@article{shakibhamedan2026improving,
  title={Improving Accuracy and Efficiency in DNNs with Approximate Multipliers: Insights from Information Bottleneck Theory},
  author={Shakibhamedan, Salar and Amirafshar, Nima and Jantsch, Axel and TaheriNejad, Nima},
  journal={MDPI AI},
  volume={7},
  pages={385},
  year={2026},
  url={https://www.mdpi.com/2673-2688/7/9/385}
}

@inproceedings{nagesh2026fair,
  title={Fair Synthetic Data Generation from Limited Samples: A MIMIC-IV Case Study},
  author={Nagesh, Nitish and Shakibhamedan, Salar and Bagheri, Mahdi and Wang, Ziyu and TaheriNejad, Nima and Jantsch, Axel and Rahmani, Amir M.},
  booktitle={22nd IEEE-EMBS International Conference on Body Sensor Networks (BSN)},
  year={2026},
  url={https://openreview.net/pdf?id=vXPGM9RY7b},
  note={Nitish Nagesh and Salar Shakibhamedan contributed equally}
}

@article{nagesh2025fairtabgen,
  title={FairTabGen: Unifying Counterfactual and Causal Fairness in Synthetic Tabular Data Generation},
  author={Nagesh, Nitish and Shakibhamedan, Salar and Bagheri, Mahdi and Wang, Ziyu and TaheriNejad, Nima and Jantsch, Axel and Rahmani, Amir M.},
  journal={arXiv preprint arXiv:2508.11810},
  year={2025},
  url={https://arxiv.org/abs/2508.11810},
  note={Nitish Nagesh and Salar Shakibhamedan contributed equally}
}

@article{katare2024approximation,
  title={Approximation Strategies for Vision Models on Edge Devices: An Accuracy-Efficiency Trade-off},
  author={Katare, Dewant and Shakibhamedan, Salar and Amirafshar, Nima and TaheriNejad, Nima and Jantsch, Axel and Janssen, Marijn and Ding, Aaron Yi},
  journal={TechRxiv},
  year={2024},
  doi={10.36227/techrxiv.173337762.24402407}
}

@inproceedings{shakibhamedan2024analytical,
  title={An Analytical Approach to Enhancing DNN Efficiency and Accuracy Using Approximate Multiplication},
  author={Shakibhamedan, Salar and Jahanjoo, Anice and Aminifar, Amin and Amirafshar, Nima and TaheriNejad, Nima and Jantsch, Axel},
  booktitle={ICML Workshop on Advancing Neural Network Training},
  year={2024},
  url={https://openreview.net/pdf?id=rver7enVfY}
}

@inproceedings{shakibhamedan2024harnessing,
  title={Harnessing Approximate Computing for Machine Learning},
  author={Shakibhamedan, Salar and Aminifar, Amin and Vassallo, Luke and TaheriNejad, Nima},
  booktitle={ISVLSI},
  year={2024},
  url={https://arxiv.org/abs/2412.15211}
}

@article{shakibhamedan2024acecnn,
  title={ACE-CNN: Approximate Carry Disregard Multipliers for Energy-Efficient CNN-Based Image Classification},
  author={Shakibhamedan, Salar and Amirafshar, Nima and Baroughi, Ahmad Sedigh and Shahhoseini, Hadi Shahriar and TaheriNejad, Nima},
  journal={IEEE Transactions on Circuits and Systems I},
  year={2024},
  url={https://ieeexplore.ieee.org/abstract/document/10457067}
}

@article{shakibhamedan2024ease,
  title={EASE: Energy Optimization through Adaptation—A Review of Runtime Energy-Aware Approximate Deep Learning Algorithms},
  author={Shakibhamedan, Salar and Aminifar, Amin and TaheriNejad, Nima and Jantsch, Axel},
  journal={TechRxiv},
  year={2024},
  url={https://www.techrxiv.org/users/716188/articles/706553}
}

@inproceedings{shakibhamedan2016persian,
  title={Persian Musical Instrument Recognition System},
  author={Shakibhamedan, Salar and Hashemifard, Kooshan and Faradji, Farhad and Vali, Mansour},
  booktitle={International Conference on Advances Research on Electrical and Computer Engineering},
  year={2016},
  url={https://www.researchgate.net/publication/335790985}
}