Mahdi Morafah

Machine Learning Researcher
Generative AI and Federated Learning Enthusiast
Former ML Research Intern at Tesla Autopilot and Qualcomm AI


Department of Electrical and Computer Engineering

University of California San Diego

9500 Gilman Dr, La Jolla, CA 92093

Email: mmorafah 'at' ucsd 'dot' edu

I am a PhD Candidate (third year) in Electrical and Computer Engineering at University of California San Diego (UCSD), luckly being advised by Prof. Bill Lin.

I am broadly interested in Machine Learning and Optimization. My current research focus is on Federated Learning, Generative AI and Business. Here are the research themes that I am currently working on:

  • Large Scale Federated Learning Over Diverse Heterogeneous Devices
  • Efficient Training and Fine-tuning of Large Language Models in Federated Learning
  • Fast Sampling for Diffusion Models
  • Multi-Modal Foundation Models
  • Effect of Generative AI in Business Market

I previously received a M.S. in Electrical and Computer Engineering at UCSD in 2021, and a B.S. in Electrical Engineering at Tehran Polytechnique.

I also working as a machine learning research intern at Tesla Autopilot and Qualcomm AI.

I am looking for machine learning internship positions for summer 2024.

I am open to collaborate on interesting project, please feel free to reach out.


Nov 20, 2023 I am excited to successfully pass my PhD qualifying exam and being advanced to candidacy!
Jul 20, 2023 New accepted paper “A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design” [Accepted to IEEE Transactions on AI (Jul, 2023)].
May 31, 2023 Served as Reviewer for IEEE TCNS and IEEE CDC.
Jan 5, 2023 Awarded AAAI 2023 student scholarship for conference attendance.
Sep 30, 2022 New paper on arXiv “Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks” [Accepted to FL NeurIPS’22 Workshops (Oct, 2022)].
Sep 21, 2022 New paper on arXiv “Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces” [Accepted to AAAI’23 (acceptance rate=19.6%) Nov, 2022].

selected publications

  1. Saeed Vahidian*, Mahdi Morafah*, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, and Bill Lin
    arXiv preprint arXiv:2209.10526 2022
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    Mahdi Morafah*, Saeed Vahidian*, Weijia Wang*, and Bill Lin
    arXiv preprint arXiv:2208.09754 2022
  3. ICDCSW Conference Award
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    Saeed Vahidian*, Mahdi Morafah*, and Bill Lin
    In 2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW) 2021
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    Vyacheslav Kungurtsev, Mahdi Morafah, Tara Javidi, and Gesualdo Scutari
    arXiv preprint arXiv:2110.10406 2021
  5. Mahdi Morafah*, Saeed Vahidian*, Chen Chen, Mubarak Shah, and Bill Lin
    arXiv preprint arXiv:2209.15595 2022
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    Mahdi Morafah, Weijia Wang, and Bill Lin
    IEEE Transactions on Artificial Intelligence 2023