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Department of Computer Science and Technology

  • PhD Student

Biography

Young Kwon is a Ph.D. student majoring in Computer Science in the Mobile Systems Group at the University of Cambridge, where he is supervised by Prof. Cecilia Mascolo and funded by Nokia Bell Labs.
 
His research focuses on resource-constrained mobile systems to enable context-aware adaptation to the changing environments (e.g., new inputs and users) in real-time while preserving privacy. To this end, He leverages computational methods from continual learning, machine learning, and network science.
 
For more details, please visit his personal website (Link)

Publications

Journal articles

  • Shatilov, KA., Kwon, YD., Lee, LH., Chatzopoulos, D. and Hui, P., 2023. MyoKey: Inertial Motion Sensing and Gesture-Based QWERTY Keyboard for Extended Realities IEEE Transactions on Mobile Computing, v. 22
    Doi: 10.1109/TMC.2022.3156939
  • Kwon, YD., Chauhan, J., Kumar, A., Hkust, PH. and Mascolo, C., 2021. Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications 6th ACM/IEEE Symposium on Edge Computing, SEC 2021,
    Doi: http://doi.org/10.1145/3453142.3491285
  • Chauhan, J., Kwon, YD., Hui, P. and Mascolo, C., 2020. ContAuth: Continual Learning Framework for Behavioral-based User Authentication Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, v. 4
    Doi: http://doi.org/10.1145/3432203
  • Kumar, A., Braud, T., Kwon, YD. and Hui, P., 2020. Aquilis: Using contextual integrity for privacy protection on mobile devices Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, v. 4
    Doi: 10.1145/3432205
  • Conference proceedings

  • Das, A., Kwon, YD., Chauhan, J. and Mascolo, C., 2022. Enabling On-Device Smartphone GPU based Training: Lessons Learned 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2022,
    Doi: 10.1109/PerComWorkshops53856.2022.9767442
  • Chen, L-Y., Chen, Y., Kwon, YD., Kang, Y. and Hui, P., 2021. IAN: interpretable attention network for churn prediction in LBSNs Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining,
    Doi: http://doi.org/10.1145/3487351.3488328
  • Kwon, YD., Chauhan, J. and Mascolo, C., FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications Proc. Interspeech 2021,
    Doi: 10.21437/Interspeech.2021-1091
  • Kwon, YD., Chauhan, J., Kumar, A., Hui, P. and Mascolo, C., Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications
  • Vallapuram, AK., Nanda, N., Kwon, YD. and Hui, P., Interpretable business survival prediction Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining,
    Doi: http://doi.org/10.1145/3487351.3488353
  • Kwon, YD., Chauhan, J. and Mascolo, C., YONO: Modeling Multiple Heterogeneous Neural Networks on Microcontrollers
  • Contact Details

    Room: 
    FN01
    Email: 

    ydk21@cam.ac.uk