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luigicapogrosso/README.md

Hi there, I am Luigi 👋

🖥️ I earned my Ph.D. in Artificial Intelligence at the Polytechnic of Turin in collaboration with the University of Verona under the supervision of Prof. Marco Cristani and Prof. Franco Fummi.

🎓 I received my B.Sc. (2019) and M.Sc. (2021) at the University of Verona in Computer Science and Computer Engineering for Robotics and Smart Industry, respectively.

📌 I had the privilege of being a visiting scholar at the Istituto Superior Técnico, working with Prof. Mário A. T. Figueiredo.

📚 My research interests cover the broad area of deep learning, and the main topics I am currently working on can be grouped into the classes of deep learning-enabled cyber-physical systems and representation learning.

📄 Check out all my publications on Google Scholar!

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  1. intelligolabs/MTL-Split intelligolabs/MTL-Split Public

    Official implementation of the paper "MTL-Split: Multi-Task Learning for Edge Devices using Split Computing" accepted @ DAC 2024.

    Python 8 1

  2. intelligolabs/LO-SC intelligolabs/LO-SC Public

    Official implementation of the paper "LO-SC: Local-only Split Computing for Accurate Deep Learning on Edge Devices" accepted @ VLSI Design 2025.

    Python 2

  3. intelligolabs/DIAG intelligolabs/DIAG Public

    Official implementation of the paper "Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect Detection" accepted @ CBMI 2024.

    Python 14

  4. intelligolabs/in_and_out intelligolabs/in_and_out Public

    Official implementation of the paper "Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect Detection" accepted @ VISAPP 2024.

    Python 15 4

  5. HermesBDD HermesBDD Public

    Official implementation of the paper "HermesBDD: A Multi-Core and Multi-Platform Binary Decision Diagram Package" accepted @ DDECS 2023.

    C++ 10 3

  6. split_et_impera split_et_impera Public

    Official implementation of the paper "Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications" accepted @ DDECS 2023.

    Python 6