A new Conference papers has been accepted A Novel Preprocessing-Driven Approach to Remaining Useful Life (RUL) Prediction Using Temporal Convolutional Networks (TCN) I will attend the 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI) to present it.

A new Conference papers has been accepted Write on Paper and Get the Online Digital Trace: A New Era for Handwriting. I will attend the 22nd Conference of the International Graphonomics Society (IGS 2025) to present it.

I have joined Luleå University of Technology (Sweden) as a Postdoctoral Researcher in the Machine Learning team, where I am engaged in research on suitable machine learning techniques.

A new article in aninternational peer-reviewed journal has been accepted: Mixture-of-experts for handwriting trajectory reconstruction from IMU sensors.

‘Design of a deep neural network architecture dedicated to handwriting synthesis from kinematic sensors of a digital pen’. It was presented to the following jury:
• Reporter:
  • Andreas Fischer - Full professor - University of Applied Sciences and Arts Western Switzerland (HES-SO)
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  • Clément Chatelain - Maître de conférences HDR, INSA Rouen Normandie​
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• Examiner:
  • Elisa Fromont - Professeur des universités, Université de Rennes​
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  • Nicolas Ragot - Maître de conférences HDR, Polytech Tours​
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• Thesis supervisor:
  • Eric Anquetil Professeur des universités, INSA Rennes​
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  • Romain Tavenard Professeur des universités, Université Rennes 2​
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  • Yann Soullard Maître de conférences, Université Rennes 2
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• Abstract: This thesis focuses on a digital pen equipped with kinematic sensors, and its aim is to reconstruct the in-line trace of handwriting. We introduce a new processing pipeline that associates pen sensor signals with the corresponding writing trajectory. Based on Dynamic Time Warping to align the signals and an architecture inspired by Temporal Convolutional Networks Additionally, we present a Mixture-Of-Experts (MOE) approach to enhance the focus and understanding of each aspect of handwriting, comprising a touching expert model for pencil touches and a pen-up expert model for pen trajectories. A significant challenge is the variation in captured signals between adults and children, due to differences in speed and confidence in handwriting gestures. We address this through a domain adaptation approach. Furthermore, we introduce a new public benchmark dataset to support future research and comparisons in the field of handwriting reconstruction.

A new Conference papers has been accepted Domain adaptation for handwriting trajectory reconstruction from IMU sensors. I will attend International Conference on Document Analysis and Recognition (ICDAR)to present it.

Two new technical papers have been accepted for the KIHT project :
• KIHT: Kaligo-based Intelligent Handwriting Teacher.
• Towards the on-device Handwriting Trajectory Reconstruction of the Sensor Enhanced Pen.

I attended the International Conference on Document Analysis and Recognition (ICDAR) to present our paper Online handwriting trajectory reconstruction from kinematic sensors using temporal convolutional network.