Yingying (Jennifer) Chen  

Department of Electrical and Computer Engineering
Rutgers University
Email: yingche at scarletmail.rutgers.edu

Associate Director
Wireless Information Network Laboratory (WINLAB)


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

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

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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 over 150 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:

Smart Healthcare, Internet of Things (IoT), Mobile Computing and Sensing, Cyber Security and Privacy, and Connected Vehicles.

Particularly, she is using machine learning techniques and data mining methods to classify and model the healthcare, security, system, network related problems. 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 served for many conference organizations. She was the General Co-chair of ACM MobiCom 2016 - a prestigious conference in mobile computing, and the Technical Program Co-chair of IEEE CNS 2016 - a top tier computer and network security conference. She also serves as an Area Chair of IEEE INFOCOM. She has served on the technical program committees of numerous ACM and IEEE conferences and on the editorial boards of the journals IEEE Transactions on Mobile Computing, IEEE Transactions on Wireless Communications, and IEEE Network Magazine.

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

Yingying serves as the TPC Co-chair of ACM WiSec 2019 together with Aurelien Francillon. Please submit your work and join us at WiSec 2019!

Yingying serves as the General Co-chair of IEEE DySPAN 2019 together with Wade Trappe. Please submit your work and join us at DySPAN 2019!

Honors & Awards

Editorial Boards



Research Grants:

Selected Publications:

Current Research Projects

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


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.

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


Mobile Healthcare Leveraging WiFi Network and Mobile Devices. 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


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

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

Exploiting Location as a New Dimension to Assist Wireless Security. As the increasingly pervasive wireless networks make it even easier to conduct attacks for new and rapidly evolving adversaries, the ubiquity of wireless is redefining security challenges. Thus, there is an urgent need to seek security solutions that can defend against attacks across the current heterogeneous mixes of wireless technologies. Location will be the cornerstone of new wireless services as future wireless services will support the access to resources and information from anywhere at anytime, implying that people will request services and information at different locations and at different times. In this project, we exploit location as a powerful information source to assist cryptographic-based methods to solve fundamental security problems such as detecting identity-based attacks and providing location-aware secure access of network resources.

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


Utilizing Physical Layer Properties for Secret Key Extraction in Mobile Environments. Information sharing and various data transactions on wireless devices have become an inseparable part of our daily lives. However, securing wireless communication remains challenging in dynamic mobile environments due to the shared nature of wireless medium and lacking of fixed key management infrastructures. Generating secret keys using physical layer information thus has drawn much attention to complement traditional cryptographic-based methods. This project is designing schemes of secret key generation among wireless devices using physical layer information of radio channel such as the Received Signal Strength (RSS) and the Channel State Information (CSI). We currently are focusing on exploring the fine-grained physical layer information (i.e., CSI) from multiple subcarriers of Orthogonal Frequency-Division Multiplexing (OFDM) to achieve higher secret bit generation rate and make the secret key extraction approaches (based on physical-layer characteristics) more practical.

This project is partially supported Army Research Office, PI: Yingying Chen.


Past Research Projects

Building Self-Healing Smart Grid Under Jamming. A key component of a smart grid is its ability to collect useful information from a power grid for enabling control centers to estimate the current states of the power grid. Such information can be delivered to the control centers via wireless or wired networks. We envision that wireless technology will be widely used for local-area communication subsystems in the smart grid (e.g., in distribution networks). However, various attacks with drastic impacts can be launched in wireless networks such as channel jamming attacks and DoS attacks. In particular, jamming attacks can cause a wide range of damages to power grids, e.g., delayed delivery of time-critical messages can prevent control centers from properly controlling the outputs of generators to match load demands. We design a communication subsystem with enhanced self-healing capability under the presence of jamming through intelligent local controller switching. Our framework allows sufficient readings from smart meters to be continuously collected by various local controllers to estimate the states of a power grid under various attack scenarios. Additionally, we provide guidelines on optimal placement of local controllers to ensure effective switching of smart meters under jamming.



Securing Spectrum Usage in Future Radio Systems. The openness of the lower-layer protocol stacks renders cognitive radios (CR) an appealing solution to dynamic spectrum access (DSA). Its open nature will increase the flexibility of spectrum utilization and promote spectrally-efficient communication. Nevertheless, due to the exposure of the protocol stacks to the public, CR platforms can become a tempting target for adversaries or irresponsible secondary users. A misuse of a CR can significantly compromise the benefits of DSA and threaten the privileges of incumbent users. Therefore, having the ability to enforce spectrum etiquettes is critical in future radio systems. We are designing efficient mechanisms, and developing effective frameworks that can both detect anomalous activities in spectrum usage as well as localize adversaries without requiring overhead on wireless devices.

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


SEMOIS: Secure Mobile Information Sharing System. This project aims to build a secure mobile information sharing system (SEMOIS) that supports secure and privacy-preserving real-time information sharing. SEMOIS will have the ability to store secure data items with flexible access control at insecure storage nodes and enables users to send context-based messages with late-binding features. Specifically, SEMOIS plans to achieve data confidentiality and privacy-preserving through data encryption and encrypted search, and enable intentional name based message dissemination without apriori knowledge of recipients. Additionally, a set of smart learning methods will be developed to extract short-term and long-term geo-social patterns from multimodal sensing data collected by mobile devices for social networking purposes, e.g., geo-social patterns are used to derive hidden social communities.

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


MILAN: Multi-Modal Passive Intrusion Learning in Pervasive Wireless Environments. This project seeks to develop effective and scalable multi-modal passive intrusion learning techniques that have the capability to detect and track device-free moving objects in pervasive wireless environments through adaptive learning. In contrast to traditional techniques, which require pre-deployment of specialized hardware, and thus not easily deployed for unscheduled tasks and may not be scalable, this project leads to new insights into intrusion learning by mining on wireless environmental data, as well as leading to new approaches to device-free wireless localization, which can be used to assist a broad array of applications, e.g., identification of people trapped in a fire building during emergency evacuation.

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


Smartphone Applications. Mobile apps, especially those location based ones, are changing the way people work and live every day, and many such apps have to deal with an indoor environment, e.g., shopping malls and airports. In many such environments, the availability of indoor location information can be used to help individuals (directions, just-in-time coupons/promotions) and organizations (passenger flow distribution in airports, customer shopping/movements' pattern in malls). All these apps would require a practical, robust and efficient smartphone indoor localization solution. We are studying a practical and energy efficient indoor localization solution leveraging multiple sensing modalities enabled by smartphones.


Anti-jamming. The increasing pervasiveness of wireless technologies, combined with the limited number of unlicensed bands, will continue to make the radio environment crowded, leading to unintentional radio interference across devices with different communication technologies that share the same spectrum. Meanwhile, the emerging of software defined radios has enabled adversaries to build intentional jammers to disrupt network communication with little effort. To ensure the successful deployment of pervasive wireless networks, we take the view point that it is crucial to localize jammers, since the locations of jammers allow a better physical arrangement of wireless devices that cause unintentional radio interference, and enable a wide range of defense strategies for combating malicious jamming attackers.



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