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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
2022
Vyhmeister E, Provan G, Doyle B, Bourke B, Castane GG, Reyes-Bozo L.  2022.  Comparison of time series and mechanistic models of vector-borne diseases. Spatial and Spatio-temporal Epidemiology. 41:100478.
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).
Sohège Y, Provan G, Quiñones-Grueiro M, Biswas G.  2020.  Deep Reinforcement Learning and Randomized Blending for Control under Novel Disturbances. IFAC-PapersOnLine. 53:8175-8180.
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.

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