Principal Investigator (PI)

Ferdous Pervej

Short Bio

Ferdous Pervej received the B.Sc. degree in Electronics and Telecommunication Engineering from Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh, in 2014; the M.S. degree in Electrical Engineering from Utah State University, Logan, UT, USA, in 2019; and the Ph.D. degree in Electrical Engineering from North Carolina State University, Raleigh, NC, USA, in 2023.

He was with Mitsubishi Electric Research Laboratories, Cambridge, MA, USA, in summer 2021 and with Futurewei Wireless Research and Standards, Schaumburg, IL, USA, from May 2022 to December 2022. From August 2023 to July 2025, he was a Postdoctoral Scholar in the Ming Hsieh Department of Electrical and Computer Engineering at the University of Southern California (USC), Los Angeles, CA, USA. He is currently an Assistant Professor of Computer Engineering within the Electrical and Computer Engineering Department at USU. His primary research interests are wireless communication and networking, distributed machine learning, vehicle-to-everything communication, edge computing/caching, and machine learning for wireless communications and networking.

Professional/Academic Background

  • Assistant Professor of Computer Engineering, Department of Electrical and Computer Engineering, Utah State University, Logan, UT (August 2025 - Present)
  • Visiting Scholar, Wireless Devices and Systems Group, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA (February 2026 - Present)
  • Postdoctoral Scholar - Research Associate, Wireless Devices and Systems Group, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA (August 2023 - July 2025)
  • Ph.D. in Electrical Engineering, North Carolina State University, Raleigh, NC (July 2023)
    • Graduate (Research/Teaching) Assistant, Department of ECE, NC State University, Raleigh, NC (August 2019 - July 2023)
    • Research Intern - Machine Learning for 5G Next Generation Radio Access Networks, Wireless Research and Standards, Futurewei Technologies, Inc., Schaumburg, IL (May 2022 - December 2022)
    • Research Intern - Advanced Vehicular Technologies, Mitsubishi Electric Research Laboratories, Cambridge, MA (May 2021 - August 2021)
  • MS in Electrical Engineering, Utah State University (June 2019)
    • Graduate (Research/Teaching) Assistant, Department of ECE, Utah State University, Logan, UT (August 2017 - June 2019)
  • B.Sc. in Electronics and Telecommunication Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh (December 2014)

Service

Technical Program Committee (TPC) Member:

  1. INFOCOM 2026, Tokyo, Japan
  2. INFOCOM 2025, London, UK
  3. 3SCity-E2C Workshop, GLOBECOM 2024, Cape Town, South Africa
  4. ICN 2021, Porto, Portugal

Reviewer:

  • Selected Journals/Magazines
    1. IEEE Journal on Selected Areas in Communications ( J-SAC )
    2. IEEE Transactions on Wireless Communications ( TWC )
    3. IEEE Transactions on Communication ( TCom )
    4. IEEE Transactions on Vehicular Technology (TVT)
    5. IEEE Transactions on Mobile Computing ( TMC )
    6. IEEE Internet of Things Journal
    7. IEEE Access
    8. IEEE Communications Letters
    9. IEEE Vehicular Technology Magazine
    10. Computer Networks (Elsevier)
  • Selected Conferences
    1. IEEE Globecom
    2. IEEE ICC
    3. IEEE VTC
    4. International Conference on Edge Computing (EDGE)

Memberships

  • IEEE
    1. Member (Aug. 2023 - Present)
      • IEEE Communication Society
      • IEEE Vehicular Technology Society
    2. Graduate Student Member (2019 - 2023)
      • IEEE Communication Society
      • IEEE Vehicular Technology Society

Talks and Presentations

  1. Personalized Hierarchical Split Federated Learning in Wireless Networks @ IEEE ICC , Montreal , Quebec , Canada , June 202 5
  2. Double Directional Wireless Channel Generation: A Statistics-Informed Generative Approach @ IEEE ICC , Montreal, Quebec, Canada, June 2025
  3. Efficient Machine Learning and Resource-Constrained Wireless Networks for Connected World @ Utah State University , Logan, UT, Feb. 2025
  4. Machine Learning for Wireless Systems: Overview and Case Study of Propagation Channel Modeling (joint presentation with A. F. Molisch) @ Amazon Lab126 , Amazon Inc. (virtual), Dec. 2024
  5. Foundation Models for Wireless Channel (joint presentation with A. F. Molisch) @ Amazon Lab126 , Amazon Inc. (virtual), Aug. 2024
  6. Resource-aware Hierarchical Federated Learning for Video Caching in Wireless Networks @ IEEE ICC , Denver, CO, USA, June 2024
  7. Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles @ IEEE Intel. Vehicles Symp. , Aachen, Germany (virtual), June 2022
  8. Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach @ IEEE VTC2020-Fall , Victoria, BC, Canada (virtual), Dec.  2020
  9. Dynamic Power Allocation and Virtual Cell Formation for Throughput-Optimal Vehicular Edge Networks in Highway Transportation @ IEEE ICC Workshops , Dublin, Ireland (virtual), June 2020
  10. User Preference Learning-Aided Collaborative Edge Caching for Small Cell Networks @ IEEE Globecom , Taipei, Taiwan (virtual), Dec. 2020
  11. Artificial Intelligence Assisted Collaborative Edge Caching in Small Cell Networks @ IEEE Globecom , Taipei, Taiwan (virtual), Dec. 2020