Education:

• 2017 – Ph.D. in Computer Science, University of California, Irvine, USA
• 2013 – 2016 M.S. in Electrical Engineering, University of Tehran, Tehran, Iran
• 2009 – 2013 B.S. in Electrical Engineering, University of Tehran, Tehran, Iran

Bio:

Peyman Tehrani received his B.S. and M.S degree in Electrical engineering from the University of Tehran, Tehran, Iran, in 2013 and 2016, respectively. In 2016, he was a student visiting researcher for six months at the University of Padova, Italy. Since Fall 2017, he has been working toward the Ph.D. degree in Networked Systems under the supervision of Prof. Marco Levorato, with a focus on the application of deep reinforcement learning and federated learning for the distributed control of wireless systems and smart energy networks. He interned with Qualcomm Inc. in summer 2020 as a Machine learning-Wireless system Intern, working on the using deep learning algorithms for channel prediction in 5G networks.

Email:

peymant@uci.edu

Links:

Linkedin , Google Scholar , Github

Preprints:

1. P. Tehrani, F. Lahouti, and M. Zorzi. “Resource allocation in heterogeneous full-duplex OFDMA networks: Design and analysis.” arXiv preprint arXiv:1802.03012 (2018).

Publications:

1. P. Tehrani, F. Restuccia, and M. Levorato, “Federated deep reinforcement learning for the distributed control of NextG wireless networks,” in 2021 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). IEEE, 2021, pp. 248–253..
2. P.  Tehrani  and  M.  Levorato,  “Frequency-based  multi  task  learning  with attention mechanism for fault detection in power systems,” in 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).IEEE, 2020, pp. 1–6.
3. S.  Baidya,  P.  Tehrani,  and  M.  Levorato,  “Data-driven  path  selection  for real-time  video  streaming  at  the  network  edge,”  in 2020 IEEE International Conference on Communications Workshop on Edge Machine Learning for 5G Mobile Networks and Beyond (ICC Workshops), IEEE, 2020, pp. 1–6.
4. M. R. Mili, P. Tehrani, and M. Bennis, “Energy-efficient power allocation in  ofdma  d2d  communication  by  multiobjective  optimization,” IEEE Wireless Communications Letters, vol. 5, no. 6, pp. 668–671, 2016.
5. P. Tehrani, F. Lahouti, and M. Zorzi. “Resource allocation in OFDMA networks with half-duplex and imperfect full-duplex users.” In 2016 IEEE international conference on communications (ICC), pp. 1-6. IEEE, 2016.