Lisheng Sun-Hosoya
Datalab Groupe, Crédit Agricole S.A.
12 Place des États Unis
92120 Montrouge France
Email: cecile829@gmail.com
*I am a Postdoctoral Researcher at LISN, Université Paris-Saclay, since December 2022, with responsibilities in both research and teaching (chargée de recherche et d’enseignement). My current research focuses on AutoML, Meta-Learning, and AI fairness.
Prior to my postdoc, I was a Senior Data Scientist and Research Lead at Crédit Agricole S.A., where I specialized in AutoML, Explainable AI (XAI), and AI fairness for the banking sector.
I earned my Ph.D. in Machine Learning in December 2019 under the supervision of Prof. Isabelle Guyon and Prof. Michèle Sebag, focusing on AutoML and Meta-Learning.
[Senior Data scientist, Crédit Agricole S.A., France]{}. Design and implement innovant AI pipelines in banking systems, including Financial indicators generation and Achitecture design where AutoML and Meta-Learning are used to improve learning efficiency; Also heavy Contributions in R&D project management (Green AI, Explainable AI, Reinforcement Learning, etc.), internship supervisions and scientific communication with region branches.
[Software engineer]{}. Collaboration with Google Zurich (, project manager) on prepararing the AutoDL challenge, Zurich, Switzerland and ChaLearn, California, USA. Contributions to the design and implementation of an AutoDL starting kit using [Tensorflow]{} [Github].
[Software engineer]{}. See4C EU project, Forecasting the French electricity power grid (, supervisor). Université Aix-Marseille, France. Contributions to the design and implementation of a starting kit in Python to treat problems of spatio-temporal time series [Github].
[Software engineer]{}. Codalab Worksheets. Prototype use cases of worksheets in competitions organized on the Codalab platform (, project manager). Implement five worksheets to boost collaboration and use of re-usable workflows, including the BeatAutoSklearn hackathon, run at NeurIPS 2016 challenges in machine learning workshop.[dockerhub]
[Freelance Software engineer]{}. Japan.
[Research projet engineer]{} in Astrophysics. Observatoire de Paris, France.
: and .
: Meta-Learning as a Markov Decision Process.
The success of Machine Learning applications in many fields relies heavily on well designed models / architectures, which is not only time consuming, but also requires domain knowledge. My PhD thesis aims at developing a methodology to automate the process of Machine Learning (AutoML). We dig into the meta-learning aspect of AutoML and formulate the problem as a Markov Decision Process (MDP). We also provide different approaches (collaborative filtering, reinforcement learning) to solve it.
: The physical properties of local galaxies with high specific star formation rate
: Prof. Matthew Lehnert and Prof. Wim van Driel, Institut d’Astrophysique de Paris, Paris, France.
Manh Hung Nguyen, Nathan Grinsztajn, Isabelle Guyon, and . MetaREVEAL:RL-based Meta-learning from Learning Curves.
. Meta-Learning as a Markov Decision Process. Machine Learning [cs.LG]. Université Paris Saclay (COmUE), 2019. (Ph.D thesis)
, Isabelle Guyon, and Michèle Sebag. Lessons learned from the AutoML challenge. (Conférence sur l’Apprentissage Automatique CAP2018, Oral).
, Isabelle Guyon, and Michèle Sebag. ActivMetal: Algorithm recommendation with Active Meta Learning. (IAL 2018 workshop, ECML PKDD, 2018, Oral)
Isabelle Guyon, , Marc Boullé, Hugo Jair Escalante, Sergio Escalera, Zhengying Liu, Damir Jajetic, Bisakha Ray, Mehreen Saeed, Michèle Sebag, et al. Analysis of the AutoML challenge series 2015–2018. In AutoML: methods, systems, challenges, Frank Hutter, Lars Kotthoff, Joaquin Vanschorenpages eds, 177–219. Springer, 2019.
Zhengying Liu, Olivier Bousquet, André Elisseeff, Sergio Escalera, Isabelle Guyon,Julio Jacques, Adrien Pavao, Danny Silver, , Sébastien Treguer, et al. AutoDL challenge design and beta tests. Towards automatic deep learning. NeurIPS 2018 workshop on Automated machine learning: methods, systems, challenges, Montréal, Canada, 2018.
Jorge Madrid, Hugo Jair Escalante, Eduardo Morales, Wei-Wei Tu, Yang Yu, , Isabelle Guyon, and Michèle Sebag. Towards AutoML in the presence of drift: first results. Workshop AutoML 2018 @ ICML/IJCAI-ECAI, Stockholm, Sweden, 2018.
Chiheb Eddine NAJJAR, École des Ponts ParisTech, master’s thesis “Reinforcement Learning For Lead Qualification”, Crédit Agricole S.A., 2021.
Manh Hung Nguyen, CentraleSupélec, master’s thesis “RL-based Meta-learning from Learning Curves”, Université Paris Sud, 2021. Co-supervised with Prof. Isabelle Guyon.
Achraf Azize, École Polytechnique, master’s thesis “Interpretability and AutoML”, Crédit Agricole S.A., 2020.
Nathan Grinsztajn, École Polytechnique, master’s thesis “AutoML and REVEAL games”, Université Paris Sud, 2019. Co-supervised with Prof. Isabelle Guyon.
[Volunteer Teaching Assistant]{}. Advanced Optimization and Automated Machine Learning taught by , Université Paris Sud, Gif-sur-Yvette, France. I taught the Chapter [Hyper-parameter optimization]{} (application of black box methods (wrapper) e.g. SMAC, embedded methods, regularization paths).
[Volunteer Teaching Assistant]{}. Advanced Optimization and Automated Machine Learning taught by , Université Paris Sud, Gif-sur-Yvette, France. I taught the Chapter [Hyper-parameter optimization]{} (application of black box methods (wrapper) e.g. SMAC, embedded methods, regularization paths).
[Teaching Assistant]{}Deep Learning in Practice. Mini-projets on Machine learning taught by Prof. Guillaume Charpia, Université Paris Sud, Gif-sur-Yvette, France. I taught the Chapter [AutoML and AutoDL]{}.
International Workshop on Machine Learning and Artificial Intelligence, Machine Learning, Human Learning and Robotics Program, Télécom Paris. Meta-Learning as a Markov Decision Process. Video. (Invited)
Google Zurich. AutoML1 and its post challenge analysis. (Invited)
AutoML 2015 workshop @ ICML 2015. Solution to the AutoML challenge. (Invited as the challenge’s 3rd place winner)
NIPS2018, 2019, 2020
AutoML@ICML2020, 2021
MetaLearn@AAAI2021
IJCNN 2020
JMLR
I put some of my work online for public use.
Github: https://github.com/LishengSun
Research and personal projects.
Docker: https://hub.docker.com/
I developed several docker images for various projects, for example,
this one
allows running AutoML challenge participant codes in a reproducible
and controllable way.
**: Prof. Univ. Paris-Sud, President ChaLearn, guyon@chalearn.com.
**: Senior Director, Global Research Engagement at Microsoft Research, evelynev@microsoft.com.
**: Software Engineering Manager at Google, elisseeff@google.com
**: Prof. Univ. Aix-Marseille, Cecile.Capponi@lif.univ-mrs.fr.
**: Directrice de recherche CNRS, sebag@lri.fr.
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