I am a PhD Candidate in Computer Science at Imperial College London with Prof. Stefanos Zafeiriou and Prof. Michael Bronstein working on Geometric Deep Learning, and a Machine Learning Engineer at Cogitat.io where I work on EEG decoding.

 

18/09/2022: I have submitted my thesis and am about to graduate.

I was a Research Intern at Google AI in Machine Intelligence/Machine Perception for Fall 2018 in New York City. Before that, I interned in Quantitative Research (Systematic Trading) at JPMorgan Chase & Co in London for the Summer 2018.

I graduated with an MSc in Advanced Computing from Imperial College London, and a Diplôme d'Ingénieur in Applied Mathematics and CS from Ensimag (Grenoble), both with Distinction.

I am a Qualcomm Innovation Fellow (2019), thanks Qualcomm!

I co-organized the first edition of LOGML, an interdisciplinary summer school in geometry and machine learning in summer 2021. For more information on past and current editions, please visit https://logml.ai/.

Publications & Preprints

 

2022

Team cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition

S. Bakas, S. Ludwig, K. Barmpas, M. Bahri, Y. Panagakis, N. Laskaris, D. A. Adamos, and S. Zafeiriou

Technical Report

2022

2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets

X. Wei, A. Faisal, M. Grosse-Wentrup, A. Gramfort, S. Chevallier, V. Jayaram, C. Jeunet, S. Bakas, S. Ludwig, K. Barmpas, M. Bahri, Y. Panagakis, N. Laskaris, D. A. Adamos, S. Zafeiriou, W. C. Duong, S. M. Gordon, V. J. Lawhern, M. Sliwowski, V. Rouanne, and P. Tempczyk

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track , PMLR 176:205-219

2022

Single Shot End-to-end Road Graph Extraction

G. Bahl, M. Bahri, F. Lafarge

Earthvision Workshop at CVPR 2022

2021

Binary Graph Neural Networks

M. Bahri, G. Bahl, and S. Zafeiriou

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021


CVF OpenAccess - Video

2020

Geometrically Principled Connections in Graph Neural Networks

S. Gong*, M. Bahri*, S. Zafeiriou, and M. Bronstein

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020


*: joint first-authorship

2020

Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation

M. Bahri, E. O' Sullivan, S. Gong, F. Liu, X. Liu, M. Bronstein, and  S. Zafeiriou

International Journal of Computer Vision (IJCV)


Springer article - Video

2019

Robust Kronecker Component Analysis

M. Bahri, Y. Panagakis, and S. Zafeiriou

IEEE Transactions on Pattern Analysis and Machine Intelligence

(T-PAMI)

2017

Robust Low-Rank Tensor Modeling Using Tucker and CP Decomposition

N. Xue, G. Papamakarios, M. Bahri, Y. Panagakis, and S. Zafeiriou

European Signal Processing Conference (EUSIPCO), 2017

2017

Robust Kronecker-Decomposable Component Analysis for Low Rank Modeling

M. Bahri, Y. Panagakis, and S. Zafeiriou

IEEE/CVF International Conference on Computer Vision (ICCV), 2017

Two patent applications filed.

Education

 

2017 - (2022)

Imperial College London

PhD. Computer Science

Supervisors: Prof. Stefanos Zafeiriou & Prof. Michael Bronstein

Thesis: Advances in Efficient Geometric Deep Learning for Surface and Graph Modelling

2015 - 2016

Imperial College London

MSc. Advanced Computing

2013 - 2016

Grenoble INP - Ensimag

Dip. Ing. Applied Mathematics and Computer Science

Graduated with High Honours

2010 - 2013: Classe Préparatoire aux Grandes Écoles PC*

Selected Awards & Funding

 

2021

NeurIPS 2021 BEETL Competition: EEG Transfer Learning

First place with team Cogitat.

2019

Amazon AWS Cloud Credits for Research

Awarded on proposal submission.
$6,000 - one year

2019

Qualcomm Innovation Fellowship

Awarded on proposal submission and interviews.
$40,000 - one year

2017

Department of Computing PhD Scholarship

Tuition fees and stipend.

2016

Winton Capital Applied Computing MSc Project Prize

Awarded for the best MSc thesis in Computer Science at Imperial College London (1/188 students).

Community Service

 
Co-Organizer - LOGML

London Geometry and Machine Learning Summer-School (LOGML.ai)

Dates: July 12-16, 2021

Week-long interdisciplinary summer school for early-career researchers in mathematics or computer science working at the intersection of geometry and machine learning. The summer school will offer talks and group projects from prominent researchers from academia and industry, as well as a company evening.

Reviewer

IEEE T-PAMI, IEEE T-SMC-Systems, IEEE TIP, IJCV

Professional Experience

 

June 2021 - Present

Cogitat.io

Machine Learning Engineer

Research and Engineering on EEG decoding for brain-computer interfaces. Applications in healthcare and entertainment.

October 2018 - January 2019

Google AI

Research Intern

Machine Perception

New York, NY

June 2018 - August 2018

JPMorgan Chase & Co.

Quantitative Associate Intern

Equities Systematic Trading QR

London, UK

April 2017 - July 2017

Speechmatics

Speech Recognition Intern

Research & Development on RNN language models

Cambridge, UK

September 2016 - March 2017

HarperCollins Publishers

Data Scientist

Graph modeling for digital marketing

London, UK

June 2015 - September 2015

Morgan Stanley

Summer Analyst

Technology & Data

London, UK