Post-doctoral position on causal reasoning for time series data – LIG
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The “all connected” carries multiple use cases. The high availability of information systems requires efficient monitoring tools. The rise of connected objects and the increasing complexity of digital environments are calling into question the traditional tools of CIOs and technical departments.
To meet these challenges, our project is targeting a new generation of “AIOps” software, formalized by Gartner. AIOps combines AI and big data to automate problem detection and resolution. This project seeks to structurally improve problem-solving activities through the assistance of Artificial Intelligence based on causal reasoning. Indeed, the resolution of a problem very often involves the analysis of the causal chain between eclectic events that are a priori independent.
The project fits within the Grenoble Computer Science Lab (called LIG, http://www.liglab.fr/en) and the Interdisciplinary Institute in Artificial Intelligence MIAI@Grenoble Alpes (https://miai.univ-grenoble- alpes.fr/). MIAI@Grenoble Alpes is one of the four AI Institutes created by the French government to accelerate R&D, teaching and innovation in AI in France.
Datasets structured as time series are available in many applications: provided by FMRI to study brain activity, summarizing the monitoring activity to detect IT anomalies, to name just a few of applications. However, as with any machine learning study, it is important to take into account the intrinsic causal structure to improve the decision as causal relations are crucial to predict the evolution of a system. If there have been several works dedicated to inferring causal graphs between time series, few studies have been dedicated to causal reasoning and to the identification problem, which consists in computing, from data observed without any intervention, the probability of occurrences of events conditioned with variables forced to specific values.
The objectives of this post-doctoral project is to fully address the identification problem in the context of time series, and develop in this same context appropriate counterfactual reasoning procedures.
Candidates should be pursuing internationally recognized research in ML/AI, with a strong interest in causal inference and causal reasoning.
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