Emir Ceyani
Ph.D. Student at vITAL Lab, President at USC TGSA, and Research Intern at FedML.ai
ceyani@usc.edu

CV

Recent News

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

Short Bio

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.

Research Interests

Federated Learning Graph Neural Networks
Approximate Bayesian Inference Bayesian Deep Learning
Deep Probabilistic Generative Models Mathematical Foundations of Machine Learning

Education

Ph.D. in Electrical & Computer Engineering
University of Southern California | Los Angeles, CA


Advisor: Prof. Salman Avestimehr

Jan 2021 - Present
M.Sc. in Electrical & Electronics Engineering (High Honors)
Bilkent University | Ankara,TR


B.Sc. in Electrical and Electronics Engineering (High Honors)

Sep 2013 - Dec 2020

Experience

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Publications

Google Scholar

Semantic Scholar

2023

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]

2022

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]
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]

2021

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]
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]

Honors & Awards

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

Invited Talks

OREDATA 2021
MIT - FL Reading Group 2021

Professional Activities

President of USC Turkish Graduate Student Association (TGSA) Jun 2022 - Present
Organizer at the 1st & 2nd International Workshops on Federated Learning with Graph Data 2022 - 2023
Techincal Program Committee Member at the CrossFL Workshop, MLSYS2022 2022
Teaching Assistant at ProbAI 2021 Summer School 2021

Reviewing

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)

Skills

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