Machine learning for meteorological time series
We are investigating machine learning methods for time series analysis in the contexts of meteorology and finance.
We are investigating machine learning methods for time series analysis in the contexts of meteorology and finance.
We aim at elucidating the algorithms used by organisms to sense and navigate turbulent environments.
Our research is motivated and inspired by biology and is nurtured by longstanding experimental collaborations in Europe and the US.
We have a longstanding interest in the fundamental properties of turbulent transport of both particles and scalar fields.
Nicola Rigolli
Gianvito Losapio
Matteo Ferrari
Andrea Guarnore
Title | Principal Investigator | Funding | Start | End | Amount |
---|---|---|---|---|---|
Physics informed algorithms for sensing and navigating turbulent environments | Agnese Seminara - Principal Investigator | EU | ERC CoG | 2021 | 2025 | 1.97 M |
Navigation with complex odor dynamics: computational principles and neural circuit implementation in mice | Agnese Seminara - co-Principal Investigator | NIH | RO1 | 2021 | 2025 | 322k |
Machine learning approaches to navigate turbulence | Agnese Seminara - Collaborator | AFOSR - Air Force Office of Scientific Research | 2020 | 2023 | 160k |
Dynamics of cell polarity establishment | Agnese Seminara - co-Principal Investigator | ANR | 2019 | 2023 | 132k |
Liguria 4P Health: Predictive, Personalised, Preventive, Participatory healthcare | Alessandro Verri - Principal Investigator | POR-FESR Regione Liguria | 2018 | 2020 | 160k |
Title | Year | Author | Venue |
---|---|---|---|
Physics informed machine learning for wind speed prediction | 2023 | D Lagomarsino-Oneto G Meanti N Pagliana A Verri A Mazzino L Rosasco A Seminara | Energy, Volume 268, 2023, 126628 |
Reproducibility in Activity Recognition Based on Wearable Devices: a Focus on Used Datasets | 2022 | A Fasciglione M Leotta A Verri | 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2022, pp. 3178-3185 |
Physics Informed Shallow Machine Learning for Wind Speed Prediction | 2022 | D Lagomarsino-Oneto G Meanti N Pagliana Verri A A Mazzino Rosasco L Seminara A | ArXiv Preprint |
A precise relationship among Buller’s drop, ballistospore, and gill morphologies enables maximum packing of spores within gilled mushrooms | 2021 | M Iapichino YW Wang S Gentry A Pringle Seminara A | Mycologia |
Epigenetic Regulation in Myelodysplastic Syndrome as A Consequence of A MicroRNA Dysregulation | 2021 | D Avenoso M Squillario Barla A Verri A E Carminati C Nurra E Todisco F Gigli F Bertolini G Gregato C Poletti M Miglino | ResearchSquare preprint |