Yingying (Jennifer) Chen  
IEEE Fellow
Peter D. Cherasia Faculty Scholar
 

Professor, Electrical and Computer Engineering
Graduate Director, Electrical and Computer Engineering
Associate Director, Wireless Information Network Laboratory (WINLAB)
Director, Data Analysis and Information Security (DAISY) Lab

Rutgers University - New Brunswick
Email: yingche at scarletmail.rutgers.edu

 

ECE Department
Office: Core 506
Phone: 848-445-9151
Address:
96 Frelinghuysen Rd
Piscataway, NJ 08854

WINLAB
Office: C-109
Phone: 848-932-0948
Address:
671 Route 1
North Brunswick, NJ 08902


News| Media Coverage| Teaching| Research| Publications | DAISY Lab| Professional Activities| Collaborators & Students| Youtube Channel| Genealogy

Introduction

Yingying (Jennifer) Chen is a tenured Professor of Electrical and Computer Engineering at Rutgers University and the Associate Director of the Wireless Information Network Laboratory (WINLAB). She also leads the Data Analysis and Information Security Laboratory (DAISY). Her background is a combination of Computer Science, Computer Engineering and Physics. She has co-authored three books Securing Emerging Wireless Systems (Springer 2009) and Pervasive Wireless Environments: Detecting and Localizing User Spoofing (Springer 2014) and Sensing Vehicle Conditions for Detecting Driving Behaviors (Springer 2018), published 200+ journal articles and referred conference papers and obtained 8 patents. Her research has been licensed by multiple companies and reported in numerous media outlets including the Wall Street Journal, MIT Technology Review, CNN, Fox News Channel, IEEE Spectrum, Fortune, Inside Science, NPR, Tonight Show with Jay Leno and Voice of America TV.

Her research interests include: Mobile Computing and Sensing Systems, Internet of Things (IoT), Mobile Security, Smart Healthcare, Connected Vehicles, and Security in ML/AI Systems.

She is one of the pioneers to use machine learning techniques and data mining methods to classify and model the healthcare, security, system, network related problems since its infancy. Besides the algorithm development, her work has a strong emphasis on system implementation and validation in real-world scenarios. Her interdisciplinary research and education have been sponsored by multiple grants from various funding agencies:

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She is serving and served on prestigious journal editorial boards including IEEE/ACM Transactions on Networking (IEEE/ACM ToN), IEEE Transactions on Mobile Computing (IEEE TMC), IEEE Transactions on Wireless Communications (IEEE TWireless), ACM Transactions on Privacy and Security (ACM TOPS), IEEE Network Magazine, EURASIP Journal on Information Security, and International Journal of Parallel, Emergent and Distributes Systems (IJPEDS).

She is actively involving in community services. She is the Technical Program Co-chair of IEEE INFOCOM 2022, and was the Technical Program Co-chair and General Co-chair of ACM MobiCom 2018 and 2016, respectively - these are top-tier conferences in mobile computing. She also served on many other conference organizations including the Technical Program Co-chair ACM WiSec 2019, IEEE CNS 2016, and IEEE MASS 2013, and the General Co-chair of IEEE DySPAN 2019. She regularly serves on the technical program committees of ACM and IEEE conferences including ACM MobiCom, ACM MobiSys, ACM MobiHoc, ACM SenSys, ACM CCS, ACM ACSAC, ACM AsiaCCS, IEEE INFOCOM, IEEE ICDCS, IEEE CNS, IEEE MASS, IEEE SECON, IEEE ICC, IEEE Globecom.

Previously, she was a tenured professor in the Department of Electrical and Computer Engineering (ECE) at Stevens Institute of Technology. She received early promotion twice at Stevens: from Assistant to Associate Professor, and from Associate to Full Professor. She was also the Graduate Program Directors of Information and Data Engineering (IDE) and Networked Information Systems (NIS) in ECE Department at Stevens. She was a visiting professor at Princeton University. Prior to joining Stevens, she was with Alcatel - Lucent (now Nokia) at Holmdel & Murray Hill, New Jersey. Her work has involved a combination of research and development of new technologies and real systems, ranging from Network Management Systems for Lucent flagship optical and data products to voice/data integrated services.


Latest Update

Dr. Chen serves as the TPC Co-chair of IEEE INFOCOM 2022. Please submit your work and join us at INFOCOM 2022!


Dr. Chen is promoting the new column, "Women in Networks", in IEEE Network Magazine. Please contact her if you would like to be featured in this column.


Looking for self-motivated Ph.D. students conducting Computer Systems Research, preferring areas of interest and expertise include Mobile Sensing and Healthcare, Internet of Things, Cyber Security and Privacy, Deep Learning, and Software and Hardware Co-Design

Honors & Awards

Editorial Boards

Current:

Past:

Guest Editor:


Research Grants:


Selected Publications:

Books:

Yingying Chen, Wenyuan Xu, Wade Trappe, Yanyong Zhang, Securing Emerging Wireless Systems,
ISBN:978-0-387-88490-5, Springer, 2009.
   
Jie Yang, Yingying Chen, Wade Trappe, and Jerry Cheng, Pervasive Wireless Environments: Detecting and Localizing User Spoofing,
ISBN: 978-3-319-07355-2, Springer, 2014.
   
Jiadi Yu, Yingying Chen, and Xiangyu Xu, Sensing Vehicle Conditions for Detecting Driving Behaviors,
ISBN: 978-3-319-89769-1, Springer, 2018.

Journal Papers:


Conference Papers:


Current Research Projects

Adversarial Machine Learning. Driven by the advanced speech recognition technologies, voice-controllable systems have been widely integrated into smart and IoT devices (e.g., Google Home, Amazon Echo). Although these systems provide great convenience, they also bring growing concerns about their security and robustness, especially under adversarial attacks. The underlying machine learning models adopted by the state-of-the-art voice-controllable systems, are inherently vulnerable to well-crafted audio perturbations, which causes misclassification while being imperceptible to human listeners. In this project, we aim to develop audio adversary attacks that are: 1) applicable to streaming audio input (e.g., such as live human speech); 2) ignoring physical effect during over-the-air propagation in the space; 3) timing-aware attacks can be launched in a timely fashion in practice.

This project has multiple papers published on top-tier conferences: AAAI 2021, ACM CCS 2020, ACM HotMobile 2020, ICASSP 2020.

The project website is here, and the project demonstration video is below:

Injecting Adversarial Perturbations to Live Human Speech to Attack Speech Recognition System

Mobile Healthcare and Wellbeing Monitoring. Mobile devices and WiFi have become increasingly popular and gradually woven into our social lives. Smartphones equipped with powerful embedded sensors (e.g., accelerometers, GPS, microphones, and etc.) can be used to monitor multiple dimensions of human behaviors including physical, mental and social behaviors of wellbeing. The collected sensing data can thus be comprehensive enough to be mined not only for the understanding of human behaviors or daily life activities but also for supporting a broad range of mobile healthcare applications. We are designing a smartphone based secure healthcare monitoring system which allows users to be monitored for their mental, cognitive, and physical well-being and hence facilitate early diagnosis of potential illnesses and taking possible preventive measures. The communities extracted from a mobile phone enabled social network in our system can also be exploited for securing certain components of the system (e.g., coping with clone attacks). In addition, our low-cost system leverages the Channel State Information (CSI) extracted from WiFi signals on mobile devices to monitor vital signs and perform fine-grained sleep monitoring in home environments.

This project is partially funded by the National Science Foundation, PI: Yingying Chen.

The NSF project web site is here.

Research News: Stevens News, Mobile Healthcare Information and Management Systems Society News, Mobile Health in Stevens, Fierce Mobile Healthcare, Digital Journal, MIT Technology Review, Yahoo News, Zeenews, MIT 科技评论, 网易科技, and 电子工程世界.

Project Demonstration Video:

Fine-grained Sleep Monitoring Leveraging Home Wi-Fi Networks

VibSense: Sensing Touches on Ubiquitous Surfaces through Vibration. As the form factor of our mobile and wearable devices shrinks, there exists an increasing need to support interaction beyond the confines of the device itself. Particularly on wearable devices, small touchscreens and interfaces can render complex input cumbersome. VibSense pushes the limits of vibration-based sensing to determine the location of a touch on extended surface areas as well as identify the object touching the surface leveraging a single sensor. Unlike capacitive sensing, it does not require conductive materials and compared to audio sensing it is more robust to acoustic noise. It supports a broad array of applications through either passive or active sensing using only a single sensor. In VibSense's passive sensing, the received vibration signals are determined by the location of the touch impact. This allows location discrimination of touches precise enough to enable emerging applications such as virtual keyboards on ubiquitous surfaces for mobile devices. Moreover, in the active mode, the received vibration signals carry richer information of the touching object's characteristics (e.g., weight, size, location and material). This further enables VibSense to match the signals to the trained profiles and allows it to differentiate personal objects in contact with any surface.

This project has received the Best Paper Award at the IEEE International Conference on Sensing, Communication and Networking (SECON) 2017.

Research News: Rutgers News, IEEE Spectrum, Yahoo Finance News, NSF Science 360 News, and Futurity.

Project Demonstration Video:

Finger-input Authentication via Physical Vibration

Smartphone Privacy

Towards Understanding Privacy When Using Smartphone and Wearable Devices. This project focuses on addressing privacy concerns of smartphone users. In particular, it investigates how the usages of the smartphone applications (apps) may reshape users' privacy perceptions and what is the implication of such reshaping. There is only limited understanding on the consequences of user privacy losses, especially when large amount of privacy information leaked from smartphone users across many apps. We investigate how the mobile technology (i.e., smartphone and smartphone apps) can reveal users' personal information and identify the consequences of privacy violations, by taking users' social relationships into consideration. The project facilitates a deep understanding of user privacy in the age of mobile devices and further develops appropriate protective mechanisms. Smartphone user privacy across different levels are analyzed including individual, social and community relationships based on different levels of information leakage. Statistical models, such as Bayesian networks and hidden Markov models, are developed to understand users' temporal privacy leakage patterns based on experimental study.

This project is funded by the National Science Foundation, PI: Yingying Chen.

Project Demonstration Video:

Cracking the PIN Number Using Wearables

   

Guardian Angel: Sensing Driver Phone Use to Reduce Driver Distraction & Enabling Pedestrian Safety Services. Cell phone distractions have been a factor in high-profile accidents and are associated with a large number of automobile accidents. This project addresses the fundamental problem of distinguishing between a driver and passenger using a mobile phone, which is the critical input to enable numerous safety and interface enhancements for the driver distraction problem. We are building a detection system that leverages the existing car stereo infrastructure, e.g., the speakers and Bluetooth network. Our solution seeks to address major challenges including the complex multipath environment presented in the small confided space inside a car, minimizing interference between the speakers, and any sounds emitted should be unobtrusive to minimize distraction.

This project has received the Best Paper Award at the ACM International Conference on Mobile Computing and Networking (MobiCom) 2011.

This project is partially funded by the National Science Foundation, PI: Yingying Chen.

The NSF project web site is here.

Research News: The Wall Street Journal, MIT Technology Review, VOA-TV, CNet, WCBS, Yahoo News, CSDN, Sohu, Sina, Fox News Channel and New Jersey My9.

Project Demonstration Videos:

Sensing Driver Phone Use to Reduce Driver Distraction Caused by Cell Phone Usage

Look Up: Enabling Pedestrian Safety Services via Shoe Sensing

   

BigRoad: Scaling Road Data Acquisition for Dependable Self-Driving. Advanced driver assistance systems and, in particular automated driving offers an unprecedented opportunity to transform the safety, efficiency, and comfort of road travel. Developing such safety technologies requires an understanding of not just common highway and city traffic situations but also a plethora of widely different unusual events (e.g., object on the road way and pedestrian crossing highway, etc.). While each such event may be rare, in aggregate they represent a significant risk that technology must address to develop truly dependable automated driving and traffic safety technologies. By developing technology to scale road data acquisition to a large number of vehicles, this project introduces a low-cost yet reliable solution, BigRoad, that can derive internal driver inputs (i.e., steering wheel angles, driving speed and acceleration) and external perceptions of road environments (i.e., road conditions and front-view video) using a smartphone and an IMU mounted in a vehicle.

Project Demonstration Video:

Scaling Road Data Acquisition for Dependable Self-Driving

   

User Authentiaction Using Low-cost Sensors. With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification.

Project Demonstration Video:

Heart-based User Authentication Leveraging Camera on Mobile Devices

Previous Research Projects

The list of previous research projects could be found in a second page.



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