Industrial Research Contributions
Industry 4.0
Reinforcement learning can play a key role in developing Industry 4.0, acting as an enabler in many central areas such as predictive maintenance, process control, and production. It can be used to analyze data from machines and equipment to identify potential issues before they occur. It can be employed to optimize production processes and increase efficiency, or supply chain and production planning. Overall, reinforcement learning has the potential to improve efficiency, reduce costs, and increase productivity.
Relevant Collaborations
E-commerce
The adoption of machine learning and online learning techniques in e-commerce see a rapid grown during the last years. Dynamic pricing and optimized advertising algorithms are now run by the majority of the platforms in order to increase their revenue. Analytical forecasting tools are adopted to optimize the strategies, analizing the long-term effects of the business choices.
Relevant Collaborations
Green Economy
Extreme climatological events are more and more frequent and they have a huge socio-economic impact. CLINT project aims to develop an artificial intelligence framework for the detection, causation and attribution of extreme events (tropical cyclones, heatwaves and warm nights, droughts, and floods). In particular, the main contribution of our group is on the design and application of new Machine Learning techniques or state-of-the-art methods able to process big spatio-temporal climate dataset to gain data-driven information on these events.
Relevant Collaborations
