ProfessorMikhail Prokopenko

Project Opportunities

Title: Information Thermodynamics at the Edge of Chaos

Summary of opportunity:

The research will involve theoretical work as well as computer simulations. It will aim to discover fundamental connections between information-theoretic and statistical-mechanical approaches to self-organisation, while investigating nonlinear critical phenomena, with particular focus on information dynamics during phase transitions.    The PhD will be supervised by Prof. Mikhail Prokopenko. The applicant will join the Modelling and Simulation Research Group (MSRG) at The School of Computer Science, as well as the Centre for Complex Systems - The University of Sydney. 

Opportunity synopsis:

In studying the fundamental properties of complex dynamic systems, one is faced with the challenges of quantifying the dynamics during order-chaos phase transitions. These challenges become more formidable in situations when the system exhibits self-organisation: a broad phenomenon, occurring in a range of physical, biological, technological and social systems. During self-organisation, the system often approaches critical regimes (the edge of chaos) and undergoes phase changes which can be characterised through specific information dynamics (for example, changes in transfer entropy, Fisher information, excess entropy, etc.).   A search for fundamental connections between self-organisation and maximisation of suitably defined information-theoretic measures, pursued within the field of information thermodynamics, will form the research topic of the PhD study.

Applicants need to satisfy the eligibility criteria for PhD enrolment at The University of Sydney.  Backgrounds in applied mathematics, physics, computer science, and specifically in information theory and statistical mechanics will be beneficial.   The successful applicant will demonstrate a strong commitment to academic research in the proposed field. He/she will have excellent written and oral communication skills, as well as demonstrated ability to program in MATLAB, Java, or C++, and will be willing to create and develop original approaches to tackle open questions.   

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Project Opportunities

Title: Agent-based modelling, simulation and forecasting of epidemics

Summary of opportunity:

The research will involve theoretical work in information theory and network science, as well as computer agent-based simulations. It will aim to reveal fundamental theoretical connections between (i) established methods of computational epidemiology (based on percolation theory and critical thresholds) and (ii) novel crisis forecasting methods which employ quantitative information dynamics on complex networks. The PhD will be supervised by Prof Mikhail Prokopenko. The applicant will join the Modelling and Simulation Research Group (MSRG) at The School of Computer Science, as well as the Centre for Complex Systems - The University of Sydney. 

Opportunity synopsis:

There is a growing need to better understand multiple epidemiological, socio-economic, and socio-ecological implications of emerging threats posed by infectious diseases, epidemics and pandemics. The accuracy of modern epidemiological models can be considerably improved by the integration of large-scale datasets and the explicit agent-based simulation of entire populations down to the scale of a single individuals, coupled with complex network-based modelling.  This should enable a more precise forecasting of critical phenomena typically emerging in complex health systems (including phase transitions, tipping points, epidemic peaks, and so on).  This PhD study will aim at developing (i) novel modelling framework for forecasting of critical phenomena during epidemic crises; and (ii) novel computational methods for large-scale agent-based simulation of disease diffusion based on complex networks, information theory and percolation theory.

Applicants need to satisfy the eligibility criteria for PhD enrolment at The University of Sydney. Backgrounds in computational epidemiology, applied mathematics, physics, computer science, and specifically in information theory, complex networks and statistical mechanics will be beneficial. The successful applicant will demonstrate a strong commitment to academic research in the proposed field. They will have excellent written and oral communication skills, as well as demonstrated ability to program in MATLAB, Java, or C++, and will be willing to create and develop original approaches to tackle open questions. Applications should be sent by email to Prof. Mikhail Prokopenko: mikhail.prokopenko@sydney.edu.au. They should include a Resume and a Cover Letter. In their Cover Letter, applicants are invited to include a short (about 250 words) research statement explaining how they understand the issues related to the topic of research.  

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Project Opportunities

Title: Guided Self-Organisation and Swarm Engineering

Summary of opportunity:

The research will involve theoretical work as well as computer simulations. It will aim to discover innovative approaches to guided self-organisation and swarm engineering, while investigating nonlinear critical phenomena, with particular focus on information cascades and phase transitions within swarm systems. The PhD will be supervised by Prof. Mikhail Prokopenko. The applicant will join the Modelling and Simulation Research Group (MSRG) at The School of Computer Science, as well as the Centre for Complex Systems - The University of Sydney. 

Opportunity synopsis:

Examples of self-organising systems can be found practically everywhere: a heated fluid forms regular convection patterns of Bénard cells, neuronal ensembles self-organise into complex spike patterns, a swarm changes its shape in response to an approaching predator, ecosystems develop spatial structures in order to deal with diminishing resources, and so on. Typically, self-organisation (SO) is defined as the evolution of a system into an organised form in the absence of explicit external pressures. Guided Self-Organisation (GSO) attempts to reconcile two seemingly opposing forces: one is implicitly guiding a self-organising system into a better structured shape and/or functionality, while the other is diversifying the options in an entropic exploration within the available search space. In doing so, one puts in place some constraints on the system dynamics to mediate behaviours and interactions, rather than trying to precisely control a transition towards the desirable outcomes. Many animals dynamically self-organise within spatial aggregated groups (schools of fish, swarms of locusts, herds of wildebeest, and flocks of birds). Based on perception of local conditions, complex large-scale patterns and structures emerge within a swarm through individual decisions, and further propagate in information cascades. This PhD study will be focussed on several specific challenges: (i) how to identify phase transitions in collective swarm behaviour through dynamics of information cascades; (ii) how to quantify information flows and physical fluxes between the swarm and its environment;  and (iii) how to optimally guide swarm behaviour to desirable outcomes by placing broad-spectrum constraints.

Applicants need to satisfy the eligibility criteria for PhD enrolment at The University of Sydney. Backgrounds in applied mathematics, physics, computer science, and specifically in information theory and statistical mechanics will be beneficial. The successful applicant will demonstrate a strong commitment to academic research in the proposed field. He/she will have excellent written and oral communication skills, as well as demonstrated ability to program in MATLAB, Java, or C++, and will be willing to create and develop original approaches to tackle open questions. Applications should be sent by email to Prof. Mikhail Prokopenko: mikhail.prokopenko@sydney.edu.au. They should include a Resume and a Cover Letter. In their Cover Letter, applicants are invited to include a short (about 250 words) research statement explaining how they understand the issues related to the topic of research.

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Project Opportunities

Title: Emergence of universal coding in evolutionary dynamics

Summary of opportunity:

The research will involve theoretical work in information theory as well as computer simulations. It will investigate complex systems approaches to one of the most fundamental problems in systems biology: emergence of universal coding, considered as an innovation-sharing protocol in evolutionary dynamics. The PhD will be supervised by Prof. Mikhail Prokopenko. The applicant will join the Modelling and Simulation Research Group (MSRG) at The School of Computer Science, as well as the Centre for Complex Systems - The University of Sydney. 

Opportunity synopsis:

One of the most fundamental problems in systems biology is the definition and understanding of the gene. For example, Carl Woese presents the real problem of the gene show the genotype-phenotype relationship had come to be. Arguably, the reason for this increase in complexity can be identified with specific communication mechanisms within a complex sophisticated network of interactions, which are exhibited by translationally produced proteins, multi-cellular organisms, and social structures in general. The evolution of the translation mechanism is a complex process, and we may only intend to analyse its simplified models. However, in doing so this PhD study shall take a principled approach and consider a computational model of evolutionary dynamics in a generic information-theoretic way, aiming to suggest mechanisms resolving Eigen's paradox. Specifically, the project will develop new computational information‐theoretic model for evolutionary dynamics approaching thecoding threshold”.  In addition, the study will analyse how different proto‐cells could stigmergically share such information within a self-organising innovation-sharing protocol, leading to universal encoding.

Applicants need to satisfy the eligibility criteria for PhD enrolment at The University of Sydney. Backgrounds in applied mathematics, physics, computer science, and specifically in information theory, complex networks and statistical mechanics will be beneficial. The successful applicant will demonstrate a strong commitment to academic research in the proposed field. He/she will have excellent written and oral communication skills, as well as demonstrated ability to program in MATLAB, Java, or C++, and will be willing to create and develop original approaches to tackle open questions. Applications should be sent by email to Prof. Mikhail Prokopenko: mikhail.prokopenko@sydney.edu.au. They should include a Resume and a Cover Letter. In their Cover Letter, applicants are invited to include a short (about 250 words) research statement explaining how they understand the issues related to the topic of research. 

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Project Opportunities

Title: Computational intelligence, novelty generation and undecidability

Summary of opportunity:

The research will involve theoretical work in computational intelligence as well as computer simulations with cellular automata and other dynamical systems. It will aim to answer fundamental questions on the nature of universal computation, manifested across several classes of complex computational systems. The PhD will be supervised by Prof. Mikhail Prokopenko. The applicant will join the Modelling and Simulation Research Group (MSRG) at The School of Computer Science, as well as the Centre for Complex Systems - The University of Sydney. 

Opportunity synopsis:

One feature of intelligent behaviour is complexity in creating innovations: a mechanism producing computational novelty needs to exceed some threshold of complexity. Furthermore, in order to be truly impressive in generating endogenous innovation, it needs to be capable of universal computation. In other words, computational novelty may be fundamentally related to undecidability. Serious advances have been made in identifying deeper interconnections between dynamical systems, Turing Machines, and formal logic systems: in particular, the complex, class IV, cellular automata were related to formal systems with undecidable statements (Gödel's incompleteness theorem) and the Halting Problem. Nevertheless, the question whether universal computation is the ultimate innovation-generator is still unresolved, offering a challenging question: how computational intelligence, including mechanisms producing novelty, is related to undecidability?

Applicants need to satisfy the eligibility criteria for PhD enrolment at The University of Sydney. Backgrounds in applied mathematics, physics, computer science, and specifically in information theory, complex networks and statistical mechanics will be beneficial. The successful applicant will demonstrate a strong commitment to academic research in the proposed field. He/she will have excellent written and oral communication skills, as well as demonstrated ability to program in MATLAB, Java, or C++, and will be willing to create and develop original approaches to tackle open questions.  Applications should be sent by email to Prof. Mikhail Prokopenko: mikhail.prokopenko@sydney.edu.au. They should include a Resume and a Cover Letter. In their Cover Letter, applicants are invited to include a short (about 250 words) research statement explaining how they understand the issues related to the topic of research.