Projects per year
Personal profile
Research interests
- Model selection annd variable selection
- Multi-state models and survival analysis
- Multiple testing problems,
- Dimension reduction methods
- Analysis of high dimensional and massive data set such as “Omics” data
- Bayesian Modelling
- Machine Learning
- Application to combination of clinical, health and biological data
- Statistics methods to Computational Ecology and Environmental Sciences
Teaching
- STAT7111/8111 (Generalized Linear Model), S2, 2020
- Mathematical Background for Biostatistics (MBB), S2, 2020 at BCA.
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The Mathematical Engineering of Deep Learning during the summer school organized by AMSI https://ss.amsi.org.au/
Research student supervision
PhD scholarships
If you are interested in doing a PhD or MRes at Macquarie under my supervision, and are eligible to apply for any of the scholarships listed here, then please contact me in advance of the closing dates.
Postgraduate research topics in statistics:
Competing risks for clustered data: the heterogenity of the COVID mortality across countries
Supervisor: Benoit Liquet
Topic Description:
The mortality rate of COVID-19 among patients admitted to the intensive care unit (ICU) is currently very high. It is one of the primary cause of death in ICUs. The aim is to build a prediction model that predicts the risks and chances of the expected course of intensive care unit (ICU) admissions for suspected COVID infected patient. The model should provide evidence based information for physicians.
The specific aims are to develop and implement an updating mechanism which allows dynamic updating of the predictions for a given patient when important markers change, and dynamic updating of a model which was adapted to a given ICU when new data was entered into the database. To achieve it, we will exploit combined data sets which detail a variety of patients entering in ICU from diverse geographic locations. Then, the heterogeneity of practice across countries will be addressed.
The main aims of this project are to:
• Develop a multi-state model which models the course of the patient in intensive care unit.
• Different Markov models will be investigated: non-homogeneous Markov models; semi-markov Models and finally non-homogeneous semi-markov Models
• An additional avenue is also to study the ``landmark’’ model
• Incorporate random effect (“Frailty Models”) to take into account potential heterogeneity
• Assessment of prediction accuracy
• Development of R packages.
• Writing of scientific article
Joint sparse group Bayesian models on individual data for detecting pleiotropic effects: application to cancer datasets
Supervisor: Benoit Liquet
Topic Description:
Recent technological advances in molecular biology and genomics given rise to numerous large-scale datasets. The sheer size and complexity of these data sets imposes new methodological challenges. The aim of this project is to boost and enhance the current toolkit for integrative analysis of massive datasets, especially for detecting pleiotropy.
The novel methodology that we aim to develop will enable scientists to explore and analyse massive data sets from the “dark genome”, majority of genes in the genome have minimal knowledge. This research contributes to extend pleiotropy knowledge in human disease in order to drive diagnosis, therapeutic intervention and individualised treatment within precision medicine.
The new statistical approaches will be applied to enrich our insights about the genetic mechanisms of thyroid and breast cancer types.
The main aims of this project are to:
• Methods development for pleiotropy analyses
• Simulation studies design and validation of methods.
• Application to real data. Comparison of results while considering each cancer individually and while considering both cancers together.
• Development of R packages.
• Writing of scientific article
Advanced Machine Learning methods for Corals classification within the lagoon of Maupiti
Supervisor: Benoit Liquet
Partners: Damien Sous (MIO/SIAME), Samuel Meulé (CEREGE)
Topic Description:
Mapping Coral Reef Ecosystems are crucial for monitoring, protecting and ressource management purpose. Remote sensing by satellite offers a valuable cost-effective and time-efficient tool to provide image data of Coral reefs. A major challenge is to be able to extract information on coral classification and habitat description from the satellite data, in order to track the evolution of coral reef-lagoon systems in a global degradation context. A first accurate processing of the images is carried out to extract statistical metrics of the reflectance in different bands. These parameters are used to build an extensive database of explanatory variables. A series of classification tools can thus be applied to discriminate and to classify the different coral types. In particular, supervised classification methods could be explored such as:
• Softmax regression model
• Support Vector Machine
• Random Forest
• Gradient Boosting tree
In this project, different Machine learning methods for Corals classification will be compared in order to provide the best tool for pixel classification. A significant outcome will be the creation of an automated computational R package to classifies each pixel in an image given a pre-defined set of classes.
Education/Academic qualification
Statistics, HDR (PhD Supervisor diploma), Statistical Modelling and Biomedical Applications, University of Bordeaux
2009
Award Date: 11 Dec 2009
Statistics, Phd, Universite de Bordeaux
1999 → 2002
Award Date: 9 Dec 2002
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Collaborations and top research areas from the last five years
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FightMND MCR Fellowship: Comprehensive disease-gene discovery across FALS, SALS & ALS twins
Williams, K., Liquet-Weiland, B. & Blair, I.
1/02/24 → 31/01/28
Project: Research
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BDRC: Biomolecular Discovery Research Centre
Sunna, A., Packer, N., Paulsen, I., Guillemin, G., Li, M., Sofronov, G., Haynes, P., Mabbutt, B., Tetu, S., Thaysen-Andersen, M., Ponton, F., Garcia-Bennett, A., Wang, Y., Cain, A., Kroukamp, H., Maselko, M., Francis, H., Liquet-Weiland, B., Rodger, A., Lee, A., Connor, M., Inglis, D., Carthey, A. & Asadnia, M.
1/01/20 → 31/12/22
Project: Other
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Best subset solution path for linear dimension reduction models using continuous optimization
Liquet, B., Moka, S. & Muller, S., Feb 2025, In: Biometrical Journal. 67, 1, p. 1-17 17 p., e70015.Research output: Contribution to journal › Article › peer-review
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HEXB drives raised paucimannosylation in colorectal cancer and stratifies patient risk
Kawahara, R., Kautto, L., Bansal, N., Dipta, P., Chau, T. H., Liquet-Weiland, B., Ahn, S. B. & Thaysen-Andersen, M., Mar 2025, In: Molecular & cellular proteomics : MCP. 24, 3, p. 1-16 17 p., 100927.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Mathematical engineering of deep learning
Liquet, B., Moka, S. & Nazarathy, Y., 2025, Boca Raton, FL: CRC Press, Taylor & Francis Group. 402 p. (Chapman & Hall/CRC Data Science Series)Research output: Book/Report › Book › peer-review
5 Citations (Scopus) -
COMBSS: best subset selection via continuous optimization
Moka, S., Liquet, B., Zhu, H. & Muller, S., Apr 2024, In: Statistics and Computing. 34, 2, p. 1-22 22 p., 75.Research output: Contribution to journal › Article › peer-review
Open AccessFile3 Citations (Scopus)106 Downloads (Pure) -
Dealing with area-to-point spatial misalignment in species distribution models
Mourguiart, B., Chevalier, M., Marzloff, M., Caill-Milly, N., Mengersen, K. & Liquet, B., 2024, (Submitted) Zenodo, (Zenodo).Research output: Working paper › Preprint
Prizes
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Winner - Faculty of Science and Engineering Award for Inter-School Collaboration - 2023
Nayak, Abhaya (Recipient), Mahmood, Adnan (Recipient), Beheshti, Amin (Recipient), McIver, Annabelle (Recipient), Bilgin, Ayse (Recipient), Liquet-Weiland, Benoit (Recipient), Svensson, Carl (Recipient), Ramakrishnan, Charanya (Recipient), Smith, Connor (Recipient), Molla-Aliod, Diego (Recipient), Vatsalan, Dinusha (Recipient), Xue, Emma (Recipient), Gupta, Gaurav (Recipient), Sofronov, Georgy (Recipient), Baker, Greg (Recipient), Liu, Guanfeng (Recipient), Asghar, Hassan (Recipient), Doosti, Hassan (Recipient), Zhu, Houying (Recipient), Jiang, Iris (Recipient), Zheng, James (Recipient), Lin, Huan (Recipient), Wu, Jia (Recipient), Ma, Jun (Recipient), Binkowski, Karol (Recipient), Dras, Mark (Recipient), Manuguerra, Maurizio (Recipient), Orgun, Mehmet (Recipient), Lay, Michael (Recipient), Zou, Nan (Recipient), Kordzakhia, Nino (Recipient), Busch, Peter (Recipient), Graham, Petra (Recipient), Schwitter, Rolf (Recipient), Chen, Shan (Recipient), Smith, Stephen (Recipient), Cassidy, Steve (Recipient), Prvan, Tania (Recipient), Fung, Thomas (Recipient), Li, Yan (Recipient), Wang, Yan (Recipient), Zhang, Yang (Recipient) & Zhou, Yipeng (Recipient), 2023
Prize: Other distinction