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Nima Mohammadi

Degree Objective: PhD

Research Interests:

  • Emerging Application of Machine Learning in Wireless Networks
  • Cognitive Radio Networks
  • Spiking Neural Networks and Neuromorphic Computing
  • Distributed/Federated Learning
  • Differential Privacy
  • Explainable AI

Education:

  • M.Sc. in Computer Science, University of Tehran, Tehran, Iran, 2017
  • B.Sc. in Computer Engineering, SRTTU, Tehran, Iran, 2013

Publications:

  • Mohammadi N, Bai J, Fan Q, Song Y, Yi Y, Liu L. Differential Privacy Meets Federated Learn-ing under Communication Constraints. IEEE Internet of Things Journal. 2021 Aug.
  • Mohammadi N, Liu L, Yi Y, Policy-based Fully Spiking Reservoir Computing for Multi-AgentDistributed Dynamic Spectrum Access. In 2022 IEEE International Conference on Commu-nications (ICC): Green Communication Systems and Networks Symposium (IEEE ICC’22 -GCSN Symposium)
  • Zheng H,Mohammadi N, Bai K, Yi Y. Low-power Analog and Mixed-signal IC Design of Mul-tiplexing Neural Encoder in Neuromorphic Computing. In 2021 22nd IEEE InternationalSymposium on Quality Electronic Design (ISQED) 2021 Apr 7.
  • Emamjomeh A, Goliaei B, Torkamani A, Ebrahimpour R,Mohammadi N, Parsian A. Protein-protein interaction prediction by combined analysis of genomic and conservation informa-tion. Genes & genetic systems. 2014;89(6):259-72.
  • Ahangi A, Karamnejad M,Mohammadi N, Ebrahimpour R, Bagheri N. Multiple classifier sys-tem for EEG signal classification with application to brain-computer interfaces. Neural Computing and Applications. 2013 Oct 1;23(5):1319-27.
  • Ebrahimpour R, Sadeghnejad N, Arani SA,Mohammadi N. Boost-wise pre-loaded mixture ofexperts for classification tasks. Neural Computing and Applications. 2013 May 1;22(1):365-77.