Anna Lito Michala
Researching better systems for IoT at the University of Glasgow.
Anna Lito Michala (http://www.dcs.gla.ac.uk/~amichala/) is an Assistant Professor in Understandable Autonomous Systems at the School of Computing Science, University of Glasgow. She has expertise in safety-critical systems software engineering, system/sub-system prognostics in extreme and resource constrained environments and particularly vessel condition monitoring. She has acquired her MSc in 2008 on Biomedical Engineering at the University of Strathclyde and Bachelor degree is in Computer Science from the University of Crete where she had worked at the FORTH instituted of research at the Computer Architecture and VLSI Systems Laboratory. Her research interests are in cyber-physical systems engineering, Industrial IoT, machine learning for decision support. Funded projects include RSE/SE CH12, EPSRC AnyScale Apps EP/L000725/1, EU FP7 INCASS 605200.
Past Activities
Code BEAM America 2021
13.15 - 13.40
Machine learning, IoT and Erlang: is this the right mix for reliable federated learning?
Edge computing and has ground-breaking potential to impact in IoT. Porting Machine Learning capabilities to the IoT device is highly desirable to reduce cloud dependency. Attempts to standardise IoT stacks have not yet gained significant acceptance due to introducing the issues of large incoming data flows, trust, privacy and security, and these are prohibitive for many scenarios. New resilient IoT stacks are necessary to address the problems at the source. That is to enable federated de-centralised solutions, and to maintain accuracy, low computational and power consumption.
OBJECTIVE
Bounce the idea of the wall and get feedback from the community. Identify other projects working in this domain and explore collaborations.
AUDIENCE
Anyone working on Embedded systems using Erlang with a keen interest on supporting data driven decision support.