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Joseph Lizier's team

Joseph Lizier -- PhD opportunities

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The sections below outline some sample PhD projects available to study with me in complex systems at the School of Computer Science and Centre for Complex Systems at The University of Sydney.
These projects focus on using information theory to study the dynamics of information processing in complex systems, such as in a computational neuroscience setting. There are variants available on each, and I'm happy to discuss other options or ideas that you may have (so long as they're relevant to my expertise). You can read about other PhD projects I have supervised at my Team page.

You are welcome to contact me without referring to one of the specific projects below.
You should include:

You will need a First-class honours equivalent undergraduate record in order to qualify for scholarships at USyd; the competition for scholarships for international students is particularly competitive.


Project area #1: Characterising information flow networks across brain regions in rest and task

Research Areas
Complex Systems and Complex Networks, Information Theory and Computational Neuroscience

Summary
The research will involve computational analysis in complex systems, complex networks, information theory, dynamical systems and computational neuroscience. The student will be exploring applications of, and/or updates to algorithms for, inferring brain network models to represent information flow relationships between brain regions, based on time-series neural recordings (such as fMRI, EEG, MEG, etc). The PhD will be supervised by A/Prof. Joseph Lizier. The applicant will join A/Prof. Lizier's Information Dynamics team in the Modelling and Simulation group, which studies complex systems and networks at The School of Computer Science. The student will collaborate with A/Prof. Mac Shine (Brain and Mind Centre) and Dr. Ben Fulcher (Physics) and their teams as part of their Systems Neuroscience and Complexity collaboration, within the University's Centre for Complex Systems.

Synopsis
Billions of years of evolution have shaped brain structure and function to solve complex problems, likely by shaping information-flow around the trillions of connections that comprise the human brain. We now have access to neural recordings of unprecedented quality and resolution, but we still do not know how distributed whole-brain neural activity patterns give rise to human cognition. Network neuroscience frames cognitive functions as emergent properties of the distributed and dynamic interactions between regions across the brain, seeking to create brain network models from high quality data. Yet the measurements used to model brain networks from time-series recordings have thus far mostly focussed on symmetric correlation-based functional networks.
This project will measure directed, multivariate and nonlinear information flows across the brain to establish network models that more wholistically map cognitive information processing directly from functional neuroimaging data. The project will utilise our JIDT and IDTxl open-source toolkits, implementing the information-theoretic measure transfer entropy and its variants to characterise information flow between time-series. (Further reading is available regarding the algorithms we use for directed functional and effective network inference in Novelli et al., 2019, and Novelli and Lizier, 2021). Multiple project possibilities are available, analysing various open data sets, including neural time-series recordings such as fMRI, potentially including both resting state and various task recordings, and in human, mouse, etc. There is also the potential to explore improvements to numerical estimators and algorithms for network inference.

Funding information
A scholarship for is available for one PhD position on this project funded by the Australian Research Council project DP240101295 "Evaluating the Network Neuroscience of Human Cognition to Improve AI", and will be co-supervised with A/Prof. Lizier's colleagues on the project A/Prof. Mac Shine (Brain and Mind Centre) and Dr. Ben Fulcher (Physics). The student will interact with their teams also, for example in examining how the learnings from organization of the human brain can inform improvements in AI.
This scholarship is offered to both international and domestic PhD applicants for 3.5 years (fulltime), providing the tuition fees and a stipend at the RTP rate.
There are opportunities for additional students (unsuccessful for the ARC funded scholarship) to work on other aspects of the project: such students will need to qualify for a University RTP scholarship (domestic or international), or identify other funding for a scholarship (e.g. China Scholarship Council), or self-fund.

Eligibility and application information
Applicants need to satisfy the eligibility criteria for PhD enrolment at The Faculty of Engineering at The University of Sydney (e.g. First-class honours equivalent results are essential).
Successful candidates will have:

How to apply:
Applications should be sent initially by email (before a formal enrolment application to the University) and include the following:

The successful applicant for the ARC funded scholarship is expected to start in mid-late 2024. That scholarship will be open until filled.


Project area #2: Synchronisation in complex networks

Research Areas
Complex Systems and Complex Networks, Dynamical Systems

Summary
The research will involve computational and mathematical analysis in dynamical systems and complex networks. The student will be developing mathematics for and computational analysis of dynamics on complex networks; this will involve computational experiments including simulations and numerical analysis. The PhD will be supervised by A/Prof. Joseph Lizier. The applicant will join A/Prof. Lizier's Information Dynamics team in the Modelling and Simulation group, which studies complex systems and networks at The School of Computer Science, and potentially involve collaborations within the University's Centre for Complex Systems.

Synopsis
Studies of the structure of complex networks have been one of the great successes of complex systems in the past several decades, establishing well-known small-world and scale-free networks for example and revealing how widely they occur in the world around us. The field has been very successful in characterising the structure of complex networks, but we remain less well informed about the function of complex networks. That is, one of the most significant open questions in complex systems research is that of structure-function: how does the structure of a complex network relate to its dynamics?
A canonical problem of structure-function has been that of characterising synchronisation, a phenomenon of interest observed across fireflies, heart cells, the human brain in epilepsy, and in power grids. How does the structure of connections between the entities in these systems help or hinder them from synchronising their activity, and can we control this?
We have recently published the first method to fully relate the structure of a complex network to how well it can synchronise (Lizier et al, PNAS, 2023), and to interpret that in terms of walks on networks. This presents the opportunity to build on this method for further insights (such as for networks with delayed coupling), and to utilise it to explore further scenarios.

Funding information
This project is not funded by a grant; students will need to qualify for a University RTP scholarship (domestic or international), or identify other funding for a scholarship (e.g. China Scholarship Council), or self-fund.

Eligibility and application information
Applicants need to satisfy the eligibility criteria for PhD enrolment at The Faculty of Engineering at The University of Sydney (e.g. First-class honours equivalent results are essential).
Successful candidates will have:

How to apply:
Applications should be sent initially by email (before a formal enrolment application to the University) and include the following:


Project area #3: Dynamic snapshots of multivariate network effects in collective animal flocking/schooling

Research Areas
Complex Systems and Complex Networks, Information Theory, Collective Animal Behaviour

Summary
The research will involve computational analysis in complex systems, complex networks, information theory and collective animal behaviour. The student will be exploring new algorithms for measuring multivariate information flow relationships between animals in flocks/schools, based on time-series recordings of their positions and speed. The PhD will be supervised by A/Prof. Joseph Lizier. The applicant will join A/Prof. Lizier's Information Dynamics team in the Modelling and Simulation group, which studies complex systems and networks at The School of Computer Science. The student will collaborate with Prof. Ashley Ward (Life Sciences), within the University's Centre for Complex Systems.

Synopsis
Collective behaviour such as flocking, swarming or schooling in animals such as birds and fish provides important evolutionary advantages, such as predator avoidance. Understanding the dynamics of these group behaviours are critical to inform our management of a changing environment. Fully characterising the connections between individuals in these groups remains difficult though, since flocking or schooling interactions induce fluid structures whereby individuals who are close-by and genuinely interacting may shift over extremely short periods of time. This presents substantial challenges for standard time-series analysis, yet if we could wholistically model such interactions there is major interest in the utility of such a method.
As a first step, we have adapted the information-theoretic measure transfer entropy (TE) to provide the gold-standard approach for measuring pairwise information flows in flocks/schools/swarms. This method identifies source-target pairs which are pairwise interacting at any snapshot in time. In collaboration with Prof. Ashley Ward (USyd Life Sciences) we have used this method to relate pairwise TE to hunger and predation in fish schools, and relative location of source fish. This project will extend that research beyond pairwise information flows to measure higher-order interactions in flocks/schools (e.g. to detect when an effect on a target results from a synergistic combination of two source individuals' actions). This will allow us to infer for the first time the set of causal parents for a target individual at any given snapshot in time. The project will utilise our JIDT open-source toolkit, extending the methodology for studying information flows in flocking within it. There is potential for many applications to real data sets of schooling fish.

Funding information
This project is not funded by a grant; students will need to qualify for a University RTP scholarship (domestic or international), or identify other funding for a scholarship (e.g. China Scholarship Council), or self-fund.

Eligibility and application information
Applicants need to satisfy the eligibility criteria for PhD enrolment at The Faculty of Engineering at The University of Sydney (e.g. First-class honours equivalent results are essential).
Successful candidates will have:

How to apply:
Applications should be sent initially by email (before a formal enrolment application to the University) and include the following: