Ke Sun

Ph.D. candidate, Department of Computer Science and Engineering, University of California, San Diego

Incoming Tenure-track Assistant Professor, Computer Science and Engineering, EECS Department, University of Michigan, Ann Arbor

kesun@ucsd.edu kesuniot@umich.edu

samsonsunke@gmail.com

Samsonsjarkal Google Scholar Resume

Personal photo

Biography

I will be joining the Computer Science and Engineering (CSE) in the Electrical Engineering and Computer Science (EECS) department at University of Michigan, Ann Arbor as an Assistant Professor in Jan 2025! I am actively recruiting undergraduate and graduate researchers. If you're interested, please check Openings in AmI Lab @ UMich CSE.

I am a final-year Ph.D. candidate in Computer Science and Engineering at the University of California, San Diego, advised by Prof. Xinyu Zhang.

Committed to the vision of "ambient intelligence", my research delves into the creation of end-to-end intelligent, cost-effective, deployable, and human-centric sensing systems for Mobile, Wearable and IoT ecosystems. Specifically, we will i). Enable novel applications; ii). Advance computational sensing techniques; iii). Address system bottlenecks; and iv). Harness innovative sensing modalities for MWIoT ecosystems.

My work has won the ACM IMWUT (UbiComp) Distinguished Paper Award and ACM SenSys Best Poster Runner-up. The tangible impact of my research is also manifested in real-world applications, with successful deployments on widely-recognized IoT devices such as Amazon Echo and Google Home. Additionaly, I'm privileged to have been chosen as the Google Ph.D. Fellowship (the sole recipient in Mobile Computing area among candidates in North America and Europe).

In the mobile and IoT realm, my work intersects various disciplines, driving innovation across different applications

News

Selected Honor and Awards

  • 2023 Google Ph.D. Fellowship in Mobile Computing Sole recipient in Mobile Computing area among candidates in North America and Europe
  • 2023 UbiComp Distinguished Paper Award 8 out of 210 accepted papers.
  • 2021 ACM MobiCom Student Travel Grants
  • 2020 ACM SenSys Best Poster Award Runner-ups 3 out of 84 accepted posters.
  • 2020 Jiangsu Province Computer Society Outstanding Dissertation Award Sole recipient.
  • 2019 Nanjing University CS Distinguished Master Thesis Award
  • 2019 Nanjing University Distinguished Graduate Student
  • 2018 ACM MobiSys Student Travel Grants
  • 2017 China National College Competition on Internet of Things, Finals First Prize

    • Project: Ultrasound based sensing applications for mobile devices

  • 2015 ACM-ICPC Asia Regional Contest(Chang Chun Site), Gold Medal

    • School Rank 9 and Team Rank 13 from 217 teams

  • 2015 CCF(China Computer Federation) Outstanding Undergraduate Award

    Top 100 undergraduates in China annually

    • Selected Publication (Full List)

    Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
    Jung-Woo Chang, Ke Sun, Nasimeh Heydaribeni, Seira Hidano, Xinyu Zhang, Farinaz Koushanfa
    NDSS 2025, Feb 2025.

    We propose Magmaw, a novel attack methodology targeting deep neural network (DNN)-based joint source-channel coding (JSCC) wireless communication systems. Magmaw is capable of generating universal adversarial perturbations for any multimodal signal transmitted over a wireless channel.
    magmaw


    RFCanvas: Modeling RF Channel by Fusing Visual Priors and Few-shot RF Measurements
    Xingyu Chen, Zihao Feng, Ke Sun, Kun Qian, Xinyu Zhang
    SenSys 2024, Nov 2024.

    We propose RFCanvas, which fuses visual priors and RF measurements to achieve high accuracy for realistic scenes and be responsive to environmental changes.
    rfcanvas


    Multimodal Daily-life Logging in Free-living Environments Using Non-Visual Egocentric Sensors on a Smartphone
    Ke Sun, Chunyu Xia, Xinyu Zhang, Hao Chen, Charlie Zhang
    ACM IMWUT (UbiComp) 2024, March 2024.

    We propose EgoADL, the first egocentric ADL sensing system that uses an in-pocket smartphone as a multi-modal sensor hub to capture body motion, interaction with the physical environment and daily objects using non-visual sensors (audio, wireless sensing, and motion sensors).
    egoadl


    StealthyIMU: Extracting Permission-protected Private Information from Smartphone Voice Assistant using Zero-Permission Sensors
    Ke Sun, Chunyu Xia, Songlin Xu, Xinyu Zhang
    NDSS 2023, Feb 2023. (Acceptance ratio: 58/398=14.6%)

    We propose StealthyIMU, a new threat that uses side channel to steal permission-protected private information from the voice-user interface.
    stealthyimu


    SCALAR: Self-Calibrated Acoustic Ranging for Distributed Mobile Devices
    Lei Wang, Haoran Wan, Ting Zhao, Ke Sun, Shuyu Shi, Haipeng Dai, Guihai Chen, Haodong Liu, Wei Wang
    IEEE TMC 2023

    We propose SCALAR, a self-calibrated acoustic ranging system that achieves sub-millimeter ranging accuracy on distributed commercial devices.
    scalar


    VECTOR: Velocity Based Temperature-field Monitoring with Distributed Acoustic Devices
    Haoran Wan, Lei Wang, Ting Zhao, Ke Sun, Shuyu Shi, Haipeng Dai, Guihai Chen, Haodong Liu, Wei Wang
    ACM IMWUT (UbiComp) 2022, Sep 2022.

    We introduce VECTOR, a temperature-field monitoring system that achieves high temperature sensing accuracy and fast response time using commercial sound playing/recording devices.
    vector


    LoEar: Push the Range Limit of Acoustic Sensing for Vital Sign Monitoring
    Lei Wang, Wei Li, Ke Sun, Fusang Zhang, Tao Gu, Chenren Xu, Daqing Zhang
    ACM IMWUT (UbiComp) 2022, Sep 2022.

    We propose LoEar, which is a novel acoustic sensing system using only a single microphone and speaker to detect vital signs (respiration and heartbeat) with a significantly increased sensing range (> 6 meter).
    loear


    UltraSE: Single-Channel Speech Enhancement Using Ultrasound
    Ke Sun, Xinyu Zhang
    ACM MobiCom 2021, Oct 2021. (Acceptance ratio: 60/299=20.1%)

    We propose UltraSE, which uses ultrasound sensing as a complementary modality to separate the desired speaker’s voice from interferences and noise.
    ultrase


    ExGSense: Toward Facial Gesture Sensing and Reconstruction with a Sparse Near-Eye Sensor Array
    Chen Chen, Ke Sun, Xinyu Zhang
    ACM/IEEE IPSN 2021, May 2021. (Acceptance ratio: 26/105=24.8%)

    ExGSense propose to sense whole facial gestures by using a sparse set of biopotential transducers. We propose a dual-branch multiview representation learning model, which can explicitly exploit the sensor diversities across time-frequency-spatial domains.
    exgsense


    "Alexa, Stop Spying on Me": Speech Privacy Protection Against Voice Assistants
    Ke Sun, Chen Chen, Xinyu Zhang
    ACM SenSys 2020, Nov 2020. (Acceptance ratio: 43/213=20.2%)

    MicShield is a companion device with voice assistant to enable the selective jamming mechanism which, for the first time, prevents VAs from recording private speech without affecting the voice assistants’ normal functionalities.
    micshield


    milliEgo: Single-chip mmWave Radar Aided Egomotion Estimation via Deep Sensor Fusion
    Chris Xiaoxuan Lu, Muhamad Risqi U. Saputra, Peijun Zhao, Yasin Almalioglu, Pedro P. B. de Gusmao, Changhao Chen, Ke Sun, Niki Trigoni, Andrew Markham
    ACM SenSys 2020, Nov 2020. (Acceptance ratio: 43/213=20.2%)

    milliEgo is a novel deep-learning approach that demonstrates the feasibility of mmWave odometry and provides a fusion framework to develop a robust mmWaveInertial Odometry.
    milliego


    Dynamic Speed Warping: Similarity-Based One-shot Learning for Device-free Gesture Signals
    Xun Wang, Ke Sun, Ting Zhao, Wei Wang, Qing Gu
    IEEE INFOCOM 2020, April 2020. (Acceptance ratio: 267/1397=19.1%)

    We propose a Dynamic Speed Warping (DSW) algorithm to enable one-shot learning for device-free gesture signals performed by different users. DSW provides a robust similarity measure between gesture samples and can work for both one-shot learning in supervised gesture recognition and unsupervised learning tasks.
    dsw


    SpiderMon: Towards Using Cell Towers as Illuminating Sources for Keystroke Monitoring
    Kang Ling, Yuntang Liu, Ke Sun, Wei Wang, Lei Xie, Qing Gu
    IEEE INFOCOM 2020, April 2020. (Acceptance ratio: 267/1397=19.1%)

    SpiderMon is a system that performs longrange keystroke monitoring using the signal transmitted by commercial 4G/5G cellular base stations. We show that SpiderMon can detect keystroke movements at a distance of 15 meters or even behind the wall.
    SpiderMon


    VSkin: Sensing Touch Gestures on Surfaces of Mobile Devices Using Acoustic Signals
    Ke Sun, Ting Zhao, Wei Wang, Lei Xie
    ACM MobiCom 2018, October 2018 (Acceptance ratio: 42/187=22.5%).

    VSkin is a system that supports fine-grained touch gesture sensing on the back of mobile devices based on acoustic signals. VSkin utilizes both the structure-borne sounds, i.e., sounds propagating through the structure of the device, and the air-borne sounds, i.e., sounds propagating through the air, to sense touch gestures.
    vskin


    Unlock With Your Heart: Heartbeat-based Authentication on Commercial Mobile Phones
    Lei Wang, Kang Huang, Ke Sun, Wei Wang, Chen Tian, Lei Xie, Qing Gu
    ACM IMWUT (UbiComp) 2018, October 2018.

    ''Unlock with your heart'' uses the built-in accelerometer to capture the heartbeat signals on commercial mobile phones. The user only needs to press the phone on his/her chest, and the system can identify the user within a few heartbeats.
    heartbeat


    Depth Aware Finger Tapping on Virtual Displays
    Ke Sun, Wei Wang, Alex X. Liu, Haipeng Dai
    ACM MobiSys 2018, June 2018. (Acceptance ratio: 37/138=26.8%)

    Dolphin is a fine-grained depthaware tapping scheme that can provide high accuracy tapping detection. It uses light-weight ultrasound based sensing, along with one COTS mono-camera, to enable 3D tracking of fingers.
    Dolphin


    Device-free Gesture Tracking using Acoustic Signals
    Wei Wang, Alex X. Liu, Ke Sun
    ACM MobiCom 2016, October 2016. (Acceptance ratio: 31/226=13.7%)

    LLAP is an ultrasound-based device-free gesture tracking scheme that can be deployed on existing mobile devices as software, without any hardware modification. We use speakers and microphones that already exist on most mobile devices to perform device-free tracking of a hand/finger.
    MIT Technology Review: This App Lets You Control Your Phone Using Sonar
    LLAP

    Service

    Reviewer
  • 2024 IEEE ICASSP, ACM IMWUT (UbiComp)
  • 2023 IEEE ICASSP, ACM TMC, ACM TOSN
  • 2022 IEEE ICASSP, ACM IMWUT (UbiComp), IEEE TMC, ACM SenSys Shadow PC
  • 2021 ACM TOSN, IEEE TMC, ACM MobileHCI
  • 2020 IEEE TMC, ACM IMWUT (UbiComp)
    • Program Committee Member:
  • 2022 ACM SenSys Shadow PC
    • Organizer and Chair:
  • 2023 ICLR Machine Learning for IoT Workshop Co-organizer and Session Chair