Dr. James Decraene
Currently with Casumo Services Ltd, Malta. linkedin

R&D objective: 

"To better understand and control complex adaptive systems", in other words, to expose the building blocks and mechanisms defining natural or artificial complex systems, then based on this knowledge to predict the system's dynamics, with the ultimate goal to best control or “optimize” the system according to predefined quantifiable objectives. Example complex adaptive systems include: human societies, cities or the stock market. Those systems are characterized by a high number of agents/factors and interdependencies. Each of the agent may adapt and change over time (e.g. through learning and various feedback loops). Hard-to-predict non-linear dynamics may emerge at the system level (e.g. riots, property bubbles, stock market crisis). 

In accordance with the above objective, I have undertaken a multi-disciplinary approach where the following scientific domains have been explored throughout my academic and industry experience:

  • Big data science and analytics: the massive quantity of data generated (e.g. by telcos, bio-sensors, stock market, webservices) provides a base understanding of the system via exploiting the various mining and statistical tools. This data and generated insights can be employed to build computational models and to cross-validate predictions.

  • Computational modeling and simulation: Data analytics is limited as it can only predict phenomena which have already been observed in the data. Computational modeling and simulation aim at reconstructing and simulating the system from the bottom-up. Those executable models can explore what-if conditions and exhibit novel phenomena.

  • High performance computing and design of experiments: The data volume and large number of simulation parameters are critical constraints limiting data analytics and large-scale simulations. High performance computing techniques (e.g. cloud) are necessary to address the computational requirements. Moreover design of experiments techniques (e.g. orthogonal sampling) can further facilitate analysis and experimentation through reducing the exploration space.

  • Evolutionary computation: Finally, advanced optimization techniques (e.g. Pareto-based multi-objective evolutionary algorithms) are necessary to generate optimal solutions, in combination with simulation techniques, and ultimately to best "control" the system given specific aims (e.g. sustainable city planning, targeted advertising, smart drugs, military operational plans).

Application domains:

My skill set has been applied to various domains, details can be found in the publication list. 

  • Telecommunication
  • Urban planning
  • System immunology
  • Military operations research
  • Evolutionary computation
  • Artificial life
Past affiliations:
  • 2013-2015: R&D Labs (with Dr. Shi Nash), Group Digital, SingTel, Singapore.
  • 2011-2013: Complex Systems Group (with Dr. Monterola and Dr. Hung), Institute of High Performance Computing, Agency for Science Technology and Research, Singapore.
  • 2009-2011: Parallel and Distributed Computing Center (with Prof. Low and Prof. Cai), School of Computer Engineering, Nanyang Technological University, Singapore.
  • 2006-2009: Artificial Life Lab  (with Prof. McMullin), The Rince Institute, School of Electronic Engineering, Dublin City University, Ireland.
  • 2005-2006: Computational Intelligence Lab (with Prof. Grogono), School of Computer Science, Concordia University, Canada.

PhD thesis: 

  • Autocatalytic Closure and the Evolution of Cellular Information Processing Networks, Dublin City University, June 2009, Dublin - Ireland. [pdf]


  • Decraene J., Shi Nash A., Method and system for generating a user activity grid table. PCT/SG2014/000135, filed March 2014 - pending.
  • Shi Nash A., Decraene J.Dang T.A.,  Knowledge model for personalization and location services. PCT/IB2014/06098, filed April 2014 - pending.
  • Decraene J., Shi Nash A., Predicting human movement behaviors using location services mode. PCT/IB2014/064554, filed September 2014 - pending.

Book chapters:

  • Zeng F., Decraene J., Low M.Y.H., Zhou S., Cai W., Diversity-driven Self-Adaptation in 

    Evolutionary Algorithms. In Intelligent Control and Computer Engineering, pages 95-106, Springer 

    2011. [link]

  • Hinze T., Decraene J., Mitchell G.G., Behre J., Schuster S., Towards a Unified Approach for the Modelling, Analysis, and Simulation of Cell Signalling Networks. In Sequence and Genome Analysis: Methods and Applications, pages 227-257,  IConcept Press, 2011. [pdf]

  • Decraene J., Mitchell G.G., McMullin B., Evolving Artificial Cell Signaling Networks: Perspectives and Methods. In Advances in Biologically Inspired Information Systems: Models, Methods, and Tools. Series Studies in Computational Intelligence, Vol. 69, pages 165-184, Springer, 2007. [pdf]

Journal papers:

  • Decraene J., Monterola C., Lee K.K.G, Hung G.G.T., Batty M., The Emergence of Urban Land Use Patterns Driven by Dispersion and Aggregation Mechanisms, Plos One, DOI: 10.1371/journal.pone.0080309, 2013. [link]
  • Decraene J., Monterola C., Lee K.K.G., Hung G.G.T., A Quantitative Procedure for the Spatial Characterization of Urban Land Use, International Journal of Modern Physics C, vol.24(1), 2013. [link]
  • Zeng F., Decraene J., Low M.Y.H., Zhou S., Cai W., Evolving Optimal and Diversified Military Operational plans for Computational Red TeamingIEEE Systems Journal special issue: Complexity in Engineering: from Complex Systems Science to Complex Systems Technology, vol. 6(3), pages, 499-509, 2012. [link]
  • Narang V., Decraene J., Wong S.Y., Aiswarya B.S., Wasem A.R., Leong S.R., Gouaillard A., Systems Immunology - A Survey of Modeling Formalisms, Applications and Simulation Tools. Immunologic Research journal, Springer, 53(1-3), pages 251-265, 2012. [link]
  • Decraene J., McMullin B., The Evolution of Complexity in Self-Maintaining Cellular Information Processing Networks. In Advances of Complex Systems, World Scientific Publishing, vol 14(1), pages 55-75, 2011. [pdf]
  • Decraene J., Hinze T., A Multidisciplinary Survey of Computational Techniques for the Modelling, Simulation and Analysis of Biochemical Networks. Journal of Universal Computer Science, vol.16(9), pages 1152-1175, 2010. [pdf]
Conference papers:
  • Manoranjan D., Kee Kiat K., Decraene J., Yap G.E., Krishnaswamy S.P.Wei W., Gomes J.B., Shi-Nash A.. Xiaoli L., CDR-To-MoVis: Developing A Mobility Visualization System From CDR Data. In Proceedings of the International Conference on Data Engineering, to appear, 2015.
  • Kanagasabai R., Veeramani A., Le Duy Ngan, Yap G.E., Decraene J., Shi-Nash A., Using Semantic Technologies to Mine Customer Insights in Telecom Industry. In Proceedings of the International Semantic Web Conference, to appear, 2014.
  • Manoranjan D., Chua G.G., Nguyen H.L., Yap G.E., Hong C., Xiaoli Li, Krishnaswamy S.P., Decraene J., Shi-Nash A., An Interactive Analytics Tool for Understanding Location Semantics and Mobility of Users Using Mobile Network Data. In Proceedings of the 15th IEEE International Conference on Mobile Data Management, IEEE Press, to appear.
  • Wei W., Wang Y., Gomes J.B., Dang T.A. Antonatos S., Mingqiang X., Peng Y., Yap G.E., Xiaoli L., Krishnaswamy S.P., Decraene J., Shi-Nash A., Oscillation Resolution for Mobile Phone Cellular Tower Data to Enable Mobility Modelling. In Proceedings of the 15th IEEE International Conference on Mobile Data Management, IEEE Press, to appear.
  • Manoranjan D., Nguyen H.L, Cao Hong, Yap G.E., Nguyen M.N, Xiaoli L., Krishnaswamy S.P., Decraene J, Antonatos S.,Wang Y., Dang T.A., Shi-Nash A., Home and Work Place Prediction for Urban Planning Using Mobile Network Data. In Proceedings of the Second International Workshop on Human Mobility Computing, 15th IEEE International Conference on Mobile Data Management, IEEE Press, to appear.
  • Decraene J., Monterola C., Lee K.K.G, Hung G.G.T., Land Use Characterization And Applications In Integrated City Planning. In Proceedings of the European Conference on Complex Systems (ECCS 2013).
  • Othman N., Decraene J., Cai W., Nan H., Low M.Y.H, Gouaillard A., Simulation-based Optimization of StarCraft Tactical AI through Evolutionary Computation, In Proceedings of the IEEE Conference on Computational Intelligence and Gaming (CIG 2012), page 394-401. IEEE Press. [link]
  • Nan H,, Decraene J., Cai W., Effective Crowd Control Through Adaptive Evolution of Agent-based Simulation Models, In Proceedings of the 44th Winter Simulation Conference (Wintersim 2012), page 1-12.  ACM Press. [link]
  • Acerbi E., Decraene J., Gouaillard A., Computational Reconstruction of Biochemical Networks, In Proceedings of the 15th International Conference on Information Fusion, IEEE press, page 1134-1141 (2012). [link]
  • Decraene J., Yong Y.C., Zeng F., Low M.Y.H, Cai W., Choo C.S., Evolutionary Design of Experiments using the MapReduce Framework. In Proceedings of Summer Simulation Conference (SummerSim 2011), pages 76-83. ACM press. [link]
  • Zeng F., Decraene J., Low M.Y.H., Cai W., Zhou S., Hingston P., High-dimensional Objective-based Data Farming. In Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2011, pages 80-87, IEEE press. [link]
  • Zeng F., Decraene J., Low M.Y.H., Cai W., Hingston P., Studies of Pareto-based Multi-objective Competitive Coevolutionary Dynamics. In Proceedings of the IEEE Congress of  Evolutionary Computation 2011, pages  2383 - 2390,  IEEE press. [link]
  • McMullin B., Decraene J., Evolution of Self-maintaining Cellular Information Processing Networks. In Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems (ECAL 2011), pages 130-132, MIT press. [pdf]
  • Decraene J., Chandramohan M., Zeng F., Low M.Y.H., Cai W., Evolving Agent-based Model Structures using Variable Length Genomes. In Proceedings of the 3rd Workshop on Optimization for Multi-agent Systems at AAMAS'11. [pdf]
  • Decraene J., Lee Y., Zeng F., Chandramohan M., Yong Y.C., Low M.Y.H., Evolutionary Design of Agent-based Simulation Experiments (demonstration). In Proceedings of the AAMAS'11, pages 1321-1322, ACM press. [pdf] [video]
  • Decraene J., Chandramohan M., Low M.Y.H., Choo. C.S., Evolvable Simulations Applied to Automated Red Teaming: A Preliminary Study. In Proceedings of the 42th Winter Simulation Conference (Wintersim 2010), pages 1444-1455, ACM Press. [pdf]
  • Decraene J., Low M.Y.H., Zeng F., Zhou S., Cai W., Automated Modeling and Analysis of Agent-based Simulations using the CASE Framework. In Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), pages  346 -351, IEEE press. [link]
  • Decraene J., Anderson M., Low M.Y.H., Maritime Counter-piracy Study using Agent-based Simulations. In Proceedings of the Spring Simulation Multiconference (SpringSim 2010), pages 82-89, ACM press. [link]
  • Decraene J., Zeng F., Low M.Y.H, Zhou S., Cai W., Research Advances in Automated Red Teaming. In Proceedings of the Spring Simulation Multiconference (SpringSim 2010), pages 145-152, ACM press. [link]
  • Zeng F., Decraene J., Low M.Y.H., Hingston P., Cai W., Zhou S., Chandramohan M., Autonomous Bee Colony Optimization for Multi-objective Function. In Proceedings of the IEEE World Congress on Computational Intelligence (WCCI 2010), pages 1-8, IEEE press. [pdf]
  • Decraene J., Mitchell G.G., McMullin B., Crosstalk and the Cooperation of Collectively Autocatalytic Reaction Networks. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2009), pages 2249-2256, IEEE press. [pdf]
  • Decraene J., Closure in Artificial Cell Signalling Networks: Investigating the Origins of Cognition in Collectively Autocatalytic Reaction Networks. In Proceedings of the Second International Conference on Bio-inspired Systems and Signal Processing (BioSignals 2009), pages 107-114, IASTED press. [pdf]
  • Decraene J., Mitchell G.G., McMullin B., Unexpected evolutionary dynamics in a string based artificial chemistry. In Proceedings of the Eleventh International Conference on Artificial Life (Alife 2008), pages 158-165, MIT Press. [pdf]
  • Decraene J., Mitchell G.G., McMullin B., Exploring Evolutionary Stability in a Concurrent Artificial Chemistry. In Proceedings of the European Conference on Complex Systems (ECCS 2008). [pdf]
  • Mitchell G.G., McMullin B., Decraene J., Kelly C. Quality time tradeoff operator for designing efficient multi level genetic algorithms. In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO 2007), pages 1527-1527, ACM press. [pdf]
  • Decraene J., Mitchell G.G., McMullin B., Studying Complex Adaptive Systems using Molecular Classifier Systems. In Proceedings of the European Conference on Complex Systems (ECCS 2007), page 174. [pdf] [poster]
  • Mitchell G.G., McMullin B., Decraene J., A Cost Benefit Operator for Efficient Multi Level Genetic Algorithm Searches. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2007), pages 1344-1350, IEEE press. [pdf]
  • Decraene J., Mitchell G.G., McMullin B., The Holland Broadcast Language and the Modeling of Biochemical Networks. In Proceedings of the 10th European Conference on Genetic Programming (EuroGP 2007) - pages 361-37, Lecture Notes in Computer Science, Vol. 4445, Springer. [pdf]
  • Decraene J., Mitchell G.G., McMullin B., Evolving artificial cell signaling networks using molecular classifier systems. In Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems (Bionetics 2006) - pages 1-8, ACM/IEEE. [pdf]
  • Decraene J., Mitchell G.G., Kelly C., McMullin B., Evolving Artificial Cell Signaling Networks. In Proceedings of the European Conference on Complex Systems (ECCS 2006) - page 94. [pdf] [poster]

Technical reports:

  • Decraene J., The Holland Broadcast Language, Research Institute for Networks and Communications Engineering, School of Electronic Engineering, Dublin City University. [pdf]


  • Modeling city growth from the ground up, article in A*STAR Research: highlighting the best of A*STAR, covering the paper "The Emergence of Urban Land Use Patterns Driven by Dispersion and Aggregation Mechanisms", July 2014. [link]
  • City dynamics yield to computer modeling, article in ScienceDaily  covering the paper "A Quantitative Procedure for the Spatial Characterization of Urban Land Use”, 11 September 2013, first appeared in A*STAR Research: highlighting the best of A*STAR - April 2013 – September 2013, page 82. [link] [link]
  • Moving down the Road, article in the Irish science magazine Science Spin about selected finalist research projects which competed at the Young European Arena of Research 2008, Towards collaborative and ubiquitous systems for optimal multi-dimensional transport planning”,  May 2009.