Machine learning researcher/engineer with experience in computer vision, time series data analysis and forecasting, and natural language processing. My passion is deep learning. I enjoy using AI to solve society's problems and laying the foundations of next-generation learning algorithms. Concerning the recent AI literature, I am particularly interested in online learning of recurrent neural networks, deep generative modeling, and reinforcement learning. 🧑💻 🤖 I have experience in academia and have had the opportunity to explore exciting R&D problems in diverse industries (oil & gas, finance, healthcare, identity & security). Every day, I strive to learn something new by reading research articles or implementing a new idea. I have been living in Japan for more than six years before moving to the UK. 🏯 💂♂️ Please do not hesitate to contact me for any matter and let me know if I can help you. I am always happy to connect, talk, and exchange ideas with like-minded tech professionals and computer science and machine learning enthusiasts.
| Chest cine MR sequence prediction using PCA and online learning of RNNs | Deformable 3D image registration with Lucas-Kanade pyramidal optical flow |
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Prediction of dynamic sagittal MR cross-sections 6 time steps in advance using sparse 1-step approximation (left: ground-truth, right: prediction).  |
Calculation of the 3D motion of a lung tumor due to breathing using optical flow.  |
| Time series forecasting with online learning of recurrent neural networks | |
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Prediction of the 3D position of 3 markers placed on the chest 2.1s (7 time steps) in advance using decoupled neural interfaces (the sampling rate is 3.33Hz) to guide the radiation beam during lung radiotherapy.  |
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