| 2026 | ESANN | Assessing Graph Neural Networks for latency and power consumption prediction in application mappings on multicore architectures. | Oscar Roussel, Zainab Ghrayeb, Sbastien Le Nours, Christine Sinoquet |
| 2026 | ICAART | Extending Temporal Case-Based Reasoning for Action-Conditioned Time Series Prediction from Mixed Asynchronous Data: Application to Data-Driven Medical Simulation. | Hugo Boisaubert, Lucas Vincent, Corinne Lejus-Bourdeau, Christine Sinoquet |
| 2026 | IDA | Graph Neural Networks for Graph-Level Regression on Heterogeneous Network Data: Use Case in Early-Stage Optimization of Software Mapping on Multicore Platforms. | Oscar Roussel, Zainab Ghrayeb, Sbastien Le Nours, Christine Sinoquet |
| 2025 | ESANN | Investigating four deep learning approaches as candidates for unified models in time series forecasting and event prediction: application in anesthesia training. | Quentin Victor, Ianis Clavier, Hugo Boisaubert, Fabien Picarougne, Corinne Lejus-Bourdeau, Christine Sinoquet |
| 2025 | IDA | Two-in-One Models for Event Prediction and Time Series Forecasting. Comparison of Four Deep Learning Approaches to Simulate a Digital Patient Under Anesthesia. | Quentin Victor, Ianis Clavier, Hugo Boisaubert, Fabien Picarougne, Corinne Lejus-Bourdeau, Christine Sinoquet |
| 2024 | ESANN | LSTM encoder-decoder model for contextualized time series forecasting applied to the simulation of a digital patient's physiological variables. | Julien Paris, Christine Sinoquet, Fadoua Taia-Alaoui, Corinne Lejus-Bourdeau |
| 2024 | KES | Deep joint modelling of mixed asynchronous streams - Proof of concept for data-driven simulation of a digital patient under anaesthesia. | Julien Paris, Christine Sinoquet, Fadoua Taia-Alaoui, Corinne Lejus-Bourdeau |
| 2023 | DSAA | A Framework for Context-Sensitive Prediction in Time Series - Feasibility Study for Data-Driven Simulation in Medicine. | Fatoumata Dama, Christine Sinoquet, Corinne Lejus-Bourdeau |
| 2023 | ESANN | A hidden Markov model with Hawkes process-derived contextual variables to improve time series prediction. Case study in medical simulation. | Fatoumata Dama, Christine Sinoquet, Corinne Lejus-Bourdeau |
| 2021 | ICTAI | Prediction and Inference in a Partially Hidden Markov-switching Framework with Autoregression. Application to Machinery Health Diagnosis. | Fatoumata Dama, Christine Sinoquet |
| 2018 | DSAA | Random Forest Framework Customized to Handle Highly Correlated Variables: An Extensive Experimental Study Applied to Feature Selection in Genetic Data. | Christine Sinoquet, Kamel Mekhnacha |
| 2018 | ESANN | Combining latent tree modeling with a random forest-based approach, for genetic association studies. | Christine Sinoquet, Kamel Mekhnacha |
| 2018 | ESANN | Enhancement of a stochastic Markov-blanket framework with ant colony optimization, to uncover epistasis in genetic association studies. | Christine Sinoquet, Clment Niel |
| 2018 | IDA | Random Forests with Latent Variables to Foster Feature Selection in the Context of Highly Correlated Variables. Illustration with a Bioinformatics Application. | Christine Sinoquet, Kamel Mekhnacha |
| 2006 | APBC | A Novel Approach for Structured Consensus Motif Inference Under Specificity and Quorum Constraints. | Christine Sinoquet |