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Author Information
Jean Ogier du Terrail (Owkin)
Samy-Safwan Ayed (Université Côte d'Azur)
Edwige Cyffers (Inria)
Felix Grimberg (Swiss Federal Institute of Technology Lausanne)
Chaoyang He (FedML, Inc.)
Regis Loeb (Owkin)
I am currently employed by Owkin as a data scientist in the field of biotechnology research: we are looking to combine cutting-edge machine learning and biology to discover novel cancer drug candidates and train more accurate models on patient data in a privacy-preserving way. I have worked as a Machine Learning researcher at the Katholieke Universiteit Leuven on projects in the sectors of pharmaceutical and medical research. Beforehand I had a successful career in the financial markets industry in London: passionate about Science, Technology and their potential impact on our world, I went back to Mathematics and Artificial Intelligence to fulfill my insatiable intellectual curiosity and love for learning. This diversity of experience has made me a persevering, egoless, flexible and highly skilled professional.
Paul Mangold (Inria Lille)
Tanguy Marchand (Owkin)
Othmane Marfoq (Inria / Accenture)
Erum Mushtaq (University of Southern California)
Boris Muzellec (Owkin)
Constantin Philippenko (Ecole Polytechnique, IPParis)
Santiago Silva (INRIA)
Maria Teleńczuk (Owkin)
Shadi Albarqouni (HelmholtzAI)
Shadi Albarqouni is Senior Research Scientist at Chair for Computer Aided Medical Procedures (CAMP) at Technical University of Munich (TUM), Germany. He received his Ph.D. in Computer Science with summa cum laude in 2017. Since then, he has been working as a postdoctoral researcher at CAMP leading the Medical Image Analysis group with an emphasis on developing deep learning methods for medical applications. Albarqouni has more than 40 publications in both Medical Imaging Computing and Computer-Assisted Interventions published in IEEE TMI, MICCAI, IPCAI, IJCARS, BMVC, and ICRA. He serves as a reviewer for many journal IEEE TMI, IEEE JBHI, IJCARS and Pattern Recognition. Since 2015, he has been serving as a PC member for a couple of MICCAI workshops. Recently, he serves as an Area Chair at MICCAI 2019. His current research interests include Interpretable ML, Robustness, Uncertainty, Geometric Deep Models, and recently Federated Learning. He is also interested in Entrepreneurship and Startups for Innovative Medical Solutions. His goal is to help everyone in the world to get better healthcare services with the assistance of Informatics and computer science.
Salman Avestimehr (University of Southern California)
Aurélien Bellet (INRIA)
Aymeric Dieuleveut (Ecole Polytechnique, IPParis)
Martin Jaggi (EPFL)
Sai Praneeth Karimireddy (UC Berkeley)
Marco Lorenzi
Giovanni Neglia (Inria)
Marc Tommasi (INRIA)
Mathieu Andreux (Owkin)
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2020 Workshop: Privacy Preserving Machine Learning - PriML and PPML Joint Edition »
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2019 : Poster Session »
Eduard Gorbunov · Alexandre d'Aspremont · Lingxiao Wang · Liwei Wang · Boris Ginsburg · Alessio Quaglino · Camille Castera · Saurabh Adya · Diego Granziol · Rudrajit Das · Raghu Bollapragada · Fabian Pedregosa · Martin Takac · Majid Jahani · Sai Praneeth Karimireddy · Hilal Asi · Balint Daroczy · Leonard Adolphs · Aditya Rawal · Nicolas Brandt · Minhan Li · Giuseppe Ughi · Orlando Romero · Ivan Skorokhodov · Damien Scieur · Kiwook Bae · Konstantin Mishchenko · Rohan Anil · Vatsal Sharan · Aditya Balu · Chao Chen · Zhewei Yao · Tolga Ergen · Paul Grigas · Chris Junchi Li · Jimmy Ba · Stephen J Roberts · Sharan Vaswani · Armin Eftekhari · Chhavi Sharma -
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2019 Poster: PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization »
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2018 Workshop: Privacy Preserving Machine Learning »
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2018 : Aurélien Bellet »
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2018 Poster: COLA: Decentralized Linear Learning »
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2018 Poster: Sparsified SGD with Memory »
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2018 Poster: Training DNNs with Hybrid Block Floating Point »
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2017 Poster: Safe Adaptive Importance Sampling »
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2017 Poster: Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees »
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2017 Poster: Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication »
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2017 Poster: Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems »
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2016 Workshop: Private Multi-Party Machine Learning »
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2016 Poster: On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability »
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2015 Poster: On the Global Linear Convergence of Frank-Wolfe Optimization Variants »
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2015 Poster: SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk »
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2015 Poster: Extending Gossip Algorithms to Distributed Estimation of U-statistics »
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2015 Spotlight: Extending Gossip Algorithms to Distributed Estimation of U-statistics »
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2014 Workshop: OPT2014: Optimization for Machine Learning »
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2014 Poster: Communication-Efficient Distributed Dual Coordinate Ascent »
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2013 Workshop: Greedy Algorithms, Frank-Wolfe and Friends - A modern perspective »
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2012 Poster: Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling »
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