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Greg Provan

Academic

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University College Cork

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OFFICE: Wgb 1-71

TEL: +353 21 4205928

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Professor Gregory Provan is a Professor at the Computer Science Department at University College Cork. Before coming to UCC he worked for Rockwell Scientific, focusing on embedded systems for the Space Shuttle and commercial aircraft. His current research interests are in model-based diagnostics and control; large-scale systems modelling; model transformation; component-based model development; embedded systems and embedded code generation.

  • Publications
2020
Orizio R., Vuppala S., Basagiannis S., Provan G.  2020.  Towards an Explainable Approach for Insider Threat Detection: Constraint Network Learning. 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA).
2019
Provan G.  2019.  Model-Based Diagnosis with Probabilistic Models. Fault Diagnosis of Dynamic Systems: Quantitative and Qualitative Approaches. :295–318.
Orizio R, Provan G.  2019.  Identifying anomalies in Cyber Physical Systems using higher order derivatives and machine learning.
Sohege Y, Quinones M, Provan G.  2019.  Unknown Fault Tolerant Control using Deep Reinforcement Learning: A blended control approach.
2018
Orizio R, Provan G, Ranade A, Mady A.  2018.  Physics-Based Methods for Responding to Attacks and Faults . CPS-Week.
Provan G, Sohege Y.  2018.  Comparison of Control and Cooperation Frameworks for Blended Autonomy . European Control Conference.
Provan G.  2018.  Diagnosing Hybrid Dynamical Systems Using Max-Plus Algebraic Methods.
2017
Provan G.  2017.  Co-Design of Embeddable Diagnostics using Reduced-Order Models**The paper has been supported by SFI grants 12/RC/2289 and 13/RC/2094. IFAC-PapersOnLine. 50:12222-12229.
Provan G, Riccardo O.  2017.  Physics-Based Methods for Distinguishing Attacks from Faults. Tenth International Conference on Advances in Circuits, Electronics and Micro-electronics.
Provan G.  2017.  An Algebraic Approach for Diagnosing Discrete-Time Hybrid Systems. 28th International Workshop on Principles of Diagnosis (DX'17).

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