Honours in Quantitative Data Science

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. 

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

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

Honours Coordinator – Quantitative Data Science

Dr Clara Grazian
Email: c.grazian@unsw.edu.au
Phone: 9385 7475
Office: Rm 2056, Red Centre (Centre Wing)

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

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

Clara Grazian

  • Bayesian statistics
  • Clustering
  • Spatio-temporal modelling
  • Applications in genomics, cyber-security, ecology, finance
  • In collaboration with the Garvan Institute: Data-driven approaches for determining the rate of somatic mutation in individual cells
  • For some examples of my current projects, have a look at my page: https://research.unsw.edu.au/people/dr-clara-grazian

 Clara Grazian and Shane Keating

  • In collaboration with Spiral Blue, 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

Maarit Laaksonen

  • Burden of disease
  • Appropriate disease burden methods

Pierre Lafaye de Micheaux

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

Guoyin Li

  • Optimisation methods in machine learning
  • Optimisation under data uncertainty

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 analysis 
  • 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

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 UNSW Eco-Stats projects ideas