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She then worked at the ABN AMRO Bank as a quantitative analyst, before joining DIAL for her postdoctoral studies in multi-agent systems in manufacturing. She was later appointed as research fellow at the Complex Agent Based Dynamic Networks (CABDyN) research centre at the University of Oxford, where she studied supply chains from a complex networks perspective. Between 2012-2015 she was a University Lecturer at Cranfield University. She joined the Institute for Manufacturing in 2015.
Over the past decade Alexandra has advised policy makers, served in a number of scientific committees, and worked with Boeing, Rolls Royce, Jaguar Land Rover, Suzuki and Procter and Gamble on a variety of projects in these areas. She is a member of the All Party Parliamentary Group in Data Analytics, CASN-RA, and IEEE.
Having trained as a manufacturing systems engineer and then embarking on a research career in Artificial Intelligence, she is fascinated by the merger of the two. Her research interests include:
Development of automated and scalable optimisation and distributed decision making technologies, particularly with nature-inspired algorithms and Multi-agent Systems
Predictive Data Analytics, especially for predicting and handling uncertainty in supply chains and other emergent manufacturing systems
Identification of emergent patterns in industrial systems, particularly in relation to robustness, resilience and quality outcomes
Over the past decade Alexandra has advised policy makers, served in a number of scientific committees, and worked with Boeing, Rolls Royce, Jaguar Land Rover, Suzuki and Procter and Gamble on a variety of projects in these areas. She is a member of the All Party Parliamentary Group in Data Analytics, CASN-RA, and IEEE.
Having trained as a manufacturing systems engineer and then embarking on a research career in Artificial Intelligence, she is fascinated by the merger of the two. Her research interests include:
Development of automated and scalable optimisation and distributed decision making technologies, particularly with nature-inspired algorithms and Multi-agent Systems
Predictive Data Analytics, especially for predicting and handling uncertainty in supply chains and other emergent manufacturing systems
Identification of emergent patterns in industrial systems, particularly in relation to robustness, resilience and quality outcomes
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AAAI 2024no. 11 (2024): 12043-12051
CoRR (2024)
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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCHno. 3 (2024): 823-845
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS (2024): 109095-109095
CoRR (2024)
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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCHno. 4 (2024): 1510-1540
Supply Chain Analytics (2023): 100003-100003
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CoRRno. 2 (2023): 11076-11081
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