I'm a senior research engineer at Snap Research NYC. Before that, I was a research staff member at IBM T.J. Watson Research Center, Yorktown Heights, NY. My research interests are mobile sensing and computing, human computer interaction, visual recognition, augmented reality and location based services. I obtained my PhD from ECE department, Stony Brook University in 2019. I received my Bachelor’s degree in Applied Physics from University of Science and Technology of China (USTC) in 2011 and master degree from Chinese Academy of Sciences in 2014.

Fine-Grained Visual Recognition for AR Self-Assist Technical Support Demo

Fine-grained visual recognition for augmented reality enables dynamic presentation of right set of visual instructions in the rightcontext by analyzing the hardware state as the repair procedure evolves. (This work is published in IEEE ISMAR'20, accepted to IEEE TVCG special issue, 18 out of 302.)

Acoustic Sensing-based Gesture Detection for Wearable Device Interaction

We explore a novel method for interaction by using bone-conducted sound generated by finger movements while performing gestures. This promising technology can be deployed on existing smartwatches as a low power service at no additional cost.

Swift Python TensorFlow iOS

Active Visual Recognition in Augmented Reality

While existing visual recognition approaches, which rely on 2D images to train their underlying models, work well for object classification, recognizing the changing state of a 3D object requires addressing several additional challenges. This paper proposes an active visual recognition approach to this problem, leveraging camera pose data available on mobile devices. With this approach, the state of a 3D object, which captures its appearance changes, can be recognized in real time. Our novel approach selects informative video frames filtered by 6-DOF camera poses to train a deep learning model to recognize object state. We validate our approach through a prototype for Augmented Reality-assisted hardware maintenance.
Acknowledgement: This work was done during my internship at IBM Research.

Swift Python TensorFlow iOS

EchoPrint: Two-factor Authentication using Acoustics and Vision on Smartphones

We propose a novel user authentication system EchoPrint, which leverages acoustics and vision for secure and convenient user authentication, without requiring any special hardware. EchoPrint actively emits almost inaudible acoustic signals from the earpiece speaker to “illuminate” the user's face and authenticates the user by the unique features extracted from the echoes bouncing off the 3D facial contour. Because the echo features depend on 3D facial geometries, EchoPrint is not easily spoofed by images or videos like 2D visual face recognition systems. It needs only commodity hardware, thus avoiding the extra costs of special sensors in solutions like FaceID.

Java Python TensorFlow Android

EasyFind: Smart Device Controlled Laser Pointer for Fast Object Finding

EZ-Find provides a comprehensive solution for fast object finding and indoor navigation. The enabling techniques are computer vision, augmented reality and mobile computing. The fast object finding feature enables instant object identification from clutters (e.g., a book/medicine from shelf). Indoor navigation is the essential for indoor LBS, and will provide great convenience to people, especially in large scale public places such as airports and train stations.

Swift Python Raspberry Pi iOS

BatTracker: High Precision Infrastructure-free Mobile Device Tracking in Indoor Environments

We propose BatTracker, which incorporates inertial and acoustic data for robust, high precision and infrastructure-free tracking in indoor environments. BatTracker leverages echoes from nearby objects and uses distance measurements from them to correct error accumulation in inertial based device position prediction. It incorporates Doppler shifts and echo amplitudes to reliably identify the association between echoes and objects despite noisy signals from multi-path reflection and cluttered environment. A probabilistic algorithm creates, prunes and evolves multiple hypotheses based on measurement evidences to accommodate uncertainty in device position. Experiments in real environments show that BatTracker can track a mobile device's movements in 3D space at sub-cm level accuracy, comparable to the state-of-the-art infrastructure based approaches, while eliminating the needs of any additional hardware.

Java Python Android

BatMapper: Acoustic Sensing Based Indoor Floor Plan Construction Using Smartphones

In this project, we propose BatMapper, which explores a previously untapped sensing modality - acoustics - for fast, fine grained and low cost floor plan construction. We design sound signals suitable for heterogeneous microphones on commodity smartphones, and acoustic signal processing techniques to produce accurate distance measurements to nearby objects. We further develop robust probabilistic echo-object association, recursive outlier removal and probabilistic resampling algorithms to identify the correspondence between distances and objects, thus the geometry of corridors and rooms. We compensate minute hand sway movements to identify small surface recessions, thus detecting doors automatically.

Java Python MATLAB Android

Knitter: Fast, Resilient Single-User Indoor Floor Plan Construction

Lacking of floor plans is a fundamental obstacle to ubiquitous indoor location-based services. Recent work have made significant progress to accuracy, but they largely rely on slow crowdsensing that may take weeks or even months to collect enough data. In this paper, we propose Knitter that can generate accurate floor maps by a single random user’s one hour data collection efforts, and demonstrate how such maps can be used for indoor navigation. Knitter extracts high quality floor layout information from single images, calibrates user trajectories and filters outliers. It uses a multi-hypothesis map fusion framework that updates landmark positions/orientations and accessible areas incrementally according to evidences from each measurement.

Java Swift MATLAB Android iOS

ICHI'21
VitalHub: robust, non-touch multi-user vital signs monitoring using depth camera-aided UWB.
Zongxing Xie, Bing Zhou, Xi Cheng, Elinor Schoenfeld and Fan Ye
2020 IEEE International Conference on Healthcare Informatics (ICHI).

[ Best Paper Award ]

[PDF]
ACM-BCB'21
Signal quality detection towards practical non-touch vital sign monitoring.
Zongxing Xie, Bing Zhou, Fan Ye
Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics.

[ Best Student Paper ]

[PDF]
TMC'21
Robust human face authentication leveraging acoustic sensing on smartphones.
Bing Zhou, Zongxing Xie, Yinuo Zhang, Jay Lohokare, Ruipeng Gao, and Fan Ye
IEEE Transactions on Mobile Computing.
[PDF]
ISMAR'20
Fine-grained visual recognition in mobile augmented reality for technical support.
Bing Zhou, Sinem Guven Kaya
IEEE International Symposium on Mixed and Augmented Reality.

[ Accepted to IEEE TVCG special issue, 18 out of 302, Acceptance rate 6%. ]

[PDF]
ICC'19
Multi-Modal Face Authentication using Deep Visual and Acoustic Features
Bing Zhou, Zongxing Xie, Fan Ye
IEEE International Conference on Communications.
[PDF]
TMC'19
Towards Scalable Indoor Map Construction and Refinement using Acoustics on Smartphones
Bing Zhou, Mohammed Elbadry, Ruipeng Gao, Fan Ye
IEEE Transactions on Mobile Computing.
[PDF]
MobiCom'18
EchoPrint: Two-factor Authentication using Vision and Acoustics on Smartphones
Bing Zhou, Jay Lohokare, Ruipeng Gao, Fan Ye
[PDF]
MobiCom'18 (Poster)
Pose-assisted Active Visual Recognition in Mobile Augmented Reality
Bing Zhou, Sinem Guven, Shu Tao, Fan Ye
[PDF]
MobiCom'18 (Poster)
A Raspberry Pi Based Data-Centric MAC for Robust Multicast in Vehicular Network
Mohammed Elbadry, Bing Zhou, Fan Ye, Peter Milder, YuanYuan Yang
[PDF]
TMC'18
Fast and Resilient Indoor Floor Plan Construction with a Single User
Ruipeng Gao, Bing Zhou, Fan Ye, Yizhou Wang
IEEE Transactions on Mobile Computing.
[PDF]
SenSys'17
BatTracker: High Precision Infrastructure-free Mobile Device Tracking in Indoor Environments
Bing Zhou, Mohammed Salah, Ruipeng Gao, Fan Ye
[PDF]
MobiCom'17 (Demo)
Demo: Acoustic Sensing Based Indoor Floor Plan Construction Using Smartphones
Bing Zhou, Mohammed Salah, Ruipeng Gao, Fan Ye
[PDF]
MobiSys'17
BatMapper: Acoustic Sensing Based Indoor Floor Plan Construction Using Smartphones
Bing Zhou, Mohammed Salah, Ruipeng Gao, Fan Ye
[PDF]
ICC'17
Explore hidden information for indoor floor plan construction
Bing Zhou, Fan Ye
[PDF]
INFOCOM'17
Knitter: Fast, Resilient Single-User Indoor Floor Plan Construction
Ruipeng Gao (co-primary), Bing Zhou (co-primary), Fan Ye, Yizhou Wang
[PDF]
Others
For full publication lists, please refer to my Google Scholar.
Senior Research Engineer Oct 2021 - Present

Research Staff Member May 2019 - Oct 2021

Research Intern Summer 2018

Stony Brook University

Teaching Assistant Fall 2014-Spring 2015