About Me
- Name: Charles Assaad
- Address: Paris, France
I am curretly a Junior Professor & Inserm Chair at the Pierre Louis Institute of Epidemiology and Public Health (IPLESP) - Inserm and Sorbonne Université. I am leading the CIPHOD team which is specialized in causal inference in public health using large observational health databases.
My work focuses mainly on developing new theories and methodologies to assist epidemiologists in uncovering and estimating causal effects from observational temporal data, as well as identifying root causes of anoamlies, especially in the presence of high-level background knowledge (abstractions). It's an exciting research area with lots of open questions! More generally here's a non exaustive list of topics that I am interested about:
- Causal Discovery
- Causal Reasoning
- Root Cause Analysis
- History of Causation
- Machine Learning
- Time Series Analysis
- Information Theory
Applications:
- Epidemiology
- IT monitoring
- Ecology
Publications
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S. Ferreira and C. K. Assaad. Identifiability of Direct Effects from Summary Causal Graphs. the Thirty-Eighth AAAI Conference on Artificial Intelligence. 2024. Soon
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C. K. Assaad , I. Ez-zejjari and L. Zan. Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops. The 26th International Conference on Artificial Intelligence and Statistics. 2023. Link
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C. K. Assaad , E. Devijver and E. Gaussier. Survey and Evaluation of Causal Discovery Methods for Time Series (Extended Abstract). The 32nd International Joint Conference on Artificial Intelligence. 2023. Link
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A. Aït-Bachir, C. K. Assaad, C. de Bignicourt, E. Devijver, S. Ferreira, E. Gaussier, H. Mohanna and L. Zan. Case Studies of Causal Discovery from IT Monitoring Time Series. The workshop on The History and Development of Search Methods for Causal Structure at UAI. 2023. Link
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L. Zan, A. Meynaoui, C. K. Assaad , E. Devijver and E. Gaussier. A Conditional Mutual Information Estimator for Mixed Data and an Associated Conditional Independence Test. Entropy. 2022. Link
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C. K. Assaad , E. Devijver and E. Gaussier. Entropy-based Discovery of Summary Causal Graphs in Time Series. Entropy. 2022. Link
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C. K. Assaad , E. Devijver and E. Gaussier. Causal Discovery of Extended Summary Graphs in Time Series. The 38th Conference on Uncertainty in Artificial Intelligence. 2022. Link
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C. K. Assaad , E. Devijver and E. Gaussier. Survey and Evaluation of Causal Discovery Methods for Time Series. Journal of Artificial Intelligence Research. 2022. Link
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C. K. Assaad. Causal Discovery between time series. Artificial Intelligence [cs.AI]. Université Grenoble Alpes. 2021. Link
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C. K. Assaad, E. Devijver, E. Gaussier and A. Ait-Bachir. A Mixed Noise and Constraint-Based Approach to Causal Inference in Time Series. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Research Track. Springer, Cham. 2021. Link
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C. K. Assaad, E. Devijver, E. Gaussier and A. Ait-Bachir. Scaling Causal Inference in Additive Noise Models. Proceedings of the 2019 ACM SIGKDD Workshop on Causal Discovery (KDD). PMLR 104: 22-33. 2019. Link
Teaching
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(2022-2023) "Causality: inferring and reasoning with causal relations" (Master 2, ENS Lyon)
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(2022-2023) "Causality: inferring and reasoning with causal relations" (Master 2 MSIAM, UGA)
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(2021) Introduction to causality in the course "Foundations of Data Science" (Master 1 MOSIG, UGA)
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(2021) Introduction to causality in the course "Introduction à l’intelligence artificielle et la science des données" (Master 1 informatique, UGA)
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(2021) Trainning sessions about causal inference (Formation, EasyVista)
(2020) TD sessions on "Introduction à l’intelligence artificielle et la science des données" (Master 1 informatique, UGA)
(2019) TD sessions on "Algorithmique en Python" (Prépa 2, Grenoble-INP)
Projects
(2023-2028) PEPR Project: CAUSALIty Teams up with Artificial Intelligence (CAUSALI-T-AI). This project aims to address various challenges in causal modeling using advanced machine learning techniques. It focuses on four main directions: Stable causal modeling; Learning causal representations; Domain adaptation; and Causal learning in uncertain environments. Link
(2021-2023) R&D Booster Project: Causal System for Problem Resolution (CSPR). This project aims to develop an intelligent engine for monitoring systems that uses a known causal graph as well as the do-calculus to transform whenever it is possible a causal question into a statistical expression that can be directly estimated from observational data.
People
To check new open positions click here
Clement Yvernes (2024), Internship at LIG, co-advised with Emilie Devijver (CNRS) and Eric Gaussier (UGA)
Flora Helmers (2024), Internship at LIG, co-advised with Maxime Peyrard (CNRS), Emilie Devijver (CNRS) and Eric Gaussier (UGA)
Simon Ferreira (2023), Internship at EasyVista
Imad Ez-zejjari (2022), Internship at EasyVista, co-advised with Lei Zan (EasyVista)
Anouar Meynaoui (2021-2022), Posdoc at LIG financed by the CSPR R&D project, co-advised with Emilie Devijver (CNRS), Eric Gaussier (UGA) and Gregor Goessler (INRIA)
Lei Zan (2021-present), CIFRE PhD thesis at EasyVista, co-advised with Emilie Devijver (CNRS) and Eric Gaussier (UGA)
Resume
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2014-present
Junior Professor & Inserm Chair
Pierre Louis Institute of Epidemiology and Public Health (IPLESP) - Inserm and Sorbonne Université, Paris, FranceLeading the CIPHOD team which is specialized in causal inference in public health using large observational health databases.
2021-20242018-2021PhD
University of Grenoble Alpes, Grenoble, FranceCausal discovery between time series.
2017-20214/2017-9/20174/2017-5/20172016-20176/2016-9/20162014-20172011-2014