Yingying (Jennifer) Chen  
 

Associate Professor
Graduate Program Director of Networked Information Systems (NIS)


   

Department of Electrical and Computer Engineering
Stevens Institute of Technology
Castle Point on Hudson
Hoboken, NJ 07030

Office: 210 Burchard Building
Email: yingying.chen at stevens.edu
Phone: 201-216-8066
Fax: 201-216-8246

 


Associate Faculty
Center for the Advancement of Secure Systems and Information Assurance (CASSIA)

   


News |  Teaching  |  Research  |  Publications  |  DAISY Lab  |  Professional Activities  |  Collaborators &Students  |  Genealogy

Introduction

Dr. Yingying Chen is an Associate Professor in the Department of Electrical and Computer Engineering at Stevens Institute of Technology. She received early promotion with tenure at Stevens.She leads the Data Analysis and Information Security (DAISY) Lab, an inter-disciplinary research group working on a diverse set of projects related to cyber security, mobile computing, and mobile healthcare. She is also the Graduate Program Director of Networked Information Systems (NIS). Her research has been reported in numerous media outlets including the Wall Street Journal, MIT Technology Review, Inside Science TV, NPR, Tonight Show with Jay Leno and CNET.

Her research interests include:

Particularly, she is using machine learning techniques and data mining methods to classify and model the security, system, network and healthcare 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 has co-authored the book Securing Emerging Wireless Systems (Springer 2009) and published over 80 journal articles and referred conference papers. Prior to joining Stevens Institute of Technology, she was with Alcatel-Lucent 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.


Honors & Awards

Editorial Boards


Research Grants:


Current Research Projects

Sensing Driver Phone Use to Reduce Driver Distraction. 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, CNet, WCBS, Yahoo News, CSDN, Sohu and Sina.

   

Mobile Healthcare Leveraging Smartphones. Mobile phones 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).

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

Research News: Stevens News, Mobile Healthcare Information and Management Systems Society News, Mobile Health in Stevens, Fierce Mobile Healthcare, Digital Journal.

   

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.

 

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.

   

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.

   

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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