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I am a postdoc researcher at Center for Life Nano & Neuro Science (CLN2S) at Italian Institute of Technology (IIT), Rome Italy. My current research is focused on developing deep learning-based computer vision algorithms to analyze and study microfluidics experiments.

This link will take you to my Google Scholar profile. 



Machine learning - Reinforcement learning, computer vision, deep learning

HPC computing - Fortran90, Python, PyTorch, TensorFlow

Statistical physics - Active matter systems, far from equilibrium system modelling

All Publications
Durve M., Orsini S., Tiribocchi A., Montessori A., Tucny J.-M., Lauricella M., Camposeo A., Pisignano D., Succi S.
Measuring arrangement and size distributions of flowing droplets in microchannels through deep learning using DropTrack
Physics of Fluids, vol. 36, (no. 2)
Article Journal
Durve M., Orsini S., Tiribocchi A., Montessori A., Tucny J-M., Lauricella M., Camposeo A., Pisignano D., Succi S.
Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applications
The European Physical Journal E, vol. 46, (no. 32)
Succi S., Bonaccorso F., Durve M., Lauricella M., Montessori A., Tiribocchi A.
Density Functional Kinetic Theory for Soft Matter
Springer INdAM Series, vol. 51, pp. 249-260
Book Chapter Book Series
Tucny J-M., Durve M., Montessori A., Succi S.
Learning of viscosity functions in rarefied gas flows with physics-informed neural networks
Computers and Fluids, vol. 269, (no. 2024), pp. 106114
Lauricella M., Chiodo L., Bonaccorso F., Durve M., Montessori A., Tiribocchi A., Loppini A., Filippi S., Succi S.
Multiscale Hybrid Modeling of Proteins in Solvent: SARS-CoV2 Spike Protein as Test Case for Lattice Boltzmann – All Atom Molecular Dynamics Coupling
Communications in Computational Physics, vol. 33, (no. 1), pp. 57-76