User:Thomas E. Gorochowski
- Thomas E. Gorochowski
- Bristol Centre for Complexity Sciences, University of Bristol, Bristol, BS8 1TR, UK
- Personal Website
I am currently a student in the Bristol Centre for Complexity Sciences under the supervision of Prof Mario di Bernardo and Prof Claire S. Grierson. My background is in Computer Science where I gained a particular interest in programming language design and semantics, novel data visualisation techniques and parallel programming architectures. Since then I have moved into the new field of Complexity Sciences and become excited about the bringing together of non-linear dynamics, evolutionary design, complex networks, synthetic biology, and the idea of emergence to try and understand the fascinating abyss that is Nature. When I am not wrestling with all things complex, you are likely to find me running around a badminton court, day dreaming in a field, or down the river for a spot of fly fishing.
- Started 2009, Ph.D., University of Bristol.
- Dynamics of Modular Evolving Complex Networks: Analysis, Control and Applications
- 2008, M.Res. Complexity Sciences, University of Bristol.
- Bacto-Builders - Micro-scale construction by bacteria, a modelling perspective
- Cross-frequency coupling of neuronal oscillations during cognition
- 2004, M.Eng. Computer Science, University of Warwick.
- Vidi - Visualisation of fluidic systems using OpenGL
- GAVToolkit - Graph algorithm visualisation
- The Fundamental Building Blocks of Complex Systems - Complex systems comprise of huge numbers of interconnected parts which current methods find difficult pull apart and analyse. Is there another way of looking at these systems that abstracts away the miniscule details and forms higher level representations at which new fundamental laws can be derived? When you look at Nature and the huge numbers of complex living systems one thing always stands out - a modular/hierarchical structure. It is my belief that although the interactions between elements may change with setting, fundamental building blocks exist that permit the predictable creation of large scale systems. My main area of interest is in attempting to find the building blocks chosen by Nature and developing methods to allow for the creation of complex systems with prescribed behaviours - "complexity engineering".
- Synthetic Biology as a Tool to Understand Complex Systems - Synthetic biology is a new area of research that attempts to combine ideas from engineering and biology with the aim of allowing for the creation of fully synthetic biological systems. Within this field I am particularly interested in the creation of synthetic genetic regulatory circuits (GRNs) using Bio-Bricks as a foundation. A Bio-Brick is a piece of DNA that conforms to an agreed standard structure allowing for many of them to be pieced together using common lab techniques. What excites me the most is the possibility it opens to creating biological machines that can perform previously impossible tasks. It also provides a view into how complex biological systems can be built from the bottom up and is a useful foundation to test hypotheses relating to possible generic building blocks.
- Novel Computing Architectures - With current computer processor chip manufacture reaching the limits of miniaturisation, we are seeing the increased adaption of new computing architectures, e.g. multi-core and heterogeneous computing. These aim to get around the limited speeds by utilising large numbers of processing resources all running simultaneously on the different parts of the same problem - a divide and conquer approach. Although many problems can be decomposed to make good use of this shift, highly sequential processes suffer only being able to harness a small portion of the resources available. I am interested in understanding if there are completely new computing paradigms that can be used to get around some of these problems and to allow us to express problems in new ways. This includes the incorporation of ideas from Nature and techniques from bio-engineering with the hope of building highly resilient systems that can adapt and learn using distributed control and highly parallel, asynchronous processing.