machine learning

MCLab: Model Checking Lab

MCLab: Model Checking Lab

Our primary research activity focuses on AI and Model Checking based algorithms and tools for the automatic design and verification of mission or safety-critical systems with an emphasis on intelligent or autonomous systems.

 

URL: http://mclab.di.uniroma1.it

MCLab: Model Checking Lab

MCLab: Model Checking Lab

Our primary research activity focuses on AI and Model Checking based algorithms and tools for the automatic design and verification of mission or safety-critical systems with an emphasis on intelligent or autonomous systems.

 

URL: http://mclab.di.uniroma1.it

MCLab: Model Checking Lab

MCLab: Model Checking Lab

Our primary research activity focuses on AI and Model Checking based algorithms and tools for the automatic design and verification of mission or safety-critical systems with an emphasis on intelligent or autonomous systems.

 

URL: http://mclab.di.uniroma1.it

MCLab: Model Checking Lab

MCLab: Model Checking Lab

Our primary research activity focuses on AI and Model Checking based algorithms and tools for the automatic design and verification of mission or safety-critical systems with an emphasis on intelligent or autonomous systems.

 

URL: http://mclab.di.uniroma1.it

MCLab: Model Checking Lab

MCLab: Model Checking Lab

Our primary research activity focuses on AI and Model Checking based algorithms and tools for the automatic design and verification of mission or safety-critical systems with an emphasis on intelligent or autonomous systems.

 

URL: http://mclab.di.uniroma1.it

Computational Atomistic Fluid-dynamics & Engineering

Computational Atomistic Fluid-dynamics & Engineering

Our approach is a physical one, addressing problems in engineering and biology. In particular, we use molecular dynamics and multiscale simulations which address the various time and length scales typical of wetting, cavitation, and biophysical phenomena.

Neurologia sperimentale, neuroingegneria e telemedicina

Neurologia sperimentale, neuroingegneria e telemedicina

L'attività sperimentale del gruppo di ricerca coordinato dal Prof. Antonio Suppa si articola in 3 principali linee di ricerca:
- Sviluppo e applicazione di metodiche avanzate di neuromodulazione non invasiva (ad es. stimolazione magnetica transcranica- TMS ecc) al fine di studiare sperimentalmente e in modo non invasivo i meccanismi di plasticità sinaptica nelle aree motorie corticali in soggetti sani (fisiologia del sistema motorio) e in pazienti affetti da Malattia di Parkinson e altri disordini del movimento (fisiopatologia di specifici segni e sintomi motori).

A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach.

Application of Machine Learning to Mortality Modeling and Forecasting

Estimation of future mortality rates still plays a central role among life insurers in
pricing their products and managing longevity risk. In the literature on mortality modeling, a wide
number of stochastic models have been proposed, most of them forecasting future mortality
rates by extrapolating one or more latent factors. The abundance of proposed models shows that
forecasting future mortality from historical trends is non-trivial. Following the idea proposed in

Longevity risk management through Machine Learning: state of the art

Longevity risk management is an area of the life insurance business where the use of
Artificial Intelligence is still underdeveloped. The paper retraces the main results of the
recent actuarial literature on the topic to draw attention to the potential of Machine
Learning in predicting mortality and consequently improving the longevity risk quantification
and management, with practical implication on the pricing of life products
with long-term duration and lifelong guaranteed options embedded in pension contracts

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