Dr Lamiae Azizi

Senior Lecturer (US Associate Professor)
Deputy Champion (Nano Grand challenge)

Member of The University of Sydney Nano Institute

Member of the Brain and Mind Centre

F07 - Carslaw Building
The University of Sydney

Telephone (2) 9351 4249

Website Google scholar
Sydney Nano

Biographical details

I received a PhD in Applied Mathematics from Joseph Fourier University (France) and the French national research institute for the digital sciences (INRIA).

Before joining USYD, I held the position of Senior-Statistician at the MRC-university of Cambridge (UK). I am a member of the Statistics research group at the School of Mathematics and Statistics, the Deputy Champion for the Sydney Nano Grand challenge: "Next generation Materials Discovery" and the USYD Ambassador for the NSW smart sensing network.

Research interests

My research interests are developing Probabilistic Machine learning (PML) for big data and complex systems.

PML plays a central role in the development of Artificial intelligence. ML seeks to teach a machine how to learn from experience. This is achieved through developing mathematical models and algorithms that a machine, fed only with some observed data, can make predictions about future data and decisions that are rational given these predictions. Probability provides a framework for representing and manipulating the uncertainty about data and future consequences of actions. "Ultimately, Intelligence relies on understanding and acting in an imperfectly sensed and uncertain world".

My contributions lie particularly in Bayesian nonparametrics, Graphical modelling, Variational methods, and probabilistic learning. My applied research ranges from Health to Engineering and recently to Materials and Nanotechnologies related challenges.

Teaching and supervision

Timetable

L_Azizi

Current research students

Honours/Masters

  • Julien Lenhardt

Bayesian optimisation for force fields in Quantum chemistry

Micro-scale structures and communities in networks

  • Jia Min Dong:

Variational Autoencoders for Longitudinal data and applications in personalised healthcare.

PhD students

  • Nick Miltichinov (jointly supervised by Ben Goldys) :

Probabilistic Machine Learning for magnetisation dynamics of Ferromagnetic Materials

  • Matthew Ma (jointly supervised by Jie Yin) :

Data efficieny in deep probabilistic models

Stratified Space Learning

Bayesian modeling for high dimensional Time series

Probabilistic Graphical models for unsupervised Feature learning

Variational Nonparametric classification models for Genomics

Current research students

Project title Research student
Probabilistic Graphical Models for Unsupervised Feature Learning Simon LUO

Current projects

Selected grants

2019

Data Science for Integrative imaging in Alzheimer's diagnosis,L. Azizi; S.wade

Sydney - Edinburgh partnership collaboration award

Data Analytics for Ressources and Environments, Led by Prof S. Cripps

ARC Industrial Transformation Training Centre

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2018

Next generation materials Discovery,L. Azizi; I. Kassal

Sydney Nano Grand Challenges award

Awards and honours

  • Guest Editor (2019) Frontiers in Physics - Computational physics :

https://www.frontiersin.org/research-topics/9046/machine-learning-applications-for-physics-based-computational-models-of-biological-systems

  • Micorsoft Cambridge research AI for health team and I gave a tutorial "Machine Learning for Personalised Health" at the leading International conference in Machine Learning (ICML) 2018.

https://mlhealthtutorial.com


In the media

https://careerswithstem.com.au/profiles/research-statistician-lamiae-azizi/

http://www.rtbevent.com/lamiae-azizi/

Selected publications

Download citations: PDF RTF Endnote

Journals

  • Bell, K., Azizi, L., Nilsson, P., Hayen, A., Irwig, L., Ostgren, C., Sundrom, J. (2018). Prognostic impact of systolic blood pressure variability in people with diabetes. PloS One, 13(4), 1-11. [More Information]
  • Copp, T., McCaffery, K., Azizi, L., Doust, J., Mol, B., Jansen, J. (2017). Influence of the disease label polycystic ovary syndrome' on intention to have an ultrasound and psychosocial outcomes: A randomised online study in young women. Human Reproduction, 32(4), 876-884. [More Information]
  • Ballard, K., Azizi, L., Duffy, J., McNeil, M., Halaki, M., O'Dwyer, N., Layfield, C., Scholl, D., Vogel, A., Robin, D. (2016). A predictive model for diagnosing stroke-related apraxia of speech. Neuropsychologia, 81, 129-139. [More Information]
  • Chapman, S., Azizi, L., Luo, Q., Sitas, F. (2016). Has the incidence of brain cancer risen in Australia since the introduction of mobile phones 29 years ago? Cancer Epidemiology, 42, 199-205. [More Information]
  • Mattei, F., Liverani, S., Guida, F., Matrat, M., Cenee, S., Azizi, L., Menvielle, G., Sanchez, M., Pilorget, C., Lapotre-Ledoux, B., et al (2016). Multidimensional analysis of the effect of occupational exposure to organic solvents on lung cancer risk: The ICARE study. Occupational and Environmental Medicine, 73(6), 368-377. [More Information]
  • Chapman, S., Azizi, L., Luo, Q., Sitas, F. (2016). Response from the authors to correspondence related to 'Has the incidence of brain cancer risen in Australia since the introduction of mobile phones 29 years ago?'. Cancer Epidemiology, 44, 138-140. [More Information]
  • Marinovich, M., Azizi, L., Macaskill, P., Irwig, L., Morrow, M., Solin, L., Houssami, N. (2016). The Association of Surgical Margins and Local Recurrence in Women with Ductal Carcinoma In Situ Treated with Breast-Conserving Therapy: A Meta-Analysis. Annals of Surgical Oncology, 23(12), 3811-3821. [More Information]
  • Azizi, L. (2015). Book review: Bayesian methods in epidemiology (Lyle D. Broemeling, Chapman & Hall/CRC Biostatistics Series). Statistics in Medicine, 34(22), 3079-3080. [More Information]

Conferences

  • Chandra, R., Azizi, L., Cripps, S. (2017). Bayesian neural learning via langevin dynamics for chaotic time series prediction. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]

2018

  • Bell, K., Azizi, L., Nilsson, P., Hayen, A., Irwig, L., Ostgren, C., Sundrom, J. (2018). Prognostic impact of systolic blood pressure variability in people with diabetes. PloS One, 13(4), 1-11. [More Information]

2017

  • Chandra, R., Azizi, L., Cripps, S. (2017). Bayesian neural learning via langevin dynamics for chaotic time series prediction. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]
  • Copp, T., McCaffery, K., Azizi, L., Doust, J., Mol, B., Jansen, J. (2017). Influence of the disease label polycystic ovary syndrome' on intention to have an ultrasound and psychosocial outcomes: A randomised online study in young women. Human Reproduction, 32(4), 876-884. [More Information]

2016

  • Ballard, K., Azizi, L., Duffy, J., McNeil, M., Halaki, M., O'Dwyer, N., Layfield, C., Scholl, D., Vogel, A., Robin, D. (2016). A predictive model for diagnosing stroke-related apraxia of speech. Neuropsychologia, 81, 129-139. [More Information]
  • Chapman, S., Azizi, L., Luo, Q., Sitas, F. (2016). Has the incidence of brain cancer risen in Australia since the introduction of mobile phones 29 years ago? Cancer Epidemiology, 42, 199-205. [More Information]
  • Mattei, F., Liverani, S., Guida, F., Matrat, M., Cenee, S., Azizi, L., Menvielle, G., Sanchez, M., Pilorget, C., Lapotre-Ledoux, B., et al (2016). Multidimensional analysis of the effect of occupational exposure to organic solvents on lung cancer risk: The ICARE study. Occupational and Environmental Medicine, 73(6), 368-377. [More Information]
  • Chapman, S., Azizi, L., Luo, Q., Sitas, F. (2016). Response from the authors to correspondence related to 'Has the incidence of brain cancer risen in Australia since the introduction of mobile phones 29 years ago?'. Cancer Epidemiology, 44, 138-140. [More Information]
  • Marinovich, M., Azizi, L., Macaskill, P., Irwig, L., Morrow, M., Solin, L., Houssami, N. (2016). The Association of Surgical Margins and Local Recurrence in Women with Ductal Carcinoma In Situ Treated with Breast-Conserving Therapy: A Meta-Analysis. Annals of Surgical Oncology, 23(12), 3811-3821. [More Information]

2015

  • Azizi, L. (2015). Book review: Bayesian methods in epidemiology (Lyle D. Broemeling, Chapman & Hall/CRC Biostatistics Series). Statistics in Medicine, 34(22), 3079-3080. [More Information]

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