Emir Ceyani
Ph.D. Student at vITAL Lab
ceyani@usc.edu

CV

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.

Current Position

Research Assistant | University of Southern California | Los Angeles, CA

Jan 2021- Present

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
Bilkent University | Ankara,TR


Thesis Title: Spatio-temporal Forecasting Over Graphs with Deep Learning
Advisor: Dr. Salih Ergut

2018 - 2020
B.Sc. in Electrical & Electronics Engineering
Bilkent University | Ankara,TR
2013 - 2018

Publications

Google Scholar

2021

SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
C. He*, E. Ceyani*, K. and Balasubramanian*, M. Annavaram, and S. Avestimehr
Three co-1st authors have equal contribution (alphabetical order) 2021
[1] [abs] [pdf] [code] [slide]
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
C. He*, K. Balasubramanian*, E. Ceyani*, Y. Rong, P. Zhao, J. Huang, M. Annavaram, and S. Avestimehr
Accepted to ICLR - DPML 2021 & MLSys - GNNSys'21 workshops (equal contribution) 2021
[2] [abs] [pdf] [code] [slide]

2020

Spatio-temporal forecasting over graphs with deep learning
E. Ceyani
Bilkent 2020
[3] [abs] [pdf]

2018

A highly efficient recurrent neural network architecture for data regression
T. Ergen and E. Ceyani
26th Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Turkey 2018
[4] [pdf]

Industry Experience

5G VATS R&D Engineer | Turkcell Technology | Istanbul, TR
Developed deep learning algorithms for forecasting spatio-temporal grid and graph-structured mobile traffic series collected from 4G/5G base stations under non-stationary environments.

Oct 2018 - Dec 2020

Research Experience

Jan 2021 - Present University of Southern California, Prof. Salman Avestimehr
Federated learning, graph neural networks, probabilistic deep learning, information theory
June 2018 - Sept 2020 Bilkent University, Dr. Salih Ergut
Spatiotemporal Traffic Forecasting with Deep Learning Models

Honors & Awards

Deep|Bayes & PRAIRIE AI ML summer schools.

Travel & Accomodation Grant

2019
5G & Beyond Graduate Support Program and Bilkent Graduate Scholarship

Funded during M.Sc. studies by Bilkent University and ICT(Awarded for the first time).

2018 - 2020
Research Excellence Award

Awarded by Bilkent University Electrical and Electronics Engineering Department.

2018

Professional Activities

Reviewing: IEEE TNNLS, TBDCS 2019 -
Teaching Assistant at ProbAI 2021 Summer School 2021

Teaching

Neural Networks (Bilkent EEE 443-543), Grader Fall 2020
Industrial Design Project I-II (Bilkent 493-494), TA Fall 2019 - Spring 2020
Signals and Systems (Bilkent EEE-321), TA Spring 2019
Digital Signal Processing (Bilkent EEE-424), TA Fall 2018

Repositories

FedML-AI/FedML | 654 | FedML - A Research-oriented Federated Learning Library 2020
FedML-AI/FedGraphNN | 42 | FedGraphNN - A Research-oriented Federated Learning Library for Graph Neural Networks 2021
FedML-AI/SpreadGNN | 2 | SpreadGNN - Serverless Multi-Task Federated Learning for Graph Neural Networks 2021

Skills

Languages C, C++, Java, MATLAB, Julia, Python
Frameworks JAX, NumPy, Pandas, PyTorch, Pyro, SciPy, Keras
Tools Linux, vim, git, tmux, zsh

Last updated on 2021-06-14