TOELT - Advanced AI Lab

What is TOELT?

Our company is a leader in the field of machine learning, delivering innovative solutions to clients across a variety of industries (including universities and research groups). Our team of experts is constantly pushing the boundaries of what is possible with their research, and we are regularly publishing in top-tier, high impact journals. Our cutting-edge techniques and approaches have established us as a go-to resource for those looking to harness the power of machine learning. In addition to our cutting-edge techniques and approaches, we also have a track record of discovering new technologies and licensing them to drive further innovation in the field.

TOELT, unleashing the power of machine learning, one discovery at a time.

Why TOELT?

There are several reasons why you should choose our company for your machine learning needs:

  • Expertise: Our team of researchers and developers have a strong academic background and are constantly pushing the boundaries of what is possible with machine learning.

  • Innovation: We are at the forefront of the field, regularly publishing in high impact journals and discovering new technologies to drive further innovation.

  • Results: Our track record of delivering successful projects for clients speaks for itself. We have a proven track record of solving complex problems and driving real business results through the use of machine learning.

  • Large-scale experience: In addition to our work with individual clients, we also have a wealth of experience with large, international research projects, including those funded by the European Union and Innosuisse. This experience gives us a unique perspective and allows us to tackle even the most complex challenges.

  • Customized solutions: We work closely with our clients to understand their specific needs and goals, and develop customized solutions that meet their unique requirements.

Overall, our combination of expertise, innovation, large-scale experience, and a focus on delivering real results make us the go-to choice for organizations looking to leverage the power of machine learning.

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.

Papers in Preparation

  1. Gucciardi, A., Crotti, M., Itzhak, N. B., Mailleux, L., Ortibus, L., Michelucci, U., Groznik, V., & Sadikov, A. (2023). A new median filter application to deal with large windows of missing data in eye-gaze measurements. Proceedings, DETERMINED 2022: Neurodevelopmental Impairments in Preterm Children — Computational Advancements Conference. 
    @article{determined22-1,
  title = {A new median filter application to deal with large windows of missing data in eye-gaze measurements},
  author = {Gucciardi, Arnaud and Crotti, Monica and Itzhak, Nofar Ben and Mailleux, Lisa and Ortibus, Lisa and Michelucci, Umberto and Groznik, Vida and Sadikov, Aleksander},
  journal = {Proceedings, DETERMINED 2022: Neurodevelopmental Impairments in Preterm Children — Computational Advancements Conference},
  year = {2023},
  preparation = {true},
  topic = {medicine}
}
  2. Crotti, M., Itzhak, N., Mailleux, L., Michelucci, U., & Ortibus, E. (2023). Neural correlates of visuomotor functions in preterm children: a literature review focused on unilateral Cerebral Palsy. Proceedings, DETERMINED 2022: Neurodevelopmental Impairments in Preterm Children — Computational Advancements Conference. 
    @article{determined22-2,
  title = {Neural correlates of visuomotor functions in preterm children: a literature review focused on unilateral Cerebral Palsy},
  author = {Crotti, Monica and Itzhak, Nofar and Mailleux, Lisa and Michelucci, Umberto and Ortibus, Els},
  journal = {Proceedings, DETERMINED 2022: Neurodevelopmental Impairments in Preterm Children — Computational Advancements Conference},
  year = {2023},
  preparation = {true},
  topic = {medicine}
}
  3. Venturini, F., Sperti, M., Michelucci, U., Gucciardi, A., M. Martos, V., & Deriu, M. A. (2023). Dataset of Fluorescence Spectra and Chemical Parameters of Olive Oils. Under Review. 
    @article{michelucci-sr-spie,
  title = {Dataset of Fluorescence Spectra and Chemical Parameters of Olive Oils},
  author = {Venturini, Francesca and Sperti, Michela and Michelucci, Umberto and Gucciardi, Arnaud and M. Martos, Vanessa and Deriu, Marco A.},
  journal = {Under Review},
  year = {2023},
  preparation = {true},
  topic = {food}
}
  4. Michelucci, U., & Venturini, F. (2023). A Mathematical Framework for Machine Learning Model Generalisation: a Criticism to the Concept of Overfitting. In Preparation. 
    @article{michelucci-sr-spie1,
  title = {A Mathematical Framework for Machine Learning Model Generalisation: a Criticism to the Concept of Overfitting},
  author = {Michelucci, Umberto and Venturini, Francesca},
  journal = {In Preparation},
  year = {2023},
  preparation = {true},
  topic = {mltheory}
}
  5. Venturini, F., & Michelucci, U. (2023). Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra: application to olive oil quality assessment. To Be Presented at the Photonics SPIE Conference, January 27th-3rd February 2023. 
    @article{venturini-sr-spie2,
  title = {Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra: application to olive oil quality assessment},
  author = {Venturini, Francesca and Michelucci, Umberto},
  journal = {To be presented at the Photonics SPIE Conference, January 27th-3rd February 2023},
  year = {2023},
  preparation = {true}
}
  6. Michelucci, U., & Venturini, F. (2023). Deep learning super resolution for high-speed excitation emission matrix measurements. To Be Presented at the Photonics SPIE Conference, January 27th-3rd February 2023. 
    @article{michelucci-sr-spie3,
  title = {Deep learning super resolution for high-speed excitation emission matrix measurements},
  author = {Michelucci, Umberto and Venturini, Francesca},
  journal = {To be presented at the Photonics SPIE Conference, January 27th-3rd February 2023},
  year = {2023},
  preparation = {true},
  topic = {mltheory}
}

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}
}