My research includes solar astrophysics, and more general problems
emphasizing computational and statistical modeling (e.g. modeling
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In solar astrophysics I'm interested in key problems in the physics of solar flares, flare statistics, the modelling of coronal magnetic fields, solar-terrestrial relations, and solar activity in general. Computational and traditional methods are applied to solve critical problems. Techniques include large scale computation, theory/modelling, data analysis, visualization, and a combination of these methods. I work within the Sydney Institute for Astronomy that is part of the School of Physics, but my research overlaps with many different fields, and I would like to invite students from computer science, mathematics and engineering to apply as long as they meet the Honours prerequisites. Within solar astrophysics I have projects available that are predominantly computational, including large-scale parallel computing, suitable for students with a general science background. I also have projects available jointly supervised with Stephen Hardy at NICTA, Australia’s Information Communications Technology (ICT) Research Centre of Excellence, involving machine learning, Bayesian probability, and data analysis.
A nonlinear force-free magnetic field model for solar active region 10953.
Research projects are available for students at Third Year, Honours and Ph.D. levels. I encourage students to participate in all aspects of research, including identifying a project. The goal is to locate a question that you find interesting. The question may be completely novel or part of a program of ongoing research, provided I think that we can answer it!
Research in this area will develop skills (computation, numerical analysis, modelling, visualisation, data analysis, statistical techniques) which are highly transferrable and attractive to employers. Candidates will have the opportunity to collaborate with experts at world class institutions with whom I maintain ongoing research partnerships, including Lockheed Martin and Stanford University. My research is published in high-impact peer reviewed journals such as the Astrophysical Journal, the premier US journal in the field. You can achieve research at this level!
Other projects: Current projects will be listed below. A variety of other projects in related areas are possible.
PhD project: Generic methods in data mining
Mike Wheatland, SIfA/The University of Sydney (email@example.com)
Stephen Hardy, NICTA (Stephen.Hardy@nicta.com.au)
Natural scientists are increasingly confronted with problems in the analysis of large, multi-parameter datasets. For example, astrophysical images of objects may be available for multiple times at different wavelengths, or geophysical data sets may be available at different spatial locations. This project will develop new methods applicable to combining data from different data sources to improve the accuracy of the information inferred from the data. These methods will be suitable for use across the natural sciences, as well as related areas such as finance and big data analytics, based on the latest machine learning algorithms (e.g. this paper). The methods will be tested on specific problems, e.g. multi-wavelength solar observations, and the inversion of large scale geophysical datasets such as gravity and magnetic data. The project requires a strong maths and statistics background, programming skills, and interest in inter-disciplinary applications. The successful applicant's supervision will be provided jointly at NICTA and at SIfA/The University of Sydney and NICTA will contribute a top-up to a student's PhD scholarship (see this page for details).
PhD project: Model identification for nonlinear systems via machine
Is it possible to determine a model, i.e. a set of governing dynamical equations, based only on observing the time-behaviour of a nonlinear - and possibly chaotic - system? Recent work (see this page) demonstrates that this is possible for specific problems, using machine learning methods. In this project new techniques will be developed for tackling this problem, using the latest developments in non-parametric Bayesian analysis and machine learning. Specific applications will be investigated - e.g. modelling the underlying dynamics of non-linear time series data from chaotic lasers, modelling the dynamical behaviour of sunspots, and modelling the evolution of forest ecosystems using ecology datasets. The project requires a strong maths and statistics background, as well as programming skills. The successful applicant's supervision will be provided jointly at NICTA and at SIfA/The University of Sydney and NICTA will contribute a top-up to a student's PhD scholarship (see this page for details).
Hons project: Solar flare prediction via non-parametric Bayesian
Solar flares are explosive events involving magnetic energy release in
the Sun's corona. Because flares directly affect the Earth's space
weather environment (leading e.g. to damage to satellite electronics),
there is considerable interest accurate solar flare prediction. However,
the flare mechanism is not well understood, and existing methods of
prediction are probabilistic, generally relying on imperfect
associations between properties of sunspot regions in which flares occur
(in particular properties of the observed magnetic field of the sunspot
regions producing flares), and flare occurrence. A Bayesian approach to
flare prediction based only on observed solar flare statistics has been
developed and tested (Wheatland 2004, 2005). In this project the event
statistics method will be combined with machine learning approaches to
forecasting, allowing incorporaton of the additional information
provided by the properties of flare producing regions on the Sun. There
is scope for analytic and numerical work. The project will be jointly
supervised by Stephen Hardy at NICTA, who has expertise in machine
Hons/UG project: Bayesian analysis of large solar flare occurrence
In 1859 Richard Carrington observed a gigantic solar flare on the Sun,
which subsequently disabled telegraph communications on the Earth for
about a day. Solar flares are
magnetic explosions in the solar atmosphere that affect our local
"space weather." The 1859 Carrington flare disrupted
the communications technology of the day, and in the modern era
we face the prospect of the loss of communications satellites due
to a huge space weather storm (estimates of the cost run to tens
of billions of dollars). It is crucial to know how likely is the
occurrence of a Carrington-scale event. This project will begin
with an investigation of
the rate of occurrence of the largest flares and the time variation
of the rate, using a new Bayesian rate-determination algorithm applied
to data. The project emphasizes physical reasoning and estimation,
programming, data analysis, numerical methods, and statistical
procedures including Bayesian methods.
Additional Honours projects:
Feel free to discuss these and other research opportunities with me, in person or via e-mail.
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