Resilient Learning in Edge Computing
Research fields: Resilence in Distributed ML systems; Distributed Decision Making.
Description: In distributed computing environments, the collaboration of nodes for predictive analytics at the network edge plays a crucial role in supporting real-time services. When a node’s service turns unavailable for various reasons (e.g., service updates, node maintenance, or even node failure), the rest available nodes could not efficiently replace its service due to different data and predictive model (e.g., Machine Learning (ML) models). This research is based on building and maintaining the systems’ resilience to node’s service unavailability/failure and avoiding interruptions to their predictive services.
Enrolment & opportunity: The successful candidate will enrol as a PhD student at the School of Computing Science under the supervision of Dr Christos Anagnostopoulos will join the KDES Group. Our lab explores several different issues such as: distributed ml, statistical learning, scalable & adaptive information processing, and data processing algorithms.
Skills: The ideal candidate will have a background in computer science and background in either mathematics and/or statistics. special areas of interest include: mathematical modelling/optimization. a good understanding of the basic machine learning and adaptation algorithms as well as an msc in one of the above areas will be a considerable plus. programming skills (python/matlab/java), good command of english and team work capacity are required.
Contact: Dr Christos Anagnostopoulos
Model Reusability at the Edge
Research fields: Model re-usability at the Edge; multi-task learning at the edge.
Description: To cope with the challenge of managing numerous computing devices, humongous data volumes and models in Internet-of-Things environments, Edge Computing (EC) has emerged to serve latency-sensitive and compute-intensive applications. Although EC paradigm significantly eliminates latency for predictive analytics tasks by deploying computation on edge nodes’ vicinity, the large scale of EC infrastructure still has huge inescapable burdens on the required resources. This research focuses on novel paradigms where edge nodes effectively reuse local completed computations (e.g., trained models) at the network edge.
Enrolment & opportunity: The successful candidate will enrol as a PhD student at the School of Computing Science under the supervision of Dr Christos Anagnostopoulos will join the KDES Group. Our lab explores several different issues such as: distributed ml, statistical learning, scalable & adaptive information processing, and data processing algorithms.
Skills: The ideal candidate will have a background in computer science and background in either mathematics and/or statistics. special areas of interest include: mathematical modelling/optimization. a good understanding of the basic machine learning and adaptation algorithms as well as an msc in one of the above areas will be a considerable plus. programming skills (python/matlab/java), good command of english and team work capacity are required.
Contact: Dr Christos Anagnostopoulos
Distributed Learning at the Edge
Research fields: Distributed ML; Federated/Local Learning at the Edge.
Description: Contact Dr Christos Anagnostopoulos for a detailed description.
Enrolment & opportunity: The successful candidate will enrol as a PhD student at the School of Computing Science under the supervision of Dr Christos Anagnostopoulos will join the KDES Group. Our lab explores several different issues such as: distributed ml, statistical learning, scalable & adaptive information processing, and data processing algorithms.
Skills: The ideal candidate will have a background in computer science and background in either mathematics and/or statistics. special areas of interest include: mathematical modelling/optimization. a good understanding of the basic machine learning and adaptation algorithms as well as an msc in one of the above areas will be a considerable plus. programming skills (python/matlab/java), good command of english and team work capacity are required.
Contact: Dr Christos Anagnostopoulos
Learn to Adapt in Distributed Computing
Research Fields: Multidimensional data streams, concept drift, distributed adaptation.
Description: The main aim of this PhD research is the intelligent management of distributed data streams. The main focus will be in the management of heterogeneous streams of dynamically changing data and the provision of intelligent analytics techniques that will build knowledge over multiple streams. The study involves the spatio-temporal aspect of the data as well as the contextual information to support solutions fully adapted to the application domain and the underlying infrastructure. Novel techniques for distribution adaptation, model inconsistency checking, distributed time series correlation and decentralized concept drift identification will be proposed and evaluated. The implementation process will adopt widely known frameworks for supporting streaming environments (e.g., Storm).
Enrolment & opportunity: The successful candidate will enrol as a PhD student at the School of Computing Science under the supervision of Dr Christos Anagnostopoulos will join the KDES Group. Our lab explores several different issues such as: distributed ml, statistical learning, scalable & adaptive information processing, and data processing algorithms.
Skills: The ideal candidate will have a background in computer science and background in either mathematics and/or statistics. special areas of interest include: mathematical modelling/optimization. a good understanding of the basic machine learning and adaptation algorithms as well as an msc in one of the above areas will be a considerable plus. programming skills (python/matlab/java), good command of english and team work capacity are required.
Contact: Dr Christos Anagnostopoulos