In 2023 the team of about 20 students, supported by FBK researchers and other tutors of international level will delve into a project of AI for a widely distributed air quality assessment, exploring possible relation patterns between the pollutant agents distribution detected by diverse sensors based on different technologies, in collaboration with FBK centers Sensors and Devices (Research Unit Micro Nano Facility) and Digital Health & Wellbeing (Research Unit Data Science for Health).

Air quality assessment based on IoT sensors solutions can become a key approach for smart cities development: thus, AI-powered data science is a critical tool for the comprehensive understanding of the whole IoT sensor network. Furthermore, the use of  state-of-the art micro and nanoelectronics allows the development of low cost sensors, thus paving the way to a near future with diffused gas sensing platforms helping the decision-making process of the environmental protection agencies. In this framework, a growing number of tools are developed and tested to support diverse scenarios where a distributed air quality monitoring is needed with a particular interest in predictive systems raising red flags when signals of worsening environmental conditions are detected.

In particular, the WebValley 2023 Team will be involved in the development, implementation and validation of AI algorithms aimed at the monitoring of pollutant agents through the elaboration of datasets acquired from innovative sensors and consolidated technologies. Participants will experience the assembly of a monitoring station powered by low-cost sensors and will analyze historical air quality data to investigate possible correlations with associated weather phenomena and particular events.

In details, the Team will delve into the computational tools needed to analyze and make sense of the data, i.e. data science and machine/deep learning solutions and high-quality software collaboratively produced by the participants, after having been provided with the essential domain knowledge and effective operative, communicative, and organizational tools. Furthermore, they will acquire the basic steps for integration of diverse and heterogeneous data sources, including longitudinal time series of historical data.

Throughout the project evolution the participants will develop technical skills in data science, acquiring working experience on machine learning, including reproducibility and interpretability for AI solutions, and the basics of deploying models on the cloud. Participants will acquire basic knowledge of gas sensing as well and learn how a sensor becomes a detector experiencing the critical role of microelectronic components.

In 2022 the team of about 20 students, supported by FBK researchers and other tutors of international level will delve into a project of AI for a widely distributed air quality assessment, exploring possible relation patterns between the pollutant agents distribution detected by diverse sensors based on different technologies, in collaboration with FBK centers Sensors and Devices and Digital Health & Wellbeing (Research Unit Data Science for Health).
Air quality assessment based on IoT sensors solutions can become a key approach for smart cities development: thus, AI-powered data science is a critical tool for the comprehensive understanding of the whole IoT sensor network. Furthermore, the use of state-of-the art micro and nanoelectronics allows the development of low cost sensors, thus paving the way to a near future with diffused gas sensing platforms helping the decision-making process of the environmental agencies. In this framework, a growing number of tools are developed and tested to support diverse scenarios where a distributed air quality monitoring is needed with a particular interest in predictive systems raising red flags when signals of worsening environmental conditions are detected.
In particular, the WebValley 2022 Team will be involved in the development, implementation and validation of AI algorithms aimed at the monitoring of pollutant agents through the elaboration of datasets acquired from innovative sensors and consolidated technologies. An evaluation of the data collected by the Environmental Protection Agency on pollutants in recent years is also envisaged, to investigate the impact of the COVID-19 restrictions on air quality.
In details, the Team will delve into the computational tools needed to analyze and make sense of the data, i.e. data science and machine/deep learning solutions and high-quality software collaboratively produced by the participants, after having been provided with the essential domain knowledge and effective operative, communicative, and organizational tools.
Throughout the project evolution the participants will develop technical skills in data science, acquiring working experience on machine learning and life science methodologies, including reproducibility and interpretability for AI solutions in environmental fields, and the basics of deploying models on the cloud. Participants will acquire basic knowledge of gas sensing as well and learn how a sensor becomes a detector, experiencing the critical role of microelectronic components, whose future availability in Europe is one of the targets of the IPCEI european program.

In 2021 the team of about 20 students, supported by FBK researchers and other tutors of international level will delve into a project of AI for Healthcare and Precision Medicine, in collaboration with FBK Digital Health and Wellbeing research centre and the DSH and eHealthLab research units.

Data Science and Artificial Intelligence are pervasively spreading into the Life Science domain, opening the doors to a near future where algorithms are considered proper medical devices certified by institutional agencies. In this scenario, a growing number of prognostic tools are developed and tested to support physicians in their daily clinical tasks, with a particular interest in predictive systems raising red flags when signals of worsening conditions are detected. In particular, the WebValley 2021 Team will be involved in the development, implementation, and validation of AI algorithms aimed at detecting the early onset of comorbidities in diabetic patients starting from their healthcare trajectories collected as personal Electronic Health Record.  In details, the Team will delve into the computational tools needed to analyze and make sense of the data, i.e. data science and machine/deep learning solutions and high-quality software collaboratively produced by the participants, after having been provided with the essential domain knowledge and effective operative, communicative, and organisational tools.

Throughout the project evolution, the students will develop technical skills in data science, acquiring working experience on machine learning and life science methodologies, including reproducibility, interpretability and privacy for AI solutions in health, and the basics of deploying models on the cloud.

What are the desiderata needed to profile the top-notch data scientist for computational biology of the next generation? What blending of skills constitutes an essential portfolio?

WebValley 2020 tried to answer these questions, providing essential domain knowledge and effective operative tools. In details, the WebValley Team delved into omics biology, implementing machine and deep learning solutions through high-quality software collaboratively produced. Using the bleeding edge case study of unsupervised machine learning on single-cell sequencing, we aimed at forging a working prototype of the high-quality toolbox required to make a young researcher attractive to excellence biotech lab worldwide, thus providing a valuable training package.

The scientific theme of WebValley 2019 was the application of Deep Learning methods to the integration of biomedical imaging, omics markers and clinical data for predictive health. In particular, the project will aim at widening the use of machine learning on massive health data out of the hospital context, e.g. for surveillance and point of care of disease.

In collaboration with medical experts, the WebValley team will study how to combine phenotype data, extensive clinical exams and biomarkers from portable imaging devices. Challenges will span from biomedical to machine learning and techs aspects, from reproducibility and interpretability of AI in health to practical skills in data science, including how to run privacy-aware deep learning in cloud.