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Members

Meet the research members of our team
Our members hold knowledge in a wide variety of areas within data science, from Bayesian nonparametrics to machine learning.

Lamiae Azizi

Dr Lamiae Azizi received her PhD in Applied Mathematics from Joseph Fourier University (France) in December 2011.

Her research interests are developing statistical machine learning models that aim to understand real-world processes in high dimensional data; as well as developing appropriate software and visualisation tools.

Her contributions lie particularly in Bayesian nonparametrics, graphical modelling, variational methods, and probabilistic learning. Applications of interest include problems arising in the biomedical field, image analysis and engineering.


Rohitash Chandra

Dr Rohitash Chandra is a Research Fellow at the Centre for Translational Data Science and School of Geosciences at the University of Sydney. His research interests are in areas of deep learning, neuro-evolution, Bayesian methods, solid Earth Evolution, reef modelling and mineral exploration. He is currently developing novel learning algorithms for robust and dynamic decision making given misinformation and uncertainty from the environment. This provides a synergy of deep learning methods with Bayesian inference, and multi-task learning. Furthermore, he is involved in projects that employ machine learning methods and Bayesian inference via parallel tempering for solid Earth evolution, mineral exploration, and reef modelling.

Projects

  • Bayesian deep learning for uncertain environments
  • Solid Earth evolution
  • Reef modelling
  • Mineral exploration

Gilad Francis

Gilad Francis

Research Fellow

Dr Gilad Francis joined the University of Sydney in 2014 with extensive multidisciplinary experience in the private sector. His research interests include Bayesian active learning methods, planning and decision-making in robots, and data fusion. His industry engagement includes developing adversarial behavioural models for the Defence Science and Technology Group.

Research focus

Projects

  • Machine-learned defence to adversarial RF systems via system identification and strategy optimisation

Bryn Jeffries

Research associate

Dr Bryn Jeffries has research interests in the fields of database systems and machine learning. As a member of the Cooperative Research Centre (CRC) for Alertness, Safety and Productivity, he has investigated data-driven techniques to better understand, diagnose and treat sleep disorders such as insomnia and sleep apnoea.

He is currently working on the application of Gaussian processes to predict likely locations for mining mineral resources. Bryn also works in astrophysics, where he is investigating new methods to detect exoplanets.

Research focus

  • Personalised medicine (sleep health)
  • Predictions in mining

Projects

  • Origin mining
  • Sleep companion (with Alertness CRC)

 


Roman Marchant

Roman Marchant

Research Fellow and Lecturer

Dr Roman Marchant completed a PhD in Machine Learning at the School of Information Technologies. His current research at the Centre for Translational Data Science explores applying data science to the social sciences, currently focusing on predicting crime and understanding criminal behaviour. His area of expertise is Sequential Bayesian Optimisation (SBO).

Research focus

  • Data fusion
  • The science of decision-making
  • Data science ethics, law and policy
  • Human behaviour
  • Government and policy
  • Autonomous systems

Projects

  • Bayesian risk prediction instruments (with David Kohn)
  • Space-time and demographics crime prediction (with Sebastian Haan)
  • Tinder for researchers (with Chao Sun and King Tao)
  • Bayesian optimisation for GLM (with Di Lu and Huang Yi)
  • Environmental model for crime (with Jose Vera )
  • CNN model for crime (with Rohit and Rui Geng)
  • Visualisation for crime models (with Zue Zhang and Xiadong Chu)
  • Problem gamblers (with Alex Blazsinsky)

Richard Morris

Richard Morris

Research Affiliate

Dr Richard Morris has 10 years experience leading multidisciplinary projects in neuroscience, within cognitive science, computational neuroscience, brain imaging, and decision-making. A recipient of a NARSAD Young Investigator Award, his research has been supported by the NHMRC and published in top-tier journals such as Nature Communications and Molecular Psychiatry.

Research focus

  • Mental health
  • Human behaviour

Projects

  • Evaluation of early intervention services for psychosis (hYEPP)
  • Causal determinants of mental wellbeing in Australia (HILDA)
  • Bias removal in MRI datasets

Lionel Ott

Lionel Ott

Research Fellow

Dr Lionel Ott is a research fellow with expertise in both machine learning and robotics. In machine learning his main field of interest is clustering and anomaly detection, while on the robotics side he works on long-term autonomy, mapping, as well as planning.

Research focus

  • Data fusion
  • Autonomous systems

Projects

  • Origin energy: Modelling subsurface geology

Richard Scalzo

Richard Scalzo

Senior Research Scientist

Dr Richard Scalzo’s work focuses on the use of Bayesian inference to learn the covariance structures of complex sensors or simulation processes, with applications in geophysics, astrophysics, and renewable energy.

Research focus

  • Understanding the Earth’s evolution (geoscience)
  • Infrastructure (renewable energy)

Projects

  • [SAMI] Probabilistic datacubes for integral field spectroscopy
  • [Obsidian] Localising geological formation boundaries via Bayesian joint inversion
  • [MWATS] Detrending radio transient light curves from the Murchison Wide-Field Array
  • [RMHMC] Markov chain Monte Carlo on function spaces

Marcel Scharth

Marcel Scharth

Dr Marcel Scharth specialises in the fields of statistics, econometrics, and machine learning. His research interests include Bayesian methods, computational statistics, statistical learning, time series, longitudinal data, and text analysis, with focus on developing methods for the estimation of high-dimensional models in the intersection of these areas.

Research focus

  • Autonomous systems
  • Human behaviour
  • Mental health

Charmaine Tam

Charmaine Tam

NHMRC Early Career Fellow

Dr Charmaine Tam is an award-winning biomedical scientist with more than 10 years experience investigating the physiological mechanisms behind obesity and its metabolic complications. Charmaine’s research has evolved to health data analytics, where she partners with local health districts and industry to create insights from electronic medical records to improve health outcomes and health efficiency.

Research focus

  • Personalised medicine

Projects

  • Characteristics, management and outcomes of patients presenting with chest pain and acute coronary syndrome using data extracted from electronic records from Sydney Health Partner facilities. 
  • Harnessing data from electronic medical records to improve patient care and health outcomes: proof-of-concept study of febrile neutropenia in paediatric oncology. 
  • Using big data to improve health efficiency and reduce low-value care: An exploratory study using pathology testing. 
  • Analysis of the diagnosis of type 2 diabetes (T2D) in primary care in Australia. 

Emi Tanaka

Emi Tanaka

Dr Emi Tanaka received her PhD in statistics at the University of Sydney focusing on statistical methods for the analysis of DNA sequence motifs. She then worked in the statistics department for the Australian Grains Industry group providing specialist statistical inputs to various agricultural experiments.

Research focus

  • Agriculture

Projects

  • Investigating how supplementary feeding affects milk production in cows

Garth Tarr

Garth Tarr

Dr Garth Tarr is a statistician and data scientist with expertise in feature selection in complex data and predictive modelling. Garth's research is driven by applications in modelling complex agricultural data and biostatistics. He has strong industry ties and he is the statistical and methodological adviser on the Meat Standards Australia Beef Pathways Committee.

Research focus

  • Agriculture
  • Human behaviour
  • Data fusion
  • The science of decision-making