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Science Mathematics and Statistics

Honours in Quantitative Data Science

Abstract

The Honours in Quantitative Data Science is intended for students who have completed the Quantitative Data Science stream in program 3959, the Bachelor of Data Science and Decisions. It is also open to other students from other programs with a suitable mix of undergraduate courses.Ìý

Below you can find some specific information about Quantitative Data Science Honours.

For more general info about doing Honours in Quantitative Data Science, see theÌýHonours Page.

Honours Coordinator -ÌýQuantitative Data Science

If you have any questions about the Honours year, please don't hesitate to contact the Honours Coordinator.

Honours Coordinator- Quantitative Data Science

Dr Rohitash Chandra

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T:Ìý0413071839 or 02 9385 7111

Office:ÌýH13 Lawrence East 2053Ìý

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Quantitative Data Science project areas

The following are suggestions for possible supervisors and honours projects in Quantitative Data Science. Other projects are possible, and you should contact any potential supervisors to discuss your options.

  • Boris Beranger

    • Extreme value theory

    Rohitash Chandra

    • Bayesian neural networks
    • Deep learning
    • Machine learning
    • Earth and climate data science

    Feng Chen

    • Spatio-temporal data analysis
    • Inference with incomplete data
    • Statistical computation

    Josef Dick

    • Approximation properties of neural networks

    Gery Geenens

    • Nonparametric and semiparametric density estimation
    • Nonparametric and semiparametric regression, in particular binary regression
    • Functional data analysis

    ÌýShane Keating

    • In collaboration with , Detecting maritime vessel anomalies using k-means clustering

    Jeya Jeyakumar

    • Optimization based decision-making under data uncertainty
    • Data-driven robust optimization
    • Global optimization and data classification

    Pavel Krivitsky

    • Social network analysis
    • Analysis of blockchain data
    • Statistical computing

    Pierre Lafaye de Micheaux

    • Dependence measures
    • Neuro-imaging genetics
    • Data science for IoT

    Guoyin Li

    • Optimisation methods in machine learning
    • Optimisation under data uncertainty

    Raymond Louie (Co-supervisor; ÁñÁ«¹ÙÍø Computer Science & Engineering)

    • Statistical analysis of molecular data and applications in vaccine and drug design
    • Statistical modelling of biological systems
    • Machine learning

    Jake Olivier

    • Making sense of naturalistic driving study data

    Spiridon Penev

    • Inference about expectiles
    • Optimal capital allocation

    Vera Roshchina

    • Projection methods for machine learning
    • (Higher-order) Voronoi diagrams for data analysisÌý
    • Conic programming and semidefinite optimisation for data science

    Moninya Roughan (co-supervisor)

    • Oceanography
    • Big data and time series
    • Correlated variables

    Amandine Schaeffer (co-supervisor)

    • Environmental drivers of marine heatwaves
    • Understanding biological productivity in the ocean: statistical model from in-situ glider observations

    Scott Sisson

    • Bayesian inference
    • Computational statistics
    • Variational methods
    • Likelihood-free/indirect methods
    • Symbolic data analysis
    • Extreme value theory

    Eva StadlerÌý(The Kirby Institute)

    • Statistical analysis of data relating to infectious diseases (malaria or COVID-19)

    Mircea Voineagu

    • Developing mathematical methods of topological data analysis
    • Applications of topological data analysis for genetic and clinical data

    David Warton

    • Analysis of large spatial datasets
    • High-dimensional data analysis
    • Simulation-based inference
    • For more, see projects ideas