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)

Vebjørn Haugland, Pavel Gulin, David Foss, Peet Cremer, Erlend Aune, Simone Casolo, Andreas Solbrå, and Milad Mobarhan

(2026)

arXiv Bib HTML

@misc{INDEval,
  title = {IND-Eval: Aligning Evaluation and Simulation for Industrial Time Series Foundation Models (NeurIPS 2026)},
  author = {Haugland, Vebjørn and Gulin, Pavel and Foss, David and Cremer, Peet and Aune, Erlend and Casolo, Simone and Solbrå, Andreas and Mobarhan, Milad},
  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

Peter Collett, Alexander Stasik, Simone Casolo, and Signe 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, Peter and Stasik, Alexander and Casolo, Simone and Riemer-S{\o}rensen, Signe},
  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

Rafael H. Nemoto, Roberto Ibarra, Gunnar Staff, Anvar Akhiiartdinov, Daniel Brett, Peder Dalby, Simone Casolo, and Andris 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, Rafael H. and Ibarra, Roberto and Staff, Gunnar and Akhiiartdinov, Anvar and Brett, Daniel and Dalby, Peder and Casolo, Simone and Piebalgs, Andris},
  journal = {Digital Chemical Engineering},
  volume = {9},
  pages = {100124},
  year = {2023},
  issn = {2772-5081},
  doi = {10.1016/j.dche.2023.100124},
}

Topological data analysis of slug flow in offshore wells

Simone Casolo

Digital Chemical Engineering, 4, 100045, (2022)

DOI Bib HTML PDF

@article{casolo2022slug,
  title = {Topological data analysis of slug flow in offshore wells},
  author = {Casolo, Simone},
  journal = {Digital Chemical Engineering},
  volume = {4},
  pages = {100045},
  year = {2022},
  doi = {10.1016/j.dche.2022.100045},
}