Saeed Vahidian

Saeed Vahidian
saeed.vahidian@duke.edu

I am a postdoctoral researcher at Duke University, working with Prof. Yiran Chen. Previously, I got my Ph.D. from the Department of Electrical and Computer Engineering at the University of California San Diego (UCSD), where I was supervised by Prof. Bill Lin.

My research primarily is on machine learning, and computer vision. In particular, it revolves around Information Core Extraction (ICE), which focuses on extracting the most information core (synthetic data) from vast, redundant datasets, and Resilient Learning Structures (RLS), which ensure robustness, scalability, efficiency, and generalization when models are trained on these synthetic data. In addition, I worked on Distributed Data Fusion (DDF), the framework that incorporate ICE and RLS to synthesizing data from decentralized diverse sources into a cohesive structure, allowing for more effective model training.

Experience and Collaborations

UC San Diego PhD Student Sep 18 - Apr 2023	Duke University Postdoctoral Scholar April 2023 - Present	Qualcomm ML Researcher Summer Intern 2021	NASA Invited for Collaboration on DDF project 2022	Stanford Research~Collaboration 2018 - 2019	McGill University Research~Collaboration 2014-2016

News

[11/2024] One paper submitted to CVPR
[10/2024] Two papers submitted to ICLR
[09/2024] One paper submitted to Journal of Machine Learning Research
[06/2024] Two papers accepted to CVPR
[06/2024] Chair in CVPR 2024. I hold the 1st workshop on Dataset Distillation for Computer Vision at CVPR
[06/2024] Chair in CVPR 2024. We hold the 3rd FedVision Workshop at CVPR
[02/2024] One paper was accepted to ECCV.

Research Funding

[$20,390,000] AI Institute for Edge Computing Leveraging Next Generation Networks (Athena)
[$600,000] National Science Foundation (NSF) Grant

Publications (For a full list of my publication please see my Google Scholar)

2024
	CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation Qinsi Wang, Saeed Vahidian, Hancheng Ye, Jianyang Gu, Jianyi Zhang, Yiran Chen under review 2024 paper \| bibtex \| code \| @misc{wang2024coreinferacceleratinglargelanguage, title={CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation}, author={Qinsi Wang and Saeed Vahidian and Hancheng Ye and Jianyang Gu and Jianyi Zhang and Yiran Chen}, year={2024}, eprint={2410.18311}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2410.18311}}
	Group Distributionally Robust Dataset Distillation with Risk Minimization Saeed Vahidian, Mingyu Wang, Jianyang Gu, Vyacheslav Kungurtsev, Wei Jiang and Yiran Chen under review 2024 paper \| bibtex \| code \| @Article{SVahidian-RobustDD, author = {Saeed Vahidian and Mingyu Wang and Jianyang Gu and Vyacheslav Kungurtsev and Wei Jiang and Yiran Chen}, title = {Group Distributionally Robust Dataset Distillation with Risk Minimization}, journal = {CoRR}, volume = {abs/2402.04676}, year = {2024}, url = {https://doi.org/10.48550/arXiv.2402.04676}, doi = {10.48550/ARXIV.2402.04676}, eprinttype = {arXiv}}
	Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning Vyacheslav Kungurtsev, Yuanfang Peng, Jianyang Gu, Saeed Vahidian, Anthony Quinn, Fadwa Idlahcen, Yiran Chen Journal of Machine Learning Research 2024 paper \| bibtex \| @Article{SVahidian-ICEPL-2024, title={Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning}, author={Vyacheslav Kungurtsev and Yuanfang Peng and Jianyang Gu and Saeed Vahidian and Anthony Quinn and Fadwa Idlahcen and Yiran Chen}, year={2024}, eprint={2409.01410}, archivePrefix={arXiv}, url={https://arxiv.org/abs/2409.01410}}
	Efficient dataset distillation via minimax diffusion Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev, Haonan Wang, Wei Jiang, Yang You, Yiran Chen CVPR 2024 paper \| bibtex \| code \| @Article{SVahidian-minmax-Diffusion2024, title={Efficient dataset distillation via minimax diffusion}, author={Gu, Jianyang and Vahidian, Saeed and Kungurtsev, Vyacheslav and Wang, Haonan and Jiang, Wei and You, Yang and Chen, Yiran}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={15793--15803}, year={2024} }
	Exploring the Impact of Dataset Bias on Dataset Distillation Yao Lu, Jianyang Gu, Xuguang Chen, Saeed Vahidian, Qi Xuan CVPR 2024 paper \| bibtex \| code \| @Article{SVahidian-BiasedDD2024, author = {Yao Lu and Jianyang Gu and Xuguang Chen and Saeed Vahidian and Qi Xuan}, title = {Exploring the Impact of Dataset Bias on Dataset Distillation}, journal = {CoRR}, volume = {abs/2403.16028}, year = {2024}, url = {https://doi.org/10.48550/arXiv.2403.16028}, doi = {10.48550/ARXIV.2403.16028}, eprinttype = {arXiv}, eprint = {2403.16028}, timestamp = {Tue, 09 Apr 2024 15:12:39 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2403-16028.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
	Towards building the federatedGPT: Federated instruction tuning Saeed Vahidian, Jianyi Zhang, Martin Kuo, Chunyuan Li, Ruiyi Zhang, Tong Yu, Guoyin Wang, Yiran Chen ICASSP 2024 paper \| bibtex \| code \| @Article{SVahidian-FedGPT2024, title={Towards Building the Federated GPT: Federated Instruction Tuning}, author={Jianyi Zhang and Saeed Vahidian and Martin Kuo and Chunyuan Li and Ruiyi Zhang and Guoyin Wang and Yiran Chen}, year={2023}, eprint={2305.05644}, archivePrefix={arXiv}, primaryClass={cs.CL}}
	Unlocking the potential of federated learning: The symphony of dataset distillation via deep generative latents Saeed Vahidian, Yuqi Jia, Jingwei Sun, Jianyi Zhang, Vyacheslav Kungurtsev, Neil Zhenqiang Gong, Yiran Chen ECCV 2024 paper \| bibtex \| code \| @Article{SVahidian-DDF-ECCV2024, title={Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents}, author={Yuqi Jia and Saeed Vahidian and Jingwei Sun and Jianyi Zhang and Vyacheslav Kungurtsev and Neil Zhenqiang Gong and Yiran Chen}, year={2023}, eprint={2312.01537}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2312.01537}}
2023
	When do curricula work in federated learning? Saeed Vahidian, Sreevatsank Kadaveru, Woonjoon Baek, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin ICCV 2023 paper \| bibtex \| @Article{SVahidian-DDF-ICCV2023, author = {Saeed Vahidian and Sreevatsank Kadaveru and Woonjoon Baek and Weijia Wang and Vyacheslav Kungurtsev and Chen Chen and Mubarak Shah and Bill Lin}, title = {When Do Curricula Work in Federated Learning?}, booktitle = {{IEEE/CVF} International Conference on Computer Vision, {ICCV} 2023, Paris, France, October 1-6, 2023}, pages = {5061--5071}, publisher = {{IEEE}}, year = {2023}}
	Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces Saeed Vahidian, Mahdi Morafah, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin AAAI 2023 paper \| bibtex \| @Article{SVahidian-DDF-AAAI2023, author = {Saeed Vahidian and Mahdi Morafah and Weijia Wang and Vyacheslav Kungurtsev and Chen Chen and Mubarak Shah and Bill Lin}, editor = {Brian Williams and Yiling Chen and Jennifer Neville}, title = {Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces}, booktitle = {Thirty-Seventh {AAAI} Conference on Artificial Intelligence, {AAAI} 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, {IAAI} 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2023, Washington, DC, USA, February 7-14, 2023}, pages = {10043--10052}, publisher = {{AAAI} Press}, year = {2023}}
	Rethinking data heterogeneity in federated learning: Introducing a new notion and standard benchmarks Saeed Vahidian, Mahdi Morafah, Chen Chen, Mubarak Shah, Bill Lin IEEE Transaction on AI 2023 paper \| bibtex \| @Article{SVahidian-DDF-AAAI2023, author = {Saeed Vahidian and Mahdi Morafah and Weijia Wang and Vyacheslav Kungurtsev and Chen Chen and Mubarak Shah and Bill Lin}, editor = {Brian Williams and Yiling Chen and Jennifer Neville}, title = {Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces}, booktitle = {Thirty-Seventh {AAAI} Conference on Artificial Intelligence, {AAAI} 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, {IAAI} 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2023, Washington, DC, USA, February 7-14, 2023}, pages = {10043--10052}, publisher = {{AAAI} Press}, year = {2023}}
	Flis: Clustered federated learning via inference similarity for non-iid data distribution Saeed Vahidian, Mahdi Morafah, Weijia Wang, Bill Lin NeurIPS 2023 paper \| bibtex \| @Article{SVahidian-DDF-OJCS2023, author={Morafah, Mahdi and Vahidian, Saeed and Wang, Weijia and Lin, Bill}, journal={IEEE Open Journal of the Computer Society}, title={FLIS: Clustered Federated Learning Via Inference Similarity for Non-IID Data Distribution}, year={2023}, volume={4}, number={}, pages={109-120}}
	CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction Umar Khalid, Hasan Iqbal, Saeed Vahidian, Jing Hua, Chen Chen IEEE International Conference on Intelligent Robots and Systems (IROS) 2023 paper \| bibtex \| code \| @Article{SVahidian-DDF-IROS2023, author = {Umar Khalid and Hasan Iqbal and Saeed Vahidian and Jing Hua and Chen Chen}, title = {{CEFHRI:} {A} Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction}, booktitle = {{IROS}}, pages = {10141--10148}, year = {2023}, url = {https://doi.org/10.1109/IROS55552.2023.10341467}}
2022 and before
	Coresets for estimating means and mean square error with limited greedy samples Saeed Vahidian, Baharan Mirzasoleiman, Alexander Cloninger UAI 2020 paper \| bibtex \| Video \| @Article{SVahidian-ICE-UAI2023, author = {Saeed Vahidian and Baharan Mirzasoleiman and Alexander Cloninger}, title = {Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples}, booktitle = {Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, {UAI} 2020, virtual online, August 3-6, 2020}, series = {Proceedings of Machine Learning Research}, volume = {124}, pages = {350--359}, publisher = {{AUAI} Press}, year = {2020}}
	Personalized federated learning by structured and unstructured pruning under data heterogeneity Saeed Vahidian, Mahdi Morafah, Bill Lin IEEE ICDCSW 2021 Conference Award paper \| bibtex \| @Article{SVahidian-DDF-IROS2023, author = {Umar Khalid and Hasan Iqbal and Saeed Vahidian and Jing Hua and Chen Chen}, title = {{CEFHRI:} {A} Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction}, booktitle = {{IROS}}, pages = {10141--10148}, year = {2023}, url = {https://doi.org/10.1109/IROS55552.2023.10341467}}
	Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds Mohsen Joneidi, Saeed Vahidian, Ashkan Esmaeili, Weijia Wang, Nazanin Rahnavard, Bill Lin, Mubarak Shah CVPR 2020 paper \| bibtex \| Video \| @Article{SVahidian-ICE-CVPR2020, author = {Mohsen Joneidi and Saeed Vahidian and Ashkan Esmaeili and Weijia Wang and Nazanin Rahnavard and Bill Lin and Mubarak Shah}, title = {Select to Better Learn: Fast and Accurate Deep Learning Using Data Selection From Nonlinear Manifolds}, booktitle = {2020 {IEEE/CVF} Conference on Computer Vision and Pattern Recognition, {CVPR} 2020, Seattle, WA, USA, June 13-19, 2020}, pages = {7816--7826}, publisher = {Computer Vision Foundation / {IEEE}}, year = {2020}}
	Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models Siavash Khodadadeh, Sharare Zehtabian, Saeed Vahidian, Weijia Wang, Bill Lin, Ladislau Boloni ICLR 2020 paper \| bibtex \| @Article{SVahidian-Meta-ICLR2020, @article{DBLP:journals/corr/abs-2006-10236, author = {Siavash Khodadadeh and Sharare Zehtabian and Saeed Vahidian and Weijia Wang and Bill Lin and Ladislau B{\"{o}}l{\"{o}}ni}, title = {Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models}, journal = {CoRR}, volume = {abs/2006.10236}, year = {2020}, url = {https://arxiv.org/abs/2006.10236}}

Teaching

[Spring 2022] Guest Lecturer (ECE 284, Special Topics in Computer Engineering )
[Fall 2021] Intro/Differential Equations
[Spring 2019] Linear Algebra
[Fall 2019] Calculus/Science Engineering
[Winter 2018] Numerical Linear Algebra
[Fall 2018] Numerical Linear Algebra

Academic Services

[2024] Primary Organizer & Chair in the CVPR 1st Dataset Distillation Workshop
[2024] Co-Organizer & Chair in the CVPR 3rd FedVision Workshop
[2023] Committee member of the CVPR 2nd FedVision Workshop
[2018-present] Reviewer for ICML, NeurIPS, CVPR, IEEE Journal papers including TCOM, TVT, etc.