We are pleased to invite you to FIL Seminar that will be hold on February, 2. The venue will be announced asap.

When: February, 2, 3pm-4pm
Where: Campus de Gerland – ENS Lyon – Laboratoire LIP
3ème étage – Salle de conseil
46 All. d’Italie, 69007 Lyon

Attend online: https://ent-services.ens-lyon.fr/entVisio/quickjoin.php?hash=ae81089b2b5356fb8a20462eae5be1f0ee5357942a65796441d56497e5f2eda6&meetingID=9860

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Speaker:
Daniel Stilck França

Title:
Quantum computing in the near term: challenges and opportunities

Keywords:
quantum computing, optimization, characterization of quantum devices

Abstract:
The last years have seen remarkable progress in both the size and quality of available quantum computers, reaching the point in which they cannot be easily simulated by even the best classical computers. In spite of these achievements, current devices lack error correction and, thus, are inherently noisy. In this talk I will discuss how my research allows us to understand how to characterize the noise affecting these devices and how it constrains their computational power.

Speaker bio:
Daniel Stilck França did his PhD in mathematics at the Technical University under the supervision of Michael Wolf. The theme of his dissertation was the speed of convergence of quantum systems to equilibrium. After defending in late 2018, he joined the group of Matthias Christandl at the University of Copenhagen, where he researched in various topics in quantum information and computation. In April 2022 Daniel is joining Inria, LIP and ENS Lyon with an Inria Starting Faculty Position.

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Speaker:
Mathurin Massias

Title:
Leveraging sparsity in huge scale machine learning problems

Keywords:
optimization, machine learning, sparsity

Abstract:
In this talk, I will give an overview of my research in large scale machine learning. The evergrowing size of collected data calls for principled methods which are both resource efficient and interpretable, two tasks that sparsity jointly achieves. I will present recent improvements in sparse first order algorithms which allow to solve modern problems with millions of variables in a few seconds.

Speaker bio:
Mathurin Massias is a Chargé de Recherche in the OCKHAM team of Inria Lyon, LIP since November 2021. He obtained his PhD from Inria Saclay and Télécom ParisTech in 2019, followed by a 2 years postdoc at University of Genova. His work is in developing faster, more efficient algorithms in optimization for machine learning.