Dr. John Burns

Telephone:  00-353-1 404 2766
Fax:  00-353-1 404 8886
Email:  john.burns@ittdublin.ie


John Burns holds a Ph.D. and B.Sc. in Computing, both from Dublin City University (DCU). After spending almost 10 years in the software industry, consulting for clients such as JP Morgan, Chase Manhattan Bank and DBS Bank (Singapore). Dr. Burns joined ITT Dublin in 2002 after completion of his Ph.D.. Dr. Burns’ current research interests include Modelling Complex Systems, Scalable Computing, Grid and Cluster Parallel Computing, and Simulation and Visualisation. Dr. Burns is an organiser and chair of Modelling Complex Systems workshop (ICCSA-2006) conference held in Glasgow (http://www.iccsa.org/).

Lectures In:
Scalable Computing & Simulation and Visualisation (MSc. In Distributed and Mobile Computing), Enterprise Applictions Architecture and Operating Systems.


Qualifications:
B.Sc. Computing (DCU), Ph.D. Computing (DCU)


Research Interests:
Recent work has been concerned with modelling the dynamics of a multi-agent cluster-based system,

with the intention of understanding how individuality among agents arises. This research led naturally to a study of emergent networks: the process whereby scale-free networks develop spontaneously from agent interaction. This research can be  extended in many ways: currently, we are looking at how dependable multi-agent networks can be developed which exhibit a topology which promotes both scalability and robustness. In fact, the principles of immune systems theory are applicable to many complex systems, and can contribute to the derivation of new algorithms which in turn can be applied to many real-world complex systems, such as population dynamics, traffic flow and financial markets' modelling.


Publications:
[1] Perrin, D., Ruskin, H., Burns, J., Crane, M.: An agent-based approach to immune modelling. Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg New York (2006)

[2] Ruskin, H., Burns, J.: Weighted networks in immune system shape space.

Physica A, The Journal of Statistical Mechanics and its Applications. 365(2):549-555, 2006

[3] Ruskin, H., Burns, J.: Network Emergence in Immune System Shape Space. Gervasi O., et al. (eds.): Lecture Notes in Computer Science, Vol. 3481. Springer-Verlag, Berlin Heidelberg New York (2005) 1254-1263

[4] Burns, J., Ruskin, H.: A Stochastic Model of the Effector T Cell Life-cycle. In: Sloot P.M.A., Chopard, B. and Hoekstra A.G., (eds.): Cellular Automata, Lecture Notes in Computer Science, Vol. 3305. Springer-Verlag, Berlin Heidelberg (2004) 454-463

[5] Burns, J., Ruskin, H.: Network Topology in Immune System Shape Space. In: Bubak, M.; Albada, G.D.v.; Sloot, P.M.A.; Dongarra, J. (eds.): Lecture Notes in Computer Science, Vol. 3038. Springer-Verlag, Berlin Heidelberg New York (2004) 1094-1101

[6] Burns, J., Ruskin, H.: Diversity Emergence and Dynamics During Primary Immune Response: A Shape Space, Physical Space Model. Theor. in Biosci. 123(2):183-194, 2004.

[7] Burns, J., Ruskin, H.: Viral Strain Diversity and Immune Response - a Computational Model. In: M.A. Hamza (ed.): Proceedings of the International Conference on Biomedical Engineering. ACTA Press (2003) 60-65

[8] Burns, J., Ruskin, H.: A Model of Immune Suppression and Repertoire Evolution. In: Sloot, P.M.A., Gorbachev, Y.E., (eds.): Lecture Notes in Computer Science, Vol. 2660. Springer-Verlag, Berlin Heidelberg New York (2003) 75-85

[9] Burns, J., Ruskin, H.: A Monte Carlo Model of Immune System T-Cell Receptor Cross-Reactivity During Primary Response. In: P.L. Garrido (ed): 7th Granada Conference in Computational and Statistical Physics. Vol. 661 (1). American Institute of Physics (2002), 255-256

[10] Burns, J. (2002): Modelling the Human Immune Response using the Shape Space Paradigm. Working Paper, CA-0402, Dublin City University