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Machine Learning and Artificial Intelligence Subscribe to RSS - Machine Learning and Artificial Intelligence

Read more at: Fazilet Gokbudak

Fazilet Gokbudak

Read more at: Julius Cheng

Julius Cheng

Read more at: Jan Blumenkamp

Jan Blumenkamp

Read more at: David Buterez

David Buterez

Read more at: Michalis Korakakis

Michalis Korakakis

Read more at: Gilberto Atondo Siu

Gilberto Atondo Siu

I am a PhD student in Computer Science at the University of Cambridge under the supervision of Dr Alice Hutchings.

I work in the Cambridge Cybercrime Centre and my research is focused on applications of Machine Learning and Natural Language Processing to Cybercrime.

My MPhil Research Project at the University of Cambridge was about automated NLP approaches for currency exchange analysis in underground forums.

At the moment, I am analysing the impact of COVID-19 in the proliferation and evolution of investment fraud and financial scams.

Read more at: Nida Itrat Abbasi

Nida Itrat Abbasi

Research Group:

Cambridge Affective Computing and Robotics Group, Computer Laboratory, University of Cambridge.

 

Read more at: Nicholas Boucher

Nicholas Boucher

Please see Academic Webpage for more information.

Read more at: Stefanos Bakirtzis

Stefanos Bakirtzis

Theses / dissertations

  • Bakirtzis, SS., 2024 (No publication date). Applications of Artificial Intelligence to Electromagnetism and Indoor Wireless Networks
    Doi: 10.17863/CAM.113346
    •

    Conference proceedings

  • Bakirtzis, S., Fiore, M. and Wassell, I., 2024 (Accepted for publication). Towards Physics-Informed Graph Neural Network-based Computational Electromagnetics
    Doi: 10.17863/CAM.108313
    •
  • Bakirtzis, S., Fiore, M. and Wassell, I., 2024. Towards Physics-Informed Graph Neural Network-based Computational Electromagnetics IEEE Antennas and Propagation Society AP S International Symposium Digest,
    Doi: 10.1109/AP-S/INC-USNC-URSI52054.2024.10686000
    •
  • Qiu, K., Bakirtzis, S., Wassell, IJ., Song, H., Lin, K. and Zhang, J., 2023. IRDM: A Generative Diffusion Model for Indoor Radio Map Interpolation. GLOBECOM,
    •
  • Qiu, K., Bakirtzis, S., Song, H., Wassell, IJ. and Zhang, J., 2023. Deep Learning-Based Path Loss Prediction for Outdoor Wireless Communication Systems. ICASSP,
    •
  • Bakirtzis, S., Zanella, AF., Rubrichi, S., Ziemlicki, C., Smoreda, Z., Wassell, I., Zhang, J. and Fiore, M., 2023. Characterizing Mobile Service Demands at Indoor Cellular Networks Proceedings of the ACM SIGCOMM Internet Measurement Conference IMC,
    Doi: 10.1145/3618257.3624807
    •
  • Bakirtzis, S., Wassell, IJ., Fiore, M. and Zhang, J., 2022. Stochastic Evaluation of Indoor Wireless Network Performance with Data-Driven Propagation Models. GLOBECOM,
    •
  • Bakirtzis, S., Wassell, I., Fiore, M. and Zhang, J., 2022. Stochastic Evaluation of Indoor Wireless Network Performance with Data-Driven Propagation Models Proceedings IEEE Global Communications Conference Globecom,
    Doi: 10.1109/GLOBECOM48099.2022.10001717
    •
  • Bakirtzis, S., Qiu, K., Zhang, J. and Wassell, I., 2021 (Accepted for publication). DeepRay: Deep Learning Meets Ray-Tracing 2022 16th European Conference on Antennas and Propagation, EuCAP 2022,
    Doi: 10.23919/EuCAP53622.2022.9769203
    •

    Journal articles

  • Bakirtzis, S., Wassell, I., Fiore, M. and Zhang, J., 2024. AI-Assisted Indoor Wireless Network Planning With Data-Driven Propagation Models IEEE Network, v. 38
    Doi: 10.1109/MNET.2024.3397801
    •
  • Ferreira, GO., Zanella, AF., Bakirtzis, S., Ravazzi, C., Dabbene, F., Calafiore, GC., Wassell, I., Zhang, J. and Fiore, M., 2024. A Joint Optimization Approach for Power-Efficient Heterogeneous OFDMA Radio Access Networks IEEE Journal on Selected Areas in Communications,
    Doi: 10.1109/JSAC.2024.3431524
    •
  • Bakirtzis, S., Chen, J., Qiu, K., Zhang, J. and Wassell, I., 2022. EM DeepRay: An Expedient, Generalizable and Realistic Data-Driven Indoor Propagation Model IEEE Transactions on Antennas and Propagation,
    Doi: 10.1109/tap.2022.3172221
    •
  • Bakirtzis, S., Qiu, K., Wassell, I., Fiore, M. and Zhang, J., 2022. Deep Learning-based Multivariate Time Series Classification for Indoor/Outdoor Detection IEEE Internet of Things Journal,
    Doi: 10.1109/jiot.2022.3190555
    •
  • Qiu, K., Bakirtzis, S., Song, H., Zhang, J. and Wassell, I., 2022. Pseudo Ray-Tracing: Deep Leaning Assisted Outdoor mm-Wave Path Loss Prediction IEEE Wireless Communications Letters, v. 11
    Doi: 10.1109/LWC.2022.3175091
    •

    Datasets

  • Bakirtzis, S., Qiu, K., Wassell, I., Fiore, M. and Zhang, J., 2022. Research data supporting ''Deep Learning-based Multivariate Time Series Classification for Indoor Outdoor Detection"
    Doi: 10.17863/CAM.82668
    •
    Theses / dissertations

    2024 (No publication date)

  • Bakirtzis, SS., 2024 (No publication date). Applications of Artificial Intelligence to Electromagnetism and Indoor Wireless Networks
    Doi: 10.17863/CAM.113346
    •
    Conference proceedings

    2024 (Accepted for publication)

  • Bakirtzis, S., Fiore, M. and Wassell, I., 2024 (Accepted for publication). Towards Physics-Informed Graph Neural Network-based Computational Electromagnetics
    Doi: 10.17863/CAM.108313
    •

    2024

  • Bakirtzis, S., Fiore, M. and Wassell, I., 2024. Towards Physics-Informed Graph Neural Network-based Computational Electromagnetics IEEE Antennas and Propagation Society AP S International Symposium Digest,
    Doi: 10.1109/AP-S/INC-USNC-URSI52054.2024.10686000
    •

    2023

  • Bakirtzis, S., Zanella, AF., Rubrichi, S., Ziemlicki, C., Smoreda, Z., Wassell, I., Zhang, J. and Fiore, M., 2023. Characterizing Mobile Service Demands at Indoor Cellular Networks Proceedings of the ACM SIGCOMM Internet Measurement Conference IMC,
    Doi: 10.1145/3618257.3624807
    •
  • Qiu, K., Bakirtzis, S., Wassell, IJ., Song, H., Lin, K. and Zhang, J., 2023. IRDM: A Generative Diffusion Model for Indoor Radio Map Interpolation. GLOBECOM,
    •
  • Qiu, K., Bakirtzis, S., Song, H., Wassell, IJ. and Zhang, J., 2023. Deep Learning-Based Path Loss Prediction for Outdoor Wireless Communication Systems. ICASSP,
    •

    2022

  • Bakirtzis, S., Wassell, IJ., Fiore, M. and Zhang, J., 2022. Stochastic Evaluation of Indoor Wireless Network Performance with Data-Driven Propagation Models. GLOBECOM,
    •
  • Bakirtzis, S., Wassell, I., Fiore, M. and Zhang, J., 2022. Stochastic Evaluation of Indoor Wireless Network Performance with Data-Driven Propagation Models Proceedings IEEE Global Communications Conference Globecom,
    Doi: 10.1109/GLOBECOM48099.2022.10001717
    •

    2021 (Accepted for publication)

  • Bakirtzis, S., Qiu, K., Zhang, J. and Wassell, I., 2021 (Accepted for publication). DeepRay: Deep Learning Meets Ray-Tracing 2022 16th European Conference on Antennas and Propagation, EuCAP 2022,
    Doi: 10.23919/EuCAP53622.2022.9769203
    •
    Journal articles

    2024

  • Bakirtzis, S., Wassell, I., Fiore, M. and Zhang, J., 2024. AI-Assisted Indoor Wireless Network Planning With Data-Driven Propagation Models IEEE Network, v. 38
    Doi: 10.1109/MNET.2024.3397801
    •
  • Ferreira, GO., Zanella, AF., Bakirtzis, S., Ravazzi, C., Dabbene, F., Calafiore, GC., Wassell, I., Zhang, J. and Fiore, M., 2024. A Joint Optimization Approach for Power-Efficient Heterogeneous OFDMA Radio Access Networks IEEE Journal on Selected Areas in Communications,
    Doi: 10.1109/JSAC.2024.3431524
    •

    2022

  • Qiu, K., Bakirtzis, S., Song, H., Zhang, J. and Wassell, I., 2022. Pseudo Ray-Tracing: Deep Leaning Assisted Outdoor mm-Wave Path Loss Prediction IEEE Wireless Communications Letters, v. 11
    Doi: 10.1109/LWC.2022.3175091
    •
  • Bakirtzis, S., Chen, J., Qiu, K., Zhang, J. and Wassell, I., 2022. EM DeepRay: An Expedient, Generalizable and Realistic Data-Driven Indoor Propagation Model IEEE Transactions on Antennas and Propagation,
    Doi: 10.1109/tap.2022.3172221
    •
  • Bakirtzis, S., Qiu, K., Wassell, I., Fiore, M. and Zhang, J., 2022. Deep Learning-based Multivariate Time Series Classification for Indoor/Outdoor Detection IEEE Internet of Things Journal,
    Doi: 10.1109/jiot.2022.3190555
    •
    Datasets

    2022

  • Bakirtzis, S., Qiu, K., Wassell, I., Fiore, M. and Zhang, J., 2022. Research data supporting ''Deep Learning-based Multivariate Time Series Classification for Indoor Outdoor Detection"
    Doi: 10.17863/CAM.82668
    •
    Read more at: KEHAI

    KEHAI

    Department of Computer Science and Technology
    University of Cambridge
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    15 JJ Thomson Avenue
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    CB3 0FD

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