• Description of WG2:

In the era of big data, a major challenge is designing adaptive behaviours in a distributed fashion, with only partial or no reliance on a centralised authority when dealing with multiple sources of information, distributed hardware and cloud computing infrastructures. This fully pertains to the instances of open source hardware and the related open data which the WG2 of this Action focuses on. In this regard, distributed machine learning is an important step to data mining within several research areas including, in the PortASAP case, large-scale chemometrics, microfluidic multi-sensor arrays, low-cost and ubiquitous wireless sensors, portable spectrometers, and so on.

• Aims or tasks:

  • Investigate and exploit the potentialities of Information and Communication Technology (ICT) applied to open source hardware and to open format for data, apps and software.
  • Organise workshops and seminars to establish connecting networks among scholars, SMEs and other stakeholders.
  • Provide up to date information on funding opportunities.
  • Propose and manage the scientific activities of STSMs.

• Deliverables:

  • Open common data formats.
  • Networking and interfacing protocols for open source hardware and software.
  • Reviews on chemometrics and data mining tools.

WG Leader: Massimo Panella (massimo.panella(at)uniroma1.it)

Department of Information Engineering, Electronics and Telecommunications, University of Rome "La Sapienza", Italy

Massimo Panella was born in Rome, Italy, in 1971. He received in 1998 the five-year Dr.Eng. degree with Honors in Electronic Engineering and in 2002 the Ph.D. degree in Information and Communication Engineering, both from the University of Rome "La Sapienza". He is currently Associate Professor at the Department of Information Engineering, Electronics and Telecommunications (DIET) of the University of Rome "La Sapienza", where he holds courses on Electrical Engineering, Machine Learning, and Computational Intelligence. Effective April 2017, he is qualified to the role of Full Professor in Electrical Engineering and, effective March 2018, he is qualified to the role of Full Professor in Computer Science. The research activities of M. Panella pertain to circuit theory, machine learning, computational intelligence and quantum computing for modelling, optimisation and control of complex systems, that is neural networks, fuzzy logic, evolutionary algorithms and quantum circuits for the solution of both supervised and unsupervised learning problems, even in distributed environments with multiple data sources as for sensor networks, pervasive systems, IoT applications. M. Panella has published more than 100 papers during his research activity, among them several papers in international journals, invited book chapters, international conference proceedings and conference abstracts. He is currently Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, Associate Editor of Journal of Computer and System Sciences (Elsevier), Subject Editor of Electronics Letters (IET) and, formerly, Associate Editor of IEEE Transactions on Fuzzy Systems. The research topics of M. Panella have laid sound foundations for the constitution of four academic spinoffs, with leadership roles for R&D in the field of ICT, multimedia, sensor networks, energy efficiency, Intelligent Transportation Systems, safety, security, e-learning, telemedicine, and e-health.

COST is supported by the EU Framework Programme Horizon 2020

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