I will be a student organizer at the 1st FedGraph Workshop! |
Jun 2022 |
Reviewer for NeurIPS’22 Conference: Main and Dataset \& Benchmark Tracks! |
Jun 2022 |
SpreadGNN accepted to AAAI’22 Conference! |
Oct 2021 |
I am a Ph.D. student in Prof. Salman Avestimehr’s Information Theory and Machine Learning (vITAL) research lab at the University of Southern California (USC) in Los Angeles . I pursue research on the foundations and applications of federated learning, graph neural networks, probabilistic deep learning, and information theory. I seek to design learning algorithms that can effectively handle the non-I.I.D. nature of real-life data in federated setting. I try to actively contribute to FedML, a promising research library for federated learning as I believe that federated learning systems should be accessible for everyone. Previously, I was a Master’s student at Bilkent University, Ankara, Turkey, and an R&D engineer at Turkcell Technology under my master’s advisor, Dr. Salih Ergut with a prestigious 5G & Beyond Graduate Support Program.
Federated Learning | Graph Neural Networks |
Approximate Bayesian Inference | Bayesian Deep Learning |
Deep Probabilistic Generative Models | Mathematical Foundations of Machine Learning |
Ph.D. in Electrical & Computer Engineering
University of Southern California | Los Angeles, CA
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Jan 2021 - Present |
M.Sc. in Electrical & Electronics Engineering (High Honors)
Bilkent University | Ankara,TR
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Sep 2013 - Dec 2020 |
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Personalized, Federated, And Unified MRI Contrast Synthesis O. Dalmaz, U. Mirza, G. Elmas, M. Özbey, S. Dar, E. Ceyani, S. Avestimehr, and T. Çukur Published at the IEEE 20th International Symposium on Biomedical Imaging (ISBI), Virtual Conference 2023 [1] [pdf] |
Federated Learning of Generative Image Priors for MRI Reconstruction G. Elmas, S. Dar, Y. Korkmaz, E. Ceyani, B. Susam, M. Özbey, S. Avestimehr, and T. Çukur Published at the IEEE Transactions on Medical Imaging 2022 [2] [pdf] |
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pFLSynth: Personalized Federated Learning of Image Synthesis in Multi-Contrast MRI O. Dalmaz, U. Mirza, G. Elmas, M. Özbey, S. Dar, E. Ceyani, S. Avestimehr, and T. Çukur Accepted to NeurIPS Medical Imaging Meets as oral presentation 2022 [3] [pdf] |
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data C. He*, E. Ceyani*, K. Balasubramanian*, M. Annavaram, and S. Avestimehr Published at the AAAI'22 (AR: 15% (1349/9020), poster), FL-ICML'21 & DLG-KDD'21. 2021 [4] [abs] [pdf] [code] [slide] [poster] |
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FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks C. He*, K. Balasubramanian*, E. Ceyani*, C. Yang, H. Xie, L. Sun, L. He, L. Yang, P. Yu, Y. Rong, P. Zhao, J. Huang, M. Annavaram, and S. Avestimehr Accepted to ICLR - DPML 2021 & MLSys - GNNSys'21. Collaborated with Tencent AI. 2021 [5] [abs] [pdf] [code] [slide] [poster] [video] |
Finalist at | 2022 |
Full scholarship for Deep|Bayes & PRAIRIE AI ML summer schools. | 2019 |
5G & Beyond Graduate Support Program and Bilkent Graduate Scholarship | 2018 - 2020 |
Research Excellence Award Awarded by Bilkent University. | 2018 |
OREDATA | 2021 |
MIT - FL Reading Group | 2021 |
The 1st International Workshop on Federated Learning with Graph Data (FedGraph2022-ACM CIKM) |
Workshop on Cross-Community Federated Learning: Algorithms, Systems and Co-designs (CrossFL-MLSYS'22) |
Neural Information Processing Systems (NeurIPS'22-'23, Main and Dataset and Benchmark Tracks) |
IEEE Transactions of Neural Networks and Learning Systems (TNNLS) |
IEEE Transactions of Big Data (TBD) |
Languages | C, C++, Java, MATLAB, Julia, Python |
Frameworks | JAX, NumPy, Pandas, PyTorch, Pyro, SciPy, Keras |
Tools | Linux, vim, git, tmux, zsh |
All credit goes to Brandon Amos. Last updated on 2024-01-13