Simone Casolo

Manager and industrial data scientist at Cognite. Ph.D. in theoretical chemistry, University of Milan.

I specialize in applying data science and machine learning to heavy industries such as process, energy, and chemicals, where complex and messy sensor data are the norm. I am especially interested in applying AI and machine learning to industrial processes. Also, I have been applying topological and geometrical methods for signal analysis, condition monitoring, and hybrid modeling.

Below are highlights from applied work on real assets; see the projects page for summaries and the publications page for bibliographic details.

selected publications

IND-Eval: Aligning Evaluation and Simulation for Industrial Time Series Foundation Models (NeurIPS 2026)

V. Haugland, P. Gulin, D. Foss, P. Cremer, E. Aune, Simone Casolo, A. Solbrå, and M. Mobarhan

(2026)

arXiv Bib HTML

@misc{INDEval,
  title = {IND-Eval: Aligning Evaluation and Simulation for Industrial Time Series Foundation Models (NeurIPS 2026)},
  author = {Haugland, V. and Gulin, P. and Foss, D. and Cremer, P. and Aune, E. and Casolo, Simone and Solbrå, A. and Mobarhan, M.},
  year = {2026},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
  url = {https://arxiv.org/abs/2610.16652},
}

Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health

P. Collett, A. Stasik, Simone Casolo, and S. Riemer-Sørensen

(2026)

arXiv Bib HTML

@misc{collett2026fastbayesianequipmentcondition,
  title = {Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health},
  author = {Collett, P. and Stasik, A. and Casolo, Simone and Riemer-S{\o}rensen, S.},
  year = {2026},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
  url = {https://arxiv.org/abs/2604.20735},
}

Cloud-based virtual flow metering system powered by a hybrid physics-data approach for water production monitoring in an offshore gas field

R. H. Nemoto, R. Ibarra, G. Staff, A. Akhiiartdinov, D. Brett, P. Dalby, Simone Casolo, and A. Piebalgs

Digital Chemical Engineering, 9, 100124, (2023)

DOI Bib HTML

@article{casolo2023vfm,
  title = {Cloud-based virtual flow metering system powered by a hybrid physics-data approach for water production monitoring in an offshore gas field},
  author = {Nemoto, R. H. and Ibarra, R. and Staff, G. and Akhiiartdinov, A. and Brett, D. and Dalby, P. and Casolo, Simone and Piebalgs, A.},
  journal = {Digital Chemical Engineering},
  volume = {9},
  pages = {100124},
  year = {2023},
  issn = {2772-5081},
  doi = {10.1016/j.dche.2023.100124},
}