TOELT - Advanced AI Lab

TOELT is a company focused on applying advanced AI algorithms to complex and impactful research problems. We envision a new era where AI will disrupt the way we do science.

Our company is organized in two departments: research and education.

Research


In TOELT Research we support and work in large international research projects in various fields of Machine Learning applied to various fields. We have international collaborations all over Europe.

TOELT has collaborations and active projects with Spain, Belgium, Slovenia, Italy and Greece. Outside Europe we collaborate with the US and Nigera in Africa.

We work on a large variety of projects and technologies, ranging from Statistics, Machine Learning theory, medicine, sensor science, data processing and computer vision.

We have experience in various fields (click on each topic for more information):

Education


In TOELT Education we create educational material for universities and research companies in Machine Learning, Deep Learning, Statistics, Innovation and Mathematics. You can find an overview of our courses and workshops on the education page.

References

    Recent publications

    1. Michelucci, U., & Venturini, F. (2022). New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciences. ArXiv Preprint. 
      @article{michelucci2022errorsArxiv,
  title = {New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciences},
  author = {Michelucci, Umberto and Venturini, Francesca},
  journal = {arXiv preprint},
  year = {2022},
  month = oct,
  topic = {mltheory},
  abbr = {Theory},
  selected = {true}
}
    2. Venturini, F., Sperti, M., Michelucci, U., Gucciardi, A., Martos, V. M., & Deriu, M. A. (2022). Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil. Journal of Food Engineering, 336, 111198. 
      @article{venturini2023extraction,
  title = {Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil},
  author = {Venturini, Francesca and Sperti, Michela and Michelucci, Umberto and Gucciardi, Arnaud and Martos, Vanessa M. and Deriu, Marco A.},
  journal = {Journal of Food Engineering},
  volume = {336},
  pages = {111198},
  year = {2022},
  month = sep,
  publisher = {Elsevier},
  selected = {true},
  bibtex_show = {true},
  abbr = {Food},
  topic = {food}
}
    3. Milleville, K., Krishna Kumar Thirukokaranam, C., Blyau, T., Iannello, A., Michelucci, U., & Verstockt, S. (2022). Extraction and Classification of Historical Stamp Cards using Computer Vision. DH Benelux 2022: RE-MIX. 
      @inproceedings{benelux,
  title = {Extraction and Classification of Historical Stamp Cards using Computer Vision},
  author = {Milleville, Kenzo and Krishna Kumar Thirukokaranam, Chandrasekar and Blyau, Thibault and Iannello, Aurora and Michelucci, Umberto and Verstockt, Steven},
  booktitle = {DH Benelux 2022: RE-MIX},
  year = {2022},
  organization = {DH Benelux 2022},
  selected = {true},
  abbr = {Imaging}
}
    4. Michelucci, U. (2022). Applied Deep Learning with TensorFlow 2 [Book]. Springer Nature/Apress. 
      @book{michelucciapplied,
  title = {Applied Deep Learning with TensorFlow 2},
  author = {Michelucci, Umberto},
  publisher = {Springer Nature/Apress},
  year = {2022},
  abbr = {Book},
  topic = {mltheory},
  type = {Book},
  selected = {true},
  bibtex_show = {true}
}