Prof Jon Timmis Department of Computer Science and Department of Electronics University of York Heslington, York. YO10 5DD Tel +44 (0) 1904 322318/325361 Email: jon (dot) timmis (at) york (dot) ac (dot) uk

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I am Professor of Natural Computation and hold a joint appointment between the Department of Computer Science and the Department of Electronics . I am a member of the Non Standard Computation group, the Intelligent Systems group and the York Centre for Complex Systems Analysis (YCCSA). I am also a member of the York Computational Immunology Lab

I am running a new MSc in Autonomous Robotics Engineering, starting in 2013.

Research

My work cuts across many areas, but most of my work revolves around immunology, either developing computational models of immune function (computational immunology), or fault-tolerance achieved via bio-inspired engineering with a focus on the immune system (immuno-engineering). In terms of applications, I focus mainly on swarm robotic systems as a platform for testing our ideas realting to fault tolerence and anomaly detection. My wider interests are in modelling and simulation of complex systems.

Take a read of the Computational Immunology Lab website for more information on our work in that area.

I currently hold a Royal Society Wolfson Research Merit Award where my work is focussed on computational immunology and self-healing swarm robotic systems. I work on a variety of robotic platforms, we made a video for some fun, of our flying robots (make sure the volume is turned up).

I am Visiting Professor at the Universiti Kebangsaan Malaysia.

A few years ago, Nature did a piece on AIS, take a read

If you want to know more about AIS, I have written a few papers that you might find interesting all with pre-final version PDFs provided for you (you can find the full reference for citation purposes on my publication page):

Jobs and Studentships

Research Activities

Current Research Council Projects

Current Industrial Funding

I hold a grant from DSTL on chemical agent detection deployed on robotic platforms.

I hold a grant from Syngenta to develop a decision support tool for farmers to optimise bird life diversity on their land.

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

My work cuts across many areas, but most of my work revolves around immunology, either developing computational models of immune function (computational immunology), or fault-tolerance achieved via bio-inspired engineering with a focus on the immune system (immuno-engineering) In terms of applications, I focus mainly on swarm robotic systems as a platform for testing our ideas. My wider interests are in modelling and simulation of complex systems.

Below is a current list of people, that in one way or another, work with me.

Computational Immunology

Complex Systems

Bio-Inspired Engineering

PhD students writing up

Submitted

Past Students/RA's

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

Research Associates

Graduate Research Students

1. Dr. Kieran Alden. Modelling and Simulation of lyphoid tissue organogenesis. (Biology). 2012.
2. Dr. Antonio Zamorano. Complex Systems Simulation, design and implementation using FPGAs. (Electronics). 2012.
3. Dr.Ran Bi. Immune-inspired fault diagnosis for a robotic system. (Electronics) 2012.
4. Dr. Lau Hui Keng (Kelvin). Error Detection in Swarm Robotics: A Focus on Adaptivity to Dynamic Environments. (Computer Science) 2012.
5. Dr. Ameila Ismail (Rita). Immune-inspired Self Healing in Swarm Robotic Systems. (Computer Science) 2012.
6. Dr. Omer Qadir. Protien Processor Associate Memory. (Electronics) 2011.
7. Dr Mark Read. Statistical and Modelling Techniques to Build Confidence in the Investigation of Immunology through Agent-Based Simulation. (Computer Science). 2011.
8. Dr. Richard Greaves. Computational Modelling of Treg Networks in Experimental Autoimmune Encephalomyelitis. (2011) (MSc by Research)
9. Dr Nick Owens. From Biology to Algorithms. (Electronics). 2010.
10. Dr Luca Albergante (Milan). A Petri Net Model of Liver Response to Visceral Leishmaniasis: self-regulation and complex interplay in the vertebrate immune system. (Mathematics) 2010.
11. Dr Adam Knowles Immunologically Inspired Data Fusion for Anomaly Detection in Electromechanical Systems PhD. (Electronics). 2010. Thesis commercially sensitve and is unavailable
12. Dr Yang Liu A Neuro-Immune Inspired Computational Framework and its Applications to a Mchine Visual Tracking System PhD. (Electronics) 2010. PDF of thesis
13. Dr Ed Clark A Framework for modelling stochastic optimisation algorithms with Markov chains. PhD. (Electronics) 2009. PDF of thesis
14. Dr Paul AndrewsAn Investigation oor the Development of Artificial Immune Systems: A Case-Study in Immune Receptor Degeneracy PhD. (Computer Science) 2009. PDF of thesis
15. Dr. Peter May. An Artficial Immune System Approach to Mutation Testing Test Data Generation PhD. (Computer Science) 2006.
16. Dr. Andy Secker. Artificial Immune Systems for Web Content Mining: Focusing on the Discovery of Interesting Information PhD. (Computer Science) 2006. Thesis .
17. Dr. Modupe Ayara. An Immune Inspired Approach For Adaptable Error Detection in Embedded Systems PhD. (Computer Science) 2005.
18. Dr. Andrew Watkins. Exploiting Immunological Metaphors in the Development of Serial, Parallel and Distributed Learning Algorithms PhD. (Computer Science) 2005.PDF of thesis
19. Dr. Tom Knight. MARIA: A Multilayered Unsupervised Machine Learning Algorithm Based on the Vertebrate Immune System PhD. (Computer Science) 2005.download PDF of thesis
20. Dr Giuseppe Nicosia (Catania, Italy). Immune Algorithms for Optimisation and Protein Structure Prediction, (Mathematics and Computer Science) 2004.
21. Johnny Kelsey. An Immune Inspired Algorithm for Function Optimisation . MSc by Research (Computer Science). 2004.

Other Collaborations

I was a Visiting Professor at the Helsinki University of Technology, Finland, in the Power Electronics Laboratory. I was also a Visiting Professor at the University of Technology, Malaysia. I work with Vipin Kumar at TPIMS, Paul Kaye and Mark Coles of the CII here at York and Prof Eva Qwarnstrom at Sheffield 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;
• 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.

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Publications

For a Google powered list (which is very up to date) then see my Google Scholar page .

Papers Recently Submitted

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Teaching

In Electronics for 2012/13 I am not teaching in the department, excepting I have one MEng student, Sophie Alexander, working on underwater swarm robotics.

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Admin

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Misc.

For sanity, I check out the Dilbert site