I was a PhD candidate in Electrical and Computer Engineering at University of Illinois at Urbana-Champaign where I was advised by Prof. Ivan Dokmanić. My research broadly focused on designing machine learning and signal processing algorithms with applications in computational imaging and computer vision.

Previously, I got an MS degree in ECE at UIUC in 2018 where I worked with Prof. Haitham Hassanieh and Prof. Romit Roy Chowdhury on building wireless and mobile sensing systems.

I graduated with a B.Tech in Electrical Engineering and a minor in Computer Science (AI) from Indian Institute of Technology, Kanpur in 2016.

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Research interests

Signal processing

Deep learning for inverse problems

Equivariant and invariant representation learning

Publications

2023

FunkNN: Neural Interpolation for Functional Generation
, , and
ICLR, 2023
More URL arXiv 
@article{khorashadizadeh2022funknn,
  title={FunkNN: Neural Interpolation for Functional Generation},
  author={Khorashadizadeh, AmirEhsan and Chaman, Anadi and Debarnot, Valentin and Dokmani{\'c}, Ivan},
  journal={ICLR},
  year={2023},
  projectpage = {https://sada.dmi.unibas.ch/en/research/implicit-neural-representation},
  volume={abs/2212.14042},
  eprint={2212.14042},
  archivePrefix={arXiv},
  url={https://openreview.net/forum?id=BT4N_v7CLrk}
}

2022

Learning multiscale convolutional dictionaries for image reconstruction
, , and
IEEE Transactions on Computational Imaging, 2022
URL 
@article{Liu2022learning,
  title={Learning multiscale convolutional dictionaries for image reconstruction},
  author={Liu, Tianlin and Chaman, Anadi and Belius, David and Dokmani{\'c}, Ivan},
  journal={IEEE Transactions on Computational Imaging},
  volume={8},
  pages={425--437},
  year={2022},
  publisher={IEEE},
  url = {https://ieeexplore.ieee.org/iel7/6745852/9679468/09775596.pdf}
}

2021

Truly Shift-Invariant Convolutional Neural Networks
and
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
URL 
@inproceedings{Chaman_2021_CVPR,
    author    = {Chaman, Anadi and Dokmani{\'c}, Ivan},
    title     = {Truly Shift-Invariant Convolutional Neural Networks},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    url       = {https://openaccess.thecvf.com/content/CVPR2021/papers/Chaman_Truly_Shift-Invariant_Convolutional_Neural_Networks_CVPR_2021_paper.pdf},
    pages     = {3773-3783}
}
Truly Shift-Invariant Convolutional Neural Networks
and
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
URL 
@inproceedings{Chaman_2021_CVPR,
    author    = {Chaman, Anadi and Dokmani{\'c}, Ivan},
    title     = {Truly Shift-Invariant Convolutional Neural Networks},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    url       = {https://openaccess.thecvf.com/content/CVPR2021/papers/Chaman_Truly_Shift-Invariant_Convolutional_Neural_Networks_CVPR_2021_paper.pdf},
    pages     = {3773-3783}
}

2019

Multipath-enabled private audio with noise
, , and
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
URL 
@inproceedings{chaman2019multipath,
  title={Multipath-enabled private audio with noise},
  author={Chaman, Anadi and Liu, Yu-Jeh and Casebeer, Jonah and Dokmani{\'c}, Ivan},
  booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={685--689},
  year={2019},
  organization={IEEE},
  url = {https://ieeexplore.ieee.org/abstract/document/8683045/},
  

}

2018

Ghostbuster: Detecting the Presence of Hidden Eavesdroppers
, , , and
Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, 2018
URL 
@inproceedings{ghostbuster2018mobicom,
author = {Chaman, Anadi and Wang, Jiaming and Sun, Jiachen and Hassanieh, Haitham and Roy Choudhury, Romit},
title = {Ghostbuster: Detecting the Presence of Hidden Eavesdroppers},
year = {2018},
isbn = {9781450359030},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3241539.3241580},
doi = {10.1145/3241539.3241580},
abstract = {This paper explores the possibility of detecting the hidden presence of wireless eavesdroppers. Such eavesdroppers employ passive receivers that only listen and never transmit any signals making them very hard to detect. In this paper, we show that even passive receivers leak RF signals on the wireless medium. This RF leakage, however, is extremely weak and buried under noise and other transmitted signals that can be 3-5 orders of magnitude larger. Hence, it is missed by today's radios. We design and build Ghostbuster, the first device that can reliably extract this leakage, even when it is buried under ongoing transmissions, in order to detect the hidden presence of eavesdroppers. Ghostbuster does not require any modifications to current transmitters and receivers and can accurately detect the eavesdropper in the presence of ongoing transmissions. Empirical results show that Ghostbuster can detect eavesdroppers with more than 95% accuracy up to 5 meters away.},
booktitle = {Proceedings of the 24th Annual International Conference on Mobile Computing and Networking},
pages = {337–351},
numpages = {15},
keywords = {wireless, security, eavesdropper},
location = {New Delhi, India},
series = {MobiCom '18}
}