Conferences in 2009

Research Activities

Books

In Silico Immunology Darren R. Flower, Jon Timmis, editors, in silico Immunology Overview: Immunology is an all important science, addressing as it does, the most pressing medical needs of our time: infectious disease and transplantation medicine. It has given us vaccines on the one hand and therapeutic antibodies on the other. After a century of emperical research, it is now poised to finally reinvent itself as a quantitative, genome-based science. Theoretical immunology is the application of mathematical modeling to diverse aspects of immunology ranging from T cell selection in the Thymus to the epidemiology of vaccination. Immunoinformatics, the application of computational informatics to the study of immunological macromolecules, address important questions in immunobiology and vaccinology. Artificial Immune Systems (AIS) is an area of computer science which uses ideas and concepts from immunology to guide and inspire the development of new algorithms and architectures. These three different disciplines-theoretical immunology, immunoinformatics and AIS are now poised to engineer a paradigm shift from hypothesis- to data-driven research, with new understanding emerging from the analysis of complex datasets. in silico immunology will summarize these emergent disciplines and, while focusing on cutting edge developments, will address the issue of synergy as it shows how these three are set to transform immunological science and the future of health care. See it on-line using the doi link 10.1007/978-0-387-39241-7

Leandro de Castro and Jon Timmis, Artificial Immune Systems: A New Computational Intelligence Approach Overview: Over the past few decades there has been a growing interest in the use of biology as a source of inspiration for solving computational problems. This area of research is often referred to as Biologically Inspired Computing. The motivation of this field is primarily to extract useful metaphors from natural biological systems, in order to create effective computational solutions to complex problems in a wide range of domain areas. The more notable developments have been the neural networks inspired by the working of the brain, and the evolutionary algorithms inspired by neo-Darwinian theory of evolution. More recently however, there has been a growing interest in the use of the biological immune system as a source of inspiration to the development of these computational systems. The immune system contains many useful information-processing abilities, including pattern recognition, learning, memory and inherent distributed parallel processing. For these and other reasons, the immune system has received a significant amount of interest to use as a metaphor within computing. This emerging field of research is known as Artificial Immune Systems (AIS). Essentially, AIS are the use of immune system components and processes as inspiration to construct computational systems. AIS is very much an emerging area of biologically inspired computation and has received a significant amount of interest from researchers and industrial sponsors in recent years. Applications of AIS include such areas as machine learning, fault diagnosis, computer security, scheduling, virus detection, and optimisation. The field of AIS is showing great promise of being a powerful computing paradigm and therefore the writing of this book is very timely. The book will present a general introduction to the field of immunology, stressing the key areas that are currently used within the field of AIS. A framework for engineering AIS is then introduced to the reader, followed by an up to-date review of the state of the art in AIS, in then light of that framework. It is hoped that through these initial chapters the reader will become aware of the powerful metaphor of the immune system and be left with a concrete set of ideas of how to create their own AIS. The book then goes onto describing the natural immune system in context with other biological systems and explores interaction between those systems. This will allow the reader to develop an understanding and appreciation for the richness of biology and its possible inspiration. This is then followed by a discussion of the field of AIS in relation to other computational intelligence paradigms. It is hoped that this chapter will allow the reader to become familiar with other techniques and understand the relative strengths and weaknesses of each and where the use of each (including AIS) would be appropriate.

Current Research

Most of my work crosses many areas, but in general it either falls into complex systems modelling or applying ideas from nature to engineering, with a major focus on the immune system or swarm intelligence systems. For ease, I catagorise the latter as Immuno-engineering (thus including the swarm intelligence work). There is a large amount of overlap between the modelling work and the immuno-engineering work, so drawing lines between the two might not be that helpful! Below is a list of people working with me on the various topics.

Complex Systems

Immuno-Engineering

Old Projects

Past Students/RA's

Below is a list of people, who I am pleased to say survived working with me!

Research Associates

Graduate Students

Other Collaborations

I am a Visiting Professor at the Helsinki University of Technology, Finland, in the Power Electronics Laboratory. I am also a Visiting Professor at the University of Technology, Malaysia. I work with Vipin Kumar at TPIMS, Mel Cohn at the Salk Institute, Paul Kaye and Mark Coles of the IIU here at York on various aspects of immune system modelling.

Research Opportunities

I have many possibilities for research topics centred around the area of Artificial Immune Systems and homeostatic systems. Given my joint appointment, some topics for PhD are more suitable for Computer Science and others for Electronics candidates:

• The general area of Artificial Immune Systems: applications and theory;
• Integration of natural and artificial immune, neural and endocrine systems;
• Immunological modelling;
• Immune inspired fault tolerance;
• Long-term autonomy in robotic systems;
• Software testing using bio-inspired approaches;
• Pervasive adaptive systems
• Other natural computation areas such as particle swarms, artificial life and evolutionary systems.
• If you are not sure if you are ready for a PhD quite yet, think about a taught MSc in Natural Computation. See the website for more information.

[Top]

Publications

Papers Recently Accepted

Papers Recently Submitted

Book Chapters

Conference Papers

Papers in Preperation

Book Chapters

Conference Papers

[Top]

Teaching

In Electronics

[Top]

Admin

[Top]

Misc.

For sanity, I check out the Dilbert site