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.
@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
@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
@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},}