the divers’ immunological response. It’s a highly individualized condition with patients experiencing different levels of severity and can potentially lead to shock and patients’ mortality. The challenges include stratifying a highly individualized condition with heterogeneity in divers’ immune response to the presence of bubbles and strategizing the personalized therapeutic drugs. The MEDIUM platform will be instrumental in performing experiments to address the above challenges.
Congratulations Umer!
Dr. Chris Marty (ECE, Class of 1993) was recognized with the 2019 ECE Distinguished Alumnus of the Year Award on December 6 at an event held in the Richard Weeks Hall of Engineering. He is a Portfolio Manager and Managing Director at Two Sigma Investments LLC, a quantitative hedge fund located in Manhattan. In this role, Chris combines financial modeling with large scale computing to deliver superior investment results.
Prior to joining Two Sigma, Marty was head of Portfolio Analytics at Bloomberg LP. He was a founding member of the Bloomberg Portfolio Analytics system ALPHA and ran both software development and financial modeling. The system is used by thousands of Bloomberg customers to perform complex portfolio risk and performance analysis. Before joining Bloomberg, Chris was a senior technical staff member with AT&T Labs Research where he focused on network operating systems and digital rights management. Chris obtained his PhD in Computer Engineering from the City University of New York.
Chris is an active champion of the ECE Department — serves on its Advisory Board and mentors and engages students conducting research in information sciences. He is married to fellow ECE Alumna Rita Marty who is a Vice President at AT&T.
The IEEE Board of Directors, at its November 2019 meeting, has elevated Professor Yingying Chen to IEEE Fellow, effective 1 January 2020, with the following citation: for contributions to mobile computing and mobile security.
Each year, following a rigorous evaluation procedure, the IEEE Fellow Committee recommends a select group of recipients for elevation to IEEE Fellow. Less than 0.1% of voting members are selected annually for this member grade elevation.
Congratulations on this outstanding recognition of professional achievement, Yingying!
Professor Yingying Chen and her student Zhenzhe Lin have won the Best Paper Award at the 6th EAI International Conference on IoT Technologies for HealthCare (HealthyIoT 2019) for their paper titled "WiFi-enabled Automatic Eating Moment Monitoring Using Smartphones." HealthyIoT is an international scientific event series dedicated to the Internet of Things and Healthcare. HealthyIoT aims to bring together technology experts, researchers, industry and international authorities contributing towards the design, development and deployment of healthcare solutions based on IoT technologies, standards and procedures. The paper is the outcome of a collaborative effort with researchers from Indiana University & Purdue University at Indianapolis (IUPUI) and Louisiana State University (LSU).
The abstract of the award winning paper is below:
Dietary habits are closely correlated with people's health. Studies reveals that unhealthy eating habits may cause various diseases such as obesity, diabetes and anemia. To help users create good eating habits, eating moment monitoring plays a significant role. However, traditional methods mainly rely on manual self-report or wearable devices, which either require much user effort or intrusive dedicated hardware. In this work, we propose a user effort-free eating moment monitoring system by leveraging the WiFi signals extracted from the commercial off-the-shelf (COTS) smartphones. In particular, the proposed system captures the eating activities of users to determine the eating moments. It can further identify the fine-grained food intake gestures (e.g., eating with fork, knife, spoon, chopsticks and barehand) to estimate the detailed eating episode for each food intake gesture. Utilizing the dietary information, the system shows the potential to infer the food category and food amount.
Congratulations to Yingying and her team on this recognition!
Since we are not able to gather on campus on April 25, we are bringing some popular Rutgers Day programs to you! Join us on Facebook for "Virtual Rutgers Day" on April 25 from 10 a.m. - 4 p.m. Tune in for program posts throughout the day, including performances, demonstrations, activities, and photos of favorite programs. A detailed schedule and program specifics will be posted in the coming days.
