Research work

I’m broadly interested in the interplay of artificial intelligence, signal processing, statistical learning, optimization theory, and how these areas contribute to practical advancements in AI, communications, and data analysis.

You can also check my Google Scholar.

 

---

 

Selected Publications

• One-bit compressive sensing: Can we go deep and blind?
  Y Zeng*, S Khobahi*, M Soltanalian, IEEE Signal Processing Letters 29, 1629-1633, 2022. (∗: equal contributions)

• Coronet: a deep network architecture for enhanced identification of covid-19 from chest x-ray images
  C Agarwal*, S Khobahi*, D Schonfeld, M Soltanalian, Medical Imaging 2021: Computer-Aided Diagnosis 11597, 484-490, 2021. (∗: equal contributions)

• LoRD-Net: Unfolded deep detection network with low-resolution receivers
  S Khobahi, N Shlezinger, M Soltanalian, YC Eldar, IEEE Transactions on Signal Processing 69, 5651-5664, 2021.

• Efficient waveform covariance matrix design and antenna selection for MIMO radar
  A Bose*, S Khobahi*, M Soltanalian, Signal Processing 183, 107985, 2021. (∗: equal contributions)

• Model-inspired deep detection with low-resolution receivers
  S Khobahi, N Shlezinger, M Soltanalian, YC Eldar, 2021 IEEE International Symposium on Information Theory (ISIT), 3349-3354, 2021.

• Deep one-bit compressive autoencoding
  S Khobahi, A Bose, M Soltanalian, 2021 IEEE Statistical Signal Processing Workshop (SSP), 371-375, 2021.

• Model-based deep learning for one-bit compressive sensing
  *S Khobahi**, M Soltanalian, *IEEE Transactions on Signal Processing 68, 5292-5307, 2020.

• UPR: A model-driven architecture for deep phase retrieval
  N Naimipour*, S Khobahi*, M Soltanalian, 2020 54th Asilomar Conference on Signals, Systems, and Computers, 205-209, 2020. (∗: equal contributions)

• Deep radar waveform design for efficient automotive radar sensing
  S Khobahi, A Bose, M Soltanalian, 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM …, 2020.

• Deep-url: A model-aware approach to blind deconvolution based on deep unfolded richardson-lucy network
  C Agarwal*, S Khobahi*, A Bose, M Soltanalian, D Schonfeld, 2020 IEEE International Conference on Image Processing (ICIP), 3299-3303, 2020. (∗: equal contributions)

• Unfolded algorithms for deep phase retrieval
  N Naimipour*, S Khobahi*, Soltanalian, arXiv preprint arXiv:2012.11102, 2020. (∗: equal contributions)

• Deep-RLS: A model-inspired deep learning approach to nonlinear PCA
  Z Esmaeilbeig, S Khobahi, M Soltanalian</u>, arXiv preprint arXiv:2011.07458, 2020.

• Deep signal recovery with one-bit quantization
  S Khobahi, N Naimipour, M Soltanalian, YC Eldar, ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019.

• Optimized transmission for parameter estimation in wireless sensor networks
  **S Khobahi*, M Soltanalian, F Jiang, AL Swindlehurst, *IEEE Transactions on Signal and Information Processing over Networks 6, 35-47, 2019.

• Joint optimization of waveform covariance matrix and antenna selection for MIMO radar
  A Bose*, S Khobahi*, M Soltanalian, 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 1534-1538, 2019. (∗: equal contributions)

• Signal recovery from 1-bit quantized noisy samples via adaptive thresholding
  S Khobahi, M Soltanalian, 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 1757-1761, 2018.

• Optimized transmission for consensus in wireless sensor networks
  S Khobahi, M Soltanalian, 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018.

My research during my undergrad studies:

• A majorization–minimization approach for reducing out-of-band radiations in OFDM systems
  MM Naghsh, EHM Alian, S Khobahi, O Rezaei, IEEE Communications Letters 21 (8), 1739-1742, 2017.