Areas of expertise
Computing, Simulation & Modelling
Operational Analysis and Simulation
Safety, Resilience, Risk & Reliability
Vehicle Health Management
Adam has an MSc in Computer Science from Bialystok University of Technology, Poland and a Ph.D. in Information Science from University of Pittsburgh. His past industry experience includes Intel Research and HRL Laboratories. He held academic positions at the Graduate School of Public International Affairs, University of Pittsburgh and the Robotics Institute, Carnegie Mellon University.
Dr Adam Zagorecki is a Senior Research Fellow in the Centre for Simulation and Analytics.
- Adam's research interests include probabilistic approaches such as Bayesian networks, influence diagrams, decision trees, and probabilistic risk analysis.
- He specialises in decision analysis, simulation including agent-based simulation, hybrid simulation and statistical aspects of simulation modelling.
- Application areas include fault diagnosis, prognostic modelling, modelling stability and counter-insurgency, crisis management, emergency preparedness, crisis management, and organisational efficiency.
Adam developed the FATool simulation software for projectile fragmentation and lethality modelling and recently finished the KTBox project funded by EPSRC, concerned with predictive maintenance modelling.
- Centre for Defence Enterprise (formerly the MOD's Research Acquisition Organisation)
- BAE Systems
Articles In Journals
- Zagorecki A, Lupinska-Dubicka A, Voortman M & Druzdzel MJ. (2016) Modeling women's menstrual cycles using PICI gates in Bayesian network, International Journal of Approximate Reasoning, 70 (March) 123-136.
- Zagorecki A (2015) Application of sensor fusion and data mining for prediction of methane concentration in coal mines, Mining – Informatics, Automation and Electrical Engineering, 524 (4) 33-38.
- Zagorecki A, Ristvej J, Comfort LK & Lovecek T (2012) Executive dashboard systems for emergency management, Communications -Zilina-, 14 (2) 82-89.
- Zagorecki A & Druzdzel MJ (2012) Knowledge engineering for bayesian networks: how common are noisy-MAX distributions in practice?, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 43 (1) 186-195.
- Johnson D, Zagorecki A, Gelman J M & Comfort L K (2011) Improved Situational Awareness in Emergency Management through Automated Data Analysis and Modeling, Journal of Homeland Security and Emergency Management, 8 (1).
- Ristvej J & Zagorecki A (2011) Information systems for crisis management - Current applications and future directions, Communications -Zilina-, 13 (2) 59-63.
- Zagorecki A, Ko K & Comfort L K (2010) Interorganizational information exchange and efficiency: Organizational performance in emergency environments, Journal of Artificial Societies and Social Simulation, 13 (3).
- Comfort L K, Dunn M, Johnson D, Skertich R & Zagorecki A (2004) Coordination in complex systems: Increasing efficiency in disaster mitigation and response, International Journal of Emergency Management, 2 (1-2) 62-80.
- Comfort L K, Ko K & Zagorecki A (2004) Coordination in rapidly evolving disaster response systems: The role of information, American Behavioral Scientist, 48 (3) 295-313.
- Zagorecki A (2015) Prediction of methane outbreaks in coal mines from multivariate time series using random forest. In: 15th International Conference, Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2015), Tianjin, 20-23 November 2015.
- Zagorecki Adam (2014) Feature selection for naive Bayesian network ensemble using evolutionary algorithms. In: Federated Conference on Computer Science and Information Systems (FedCSIS), 2014, Warsaw, 7-10 September 2014.
- McNaught KR, Zagorecki A & Garcia Perez A (2011) Knowledge elicitation for predictive maintenance modelling with Bayesian networks. In: 7th IMA International Conference on Modelling in Industrial Maintenance and Reliability, Cambridge, 18 April 2011.
- Zagorecki A, Hameed A & Shukla A (2011) Lethality analysis based on a fragmentation model for naturally fragmenting shells.
- McNaught KR & Zagorecki A (2010) Developing a decision analytic framework based on influence diagrams in relation to mass evacuations. In: International Conference on Emergency Preparedness (InterCEPt), Birmingham, 21 September 2010.
- McNaught KR & Zagorecki A (2009) Using dynamic Bayesian networks for prognostic modelling to inform maintenance decision making. In: IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) 2009, Hong Kong, 8-11 December 2009.
- Zagorecki A, Voortman M & Druzdzel M J (2006) Decomposing local probability distributions in bayesian networks for improved inference and parameter learning.
- Zagorecki A & Druzdzel M (2006) Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?. In: The 17th European Conference on Artificial Intelligence, Riva del Garda, 29 August - 1 September 2006.
- Zagorecki A & Druzdzel M (2004) An empirical study of probability elicitation under noisy-OR assumption. In: 17th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2004), Miami Beach, 17-19 May 2004.
- Comfort LK, Ko K & Zagorecki A (2003) Modeling information flow and fragility in rapidly evolving disaster response systems. In: 1st Structural Health Monitoring and Intelligent Infrastructure, Tokyo, 13-15 November 2003.
- Hockley CJ, Zagorecki A, Duncan A, McNaught KR & Wand K Tools for HUMS Exploitation. In: 8th DSTO International Conference on Health & Usage Monitoring (HUMS2013), Melbourne Convention Centre, 25 February 2013.
- McNaught K, zagorecki a & Zagorecki A (2011) Modelling techniques to support the adoption of predictive maintenance in the context of service provision. In: Complex Engineering Service Systems: Concepts and Research, London: Springer-Verlag, p. 277-296.
- Hockley CJ, Zagorecki AT, Lacey LJ, (2011) Enabling Support Solutions in the Defence Environment. In: Complex Engineering Service Systems Concepts and Research. Ng I, Parry G, Wild PJ, McFarlane D, Tasker P (ed.), Springer, p. 257-276.