Projects

Advice for Prospective MSc or PhD Students

I am seeking to work with enthusiastic, dedicated, passionate, self-motivated, and independent students. Please review my publication record to understand the nature of my research and assess if your interests align with my work (note that I am based in a School of Mathematics and Statistics). If you believe you would be a good fit, feel free to contact me to discuss any research-related topics! You can find additional details that may be helpful here.

(Neuro) Imaging Genetics

Disentangling genetics from environmental factors using medical images

Big Data and Internet of Things: OpenData for everyone!

New statistical methods and computing tools to extract information from big data sets, with a specific focus on those coming from the Internet of Things

Dependence Measures

Finding links in the big data era

Statistical Inference for Complex Random Vectors

New statistical methods and computing tools to analyse complex-valued random vectors

Data Science Book

Our new online book on Data Science

Close Current Collaborators

Researchers currently working with me

Below are researchers with whom I have the chance to work currently on the following research projects: Dependence Measures, (Neuro)Imaging Genetics and Internet of Things.

Faculty

minipic Professor Benjamin Avanzi Dependence Measures
minipic Dr Gery Geenens Dependence Measures
minipic Professor Spiridon Penev Dependence Measures, Internet of Things
minipic Professor Benoit Gallix (Neuro) Imaging Genetics
minipic Professor Benoit Liquet (Neuro) Imaging Genetics
minipic Professor Pavlo Mozharovskyi (Neuro) Imaging Genetics, Internet of Things
minipic A/Prof Myriam Vimond (Neuro) Imaging Genetics
minipic A/Prof Wei Wen (Neuro) Imaging Genetics
minipic Dr Fabien Navarro Internet of Things

PhD Students

minipic Qian Jin __

  • 80%

minipic Rianti Siswi Utami __

  • 80%

minipic Amuchechukwu Henrietta Ibenegbu __

  • 20%

minipic Zelong Bi __

  • 20%

minipic Tran Bao Khue __

  • 0%

Honours Students

minipic Bodu William Gong __

  • 80%

M.Sc. Students

Collaborators

Past and present

Former Students

Postdocs

Ph.D. students

  • 2021-2024 minipicMarie-Félicia Béclin, Development of intelligent models from CT imaging data of patients undergoing treatment with Benralizumab (joint supervision with N. Molinari), Université de Montpellier.

  • 2017-2023 minipicGuillaume Boglioni-Beaulieu, Validating dependence assumptions in actuarial risk modelling (joint supervision with B. Avanzi and B. Wong), UNSW Sydney.

  • 2011-2016 minipicJoseph Francois Tagne Tatsinkou, Smooth Goodness-of-fit tests in Time Series Models (joint supervision with P. Duchesne), Université de Montréal.

  • 2010-2013 minipicJérémie Riou, Multiplicity of tests, and computation of sample size in clinical research (joint supervision with B. Liquet and S. Marque), Université de Bordeaux Segalen.

  • 2008-2011 minipicBastien Marchina, Goodness-of-fit tests based on characteristic functions (joint supervision with Gilles Ducharme, MSER Grant), Université Montpellier II.

M.Sc. and Honours students

  • 2024 minipicBodu Gong, Honours, School of Mathematics and Statistics, UNSW Sydney.

  • 2023 minipicZelong Bi, Data Stream Analysis - Update Statistics and Online Learning over a Sliding Window, Honours, School of Mathematics and Statistics, UNSW Sydney.

  • 2023 minipicSteven Lim, A Wassersten Distance Based Goodness-of-fit Test for a Bivariate Uniform Distribution, Honours, School of Mathematics and Statistics, UNSW Sydney.

  • 2023 Roger Huang, Approximate Bayesian Computation with Hamiltonian Monte Carlo, Honours, School of Mathematics and Statistics, UNSW Sydney.

  • 2022 minipicYang Li, Literature reviews in science: why and how with a special focus on the Raspberry Pi in scientific and medical research, M.Sc., School of Mathematics and Statistics, UNSW Sydney.

  • 2022 minipicEllen Wang, Lacune Detection Using Random Forests, Honours, School of Mathematics and Statistics, UNSW Sydney.

  • 2021 minipicRuoyu Wang, Machine Learning Tools for the Analysis of Scanner Images of the Lungs, M.Sc., Université de Montpellier.

  • 2021 minipicRobert Cantwell, Towards a Self-Contained Theory for Stochastics in the Complex Plane, Honours, School of Mathematics and Statistics, UNSW Sydney.

  • 2021 minipicAlex Zhu, R on Raspberry Pi: the RaspberryPiR Package for Collecting and Analysing streaming Data, Honours, School of Mathematics and Statistics, UNSW Sydney.

  • 2021 minipicAndy Xuanan Yu, Examining the Heritability of the Spatial Distribution of Brain White Matter Fibre Tracts Using Diffusion Tensor Imaging Scans of OATS by employing Data Curves’ Depths, Honours, School of Mathematics and Statistics, UNSW Sydney.

  • 2020 minipicMin Sun, Classification of Functional Data, M.Sc., School of Mathematics and Statistics, UNSW Sydney.

  • 2020 minipicKai Lin, Brain Age Prediction Using Machine Learning on the Diffusion Tensor Imaging of White Matter, M.Sc., School of Mathematics and Statistics, UNSW Sydney.

  • 2020 minipicSimon Ho, Using a Multilayer Feedforward Neural Network for Normality Testing, M.Sc., School of Mathematics and Statistics, UNSW Sydney.

  • 2019 minipicJames Tian, Complex Numbered Linear Regression, Honours, School of Mathematics and Statistics, UNSW Sydney.

  • 2019 minipicMuyun (Ivan) Zou, Synthetic Dependence Tests Based on Deep Learning, Honours, School of Mathematics and Statistics, UNSW Sydney.

  • 2018 minipicKeren Zhang, Datathons: What Can We Gain From Them?, M.Sc., School of Mathematics and Statistics, UNSW Sydney.

  • 2018 minipicHan Wang, Geographically Weighted Sparse Generalized Principal Component Analysis, M.Sc., School of Mathematics and Statistics, UNSW Sydney.

  • 2018 minipicMelinda Mortimer, Deep Learning Methods for Lacune Detection in MRI, Honours, School of Mathematics and Statistics, UNSW Sydney.

  • 2018 minipicArdi Wira-Sudarmo, IndepoweR: An R Package to Assist Monte-Carlo Simulation of Power Analysis of Independence Tests, M.Sc., School of Mathematics and Statistics, UNSW Sydney.

  • 2018 minipicJasmine Bermas, Supervised Partial Least Squares Approach for Classification of Mass Spectrometry Data, M.Sc., School of Mathematics and Statistics, UNSW Sydney.

  • 2018 minipicMinxi Feng, Deep Partial Least Squares Regression, M.Sc., School of Mathematics and Statistics, UNSW Sydney.

  • 2018 Yunan Xu, Online PCA Algorithms with Applications of Face Recognition, M.Sc., School of Mathematics and Statistics, UNSW Sydney.

  • 2017 minipicGuillaume Boglioni Beaulieu, A consistent test of independence between random vectors, M.Sc. Department of Mathematics and Statistics, Université de Montréal, Montréal.

  • 2014 minipicIban Harlouchet, Optimisation d’algorithme d’analyse d’empreintes olfactives (In French), M.Sc. Department of Mathematics and Statistics, Université de Montréal, Montréal.

  • 2013 minipicViet Anh Tran, Le package PoweR : un outil de recherche reproductible pour faciliter les calculs de puissance de certains tests d’hypothèses au moyen de simulations de Monte Carlo (In French), M.Sc. Department of Mathematics and Statistics, Université de Montréal, Montréal.

  • 2013 minipicMarc-olivier Billette, Analyse en composantes indépendantes avec une matrice de mélange éparse (In French), M.Sc. Department of Mathematics and Statistics, Université de Montréal, Montréal.

  • 2012 Philippe Delorme, Approximations to the determination of the sample size for testing multiple hypotheses when r among m hypotheses must be significant (In French), M.Sc. Department of Mathematics and Statistics, Université de Montréal, Montréal.

  • 2008 minipicBastien Marchina, On the effect of parameter estimation in limiting \(\chi^2\) \(U\)- and \(V\)-statistics involving complex-valued components, Master 2 ICA, Grenoble Alps University.

Undergraduate Students

See my resume.

Publications

Comment about order of authors:

  • Statistics and Probability journals = strict alphabetical order of authors
  • Neuroscience or other journals = by importance of contribution, or following the rules in the field

Go to my Technical reports and to my Ph.d. Thesis.


Technical reports

[1] Lafaye de Micheaux P. Test de normalité pour les résidus d’un modèle ARMA. Master’s thesis (DEA), Université Montpellier II et École Nationale Supérieure Agronomique de Montpellier, 127 pages, June 1998.
[2] Ducharme G. and Lafaye de Micheaux P. Goodness-of-fit tests of normality for the innovations in ARMA models. Technical report number 02-02, Groupe de biostatistique et d’analyse des systèmes, Université Montpellier II, 34 pages, February 2002.
[3] Lafaye de Micheaux P. Méthodes statistiques multivariées en IRMF. Mémoire de Master 2 Recherche, Institut National Polytechnique de Grenoble, 92 pages, June 2007. (In french).
[4] Coeurjolly J.-F., Drouilhet, R., Lafaye de Micheaux P. et Robineau, J.-F. (February 2009). asympTest: an R package for performing parametric statistical tests and confidence intervals based on the central limit theorem. Technical report hal-00358375. Laboratoire Jean Kuntzmann. Université de Grenoble, 18 pages.
[5] Bordier C., Dojat, M. and Lafaye de Micheaux P. (December 2010). Temporal and Spatial Independent Component Analysis for fMRI data sets embedded in a R package, arXiv 1012.0269v1, 23 pages.
[6] Tabelow K., Clayden J.D., Lafaye de Micheaux P., Polzehl J., Schmid V.J., Whitcher B. (December 2010). Image Analysis and Statistical Inference in Neuroimaging with R, Technical report 1578, Weierstrass Institute for Applied Analysis and Stochastics, 9 pages.

[1] Test de normalité pour les résidus d’un modèle ARMA, June 1998.

DEA thesis.

Download:
Postscript format, PDF format.

Abstract:
The results contained in this document cast a new light on the important problem of testing the residuals of an ARMA model. Indeed, the validation stage when fitting a model to data, is the determinant step in selecting the best model. The Box and Jenkin’s three stages method ends with the validation step which requires the portmanteau test. An advantage of such portmanteau tests is that they pool information from the correlations at different lags. However, a real disadvantage is that they frequently fail to reject poorly fitting models. In practice, they are more useful in disqualifying unsatisfactory models than for selecting the best-fitting model among closely competing candidates.
We need to stress that if it can be assumed that the white noise process of an ARMA process is Gaussian, then stronger conclusions can be drawn from the fitted model. For example, not is it only possible to specify an estimated mean squarred error for predicted values, but asymptotic prediction confidence bounds can also be computed. This being so, we propose to replace the portmanteau test with the one we developp here which is targeted at testing normality.

Localisation:
Bibliothèque de l’Université Montpellier II.
Author : Lafaye de Micheaux, Pierre
Title : Test de normalité pour les résidus d’un modèle ARMA / Pierre Lafaye de Micheaux
Editor : Montpellier : Université Montpellier II Sciences et Techniques du Languedoc, [1998]
Collation : 127 f. ; 30 cm
Note : thèse Mém. D.E.A. : Biostatistique-Option Agron.-Santé : Montpellier 2 : 1998
Subject : Biométrie – thèses
Link: Link to bibliographical entry

[2] Goodness-of-fit tests of normality for the innovations in ARMA models, Février 2002.

Technical report number 02-02.
Groupe de Biostatistique et d’Analyse des systèmes.
Université Montpellier II

Download:
Postscript format, PDF format.

Abstract:
In this paper, we propose a goodness-of-fit test of normality for the innovations of an ARMA(p,q) model with known mean or trend. The test is based on the data-driven smooth test approach and is simple to perform. An extensive simulation study is conducted to see if, for moderate sample sizes, the test holds its level throughout the parameter space. The power of the procedure is also explored by simulation. It is found that our test is generally more powerful than existing tests while holding its level throughout most of the parameter space and thus, can be recommended. This meshes with theoretical results showing the superiority of the data-driven smooth test approach in related contexts.

[3] Méthodes statistiques multivariées en IRMF, June 2007. (In french).

Download:
Postscript format, PDF format.

Abstract:
The functional magnetic resonance imaging (fMRI) is a new neuroimaging technique that can localize neuronal activiy with a great spatial precision and with a good temporal precision. To detect activated areas in the brain, this method uses local blood oxygenation variations which are reflected by small variations in a certain kind of images obtained by magnetic resonance. The ability to obtain a functional map of the brain non invasively gives new opportunities to disentangle mysteries of the human brain. In this thesis, we describe some non parametric methods of multivariate analysis of fMRI data: Principal component analysis, Independant component analysis and Projection pursuit. We also try to explain the links between these methods, the different views one can have on them and we deal with underlying spatial and temporal aspects. We also provide a computer tool that can simulate, in a very simplified way, fMRI brain signals. This tool enable one to artificially generate fMRI data for which we control many parameters. It will serve as a basis to compare quantitatively the statistical methods presented. We also apply these various statistical methods on a real data set obtained from a human visual fMRI experiment. At least, we propose various avenues of research that could be explored to pursue this preliminary work.

[4] asympTest: an R package for performing parametric statistical tests and confidence intervals based on the central limit theorem, February 2009.

Download:
Postscript format, PDF format.

Abstract:
This paper describes an R package implementing large sample tests and confidence intervals (based on the central limit theorem) for various parameters. The one and two sample mean and variance contexts are considered. The statistics for all the tests are expressed in the same form, which facilitates their presentation. In the variance parameter cases, the asymptotic robustness of the classical tests depends on the departure of the data distribution from normality measured in terms of the kurtosis of the distribution.

[5] Temporal and Spatial Independent Component Analysis for fMRI data sets embedded in a R package, December 2010.

Download:
Postscript format, PDF format.

Abstract:
For statistical analysis of functional Magnetic Resonance Imaging (fMRI) data sets, we propose a data-driven approach based on Independent Component Analysis (ICA) im- plemented in a new version of the AnalyzeFMRI R package. For fMRI data sets, spatial dimension being much greater than temporal dimension, spatial ICA is the tractable ap- proach generally proposed. However, for some neuroscientific applications, temporal inde- pendence of source signals can be assumed and temporal ICA becomes then an attracting exploratory technique. In this work, we use a classical linear algebra result ensuring the tractability of temporal ICA. We report several experiments on synthetic data and real MRI data sets that demonstrate the potential interest of our R package.

[6] Image Analysis and Statistical Inference in Neuroimaging with R, December 2010.

Download:
Postscript format, PDF format.

Abstract:
R is a language and environment for statistical computing and graphics. It can be considered an alternative implementation of the S language developed in the 1970s and 1980s for data analysis and graphics (Becker and Chambers, 1984; Becker et al., 1988). The R language is part of the GNU project and offers versions that compile and run on almost every major operating system currently available. We highlight several R packages built specifically for the analysis of neuroimaging data in the context of functional MRI, diffusion tensor imaging, and dynamic contrast-enhanced MRI. We review their methodology and give an overview of their capabilities for neuroimaging. In addition we summarize some of the current activities in the area of neuroimaging software development in R.

Ph.D thesis

THÈSE DE DOCTORAT (82 pages) Transparents de la soutenance 193k

Auteur: Lafaye de Micheaux Pierre.
Titre: Tests d’indépendance en analyse multivariée et tests de normalité dans les modèles ARMA.
Lieu: Thèse de doctorat réalisée en cotutelle. Université Montpellier II et Université de Montréal.
Date de soutenance: 16 décembre 2002 à l’Université de Montréal.
Résumé:
On construit un test d’ajustement de la normalité pour les innovations d’un modèle ARMA(p,q) de tendance et moyenne connues, basé sur l’approche du test lisse dépendant des données et simple à appliquer. Une vaste étude de simulation est menée pour étudier ce test pour des tailles échantillonnales modérées. Notre approche est en général plus puissante que les tests existants. Le niveau est tenu sur la majeure partie de l’espace paramétrique. Cela est en accord avec les résultats théoriques montrant la supériorité de l’approche du test lisse dépendant des données dans des contextes similaires. Un test d’indépendance (ou d’indépendance sérielle) semi-paramétrique entre des sous-vecteurs de loi normale est proposé, mais sans supposer la normalité jointe de ces marginales. La statistique de test est une fonctionnelle de type Cramér-von Mises d’un processus défini à partir de la fonction caractéristique empirique. Ce processus est défini de façon similaire à celui de Ghoudi et al. (2001) construit à partir de la fonction de répartition empirique et utilisé pour tester l’indépendance entre des marginales univariées. La statistique de test peut être représentée comme une V-statistique. Il est convergent pour détecter toute forme de dépendance. La convergence faible du processus est établie. La distribution asymptotique des fonctionnelles de Cramér-von Mises est approchée par la méthode de Cornish-Fisher au moyen d’une formule de récurrence pour les cumulants et par le calcul numérique des valeurs propres dans la formule d’inversion. La statistique de test est comparée avec celle de Wilks pour l’hypothèse paramétrique d’indépendance dans le modèle MANOVA à un facteur avec effets aléatoires.

Télécharger:
[PDF A4] 574k, [PDF letter] 574k, [PS A4] 914k, [PS letter] 914k.

[PDF écran] 2.5M (avec hyperliens et programmes Fortran et C++).
Cette version PDF est très intéressante. Elle contient tous les programmes Fortran et C++ des simulations (presque 15000 lignes). Il y a aussi un programme Javascript inclus dans le premier article qui permet d’effectuer le test “en direct” (voir page 49, Javascript Application). Elle contient aussi des hyperliens facilitant la lecture. A visionner en mode plein-écran.

[MathML] 4.9M (Nécessite Mozilla ou Netscape 7.0. Temps de chargement assez long, patientez …).
[MathML.gz] 318k (Les fichiers MathML contiennent les programmes Fortran et C++).
MathML est une nouvelle technologie révolutionnaire qui permet de rechercher des expressions mathématiques dans le texte (et les copier-coller dans d’autres applications comme Mathematica). Les formules ne sont plus sous la forme d’images.

Software

https://www.datasciencemeta.com/rpackages

  • CompQuadForm (with Duchesne P.). Distribution function of quadratic forms of Gaussian random variables. cran.r-project.org/package=CompQuadForm

    Ranked among the top 5% of all R packages in terms of total downloads.

A.dep.tests dependogram
  • AnalyzeFMRI (initiated by Marchini, J.). Functions for I/O FMRI data in various formats and for treating brain images with ICA methods. Visualization. cran.r-project.org/package=AnalyzeFMRI

More Details

  • TRSbook (with Drouilhet R. and Liquet B.). Functions and Datasets to Accompany the Book “The R Software: Fundamentals of Programming and Statistical Analysis”. cran.r-project.org/package=TRSbook
  • LeLogicielR (with Drouilhet R. and Liquet B.). Functions and datasets to accompany the book “Le logiciel R: Maitriser le langage, Effectuer des analyses statistiques” (French). cran.r-project.org/package=LeLogicielR

Books

bookpic bookpic bookpic bookpic bookpic

  • The website for my books on the R software above (with B. Liquet and R. Drouilhet). Versions in French, English, Chinese and Indonesian.

  • The website for the draft of my current book on Data Science.

  • I am also in the process of writing a book entitled Statistical Inference for Complex Random Vectors (with G. Geenens and R. Cantwell).

Recent & Upcoming Talks

9th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics) (2016)
Details Slides

Australian Statistical Conference (2016)
Details Slides

Journées de STAtistique de Rennes (jSTAR), 13rd edition on Big Data (2016)
Details Slides

Australian Statistical Conference in conjunction with the Institute of Mathematical Statistics Annual Meeting (2014)
Details Slides

Grants

  • 2023-2024 UNSW Sydney’s UNSW Research Infrastructure Scheme Grant ($304,183)
    • With D. Falster, S. Nakagawa, W. Cornwell, J. Richmond, J. Lee, L. Williams, C. Foster, F. Vafaee, S. Sisson, F. Kar, D. Warton, M. Lyons, G. Abramowitz, and S. Laffan.. “Building quality R software infrastructure”
  • 2020 UNSW Sydney’s UNSW Research Infrastructure Scheme Grant ($235,453)
    • With D. Falster, S. Nakagawa, W. Cornwell, D. Navarro, J. Richmond, D. Warton, M. Lyons, G. Abramowitz, A. Ukkola, M. De Kauwe and S. Laffan. “Building quality software packages in R”
  • 2014-2019 NSERC Discovery ($70,000)
    • Lafaye de Micheaux P, “Multivariate Methods for the Treatment of High Dimensional Complex Neuroimaging Genetics Data”
  • 2013 Mitacs Accelerate Research Internships Program ($30,000)
    • Lafaye de Micheaux P, Laliberté G, Harlouchet I, _“Optimization of Olfactory Fingerprint Analysis Algorithm”
  • 2013 NSERC Research Tools and Instruments ($28,143) * Lafaye de Micheaux P (Lead CI), Granville A, Polterovich I, Patera J, Owens R, Lessard S, Murua A, Perron F, “Computational Resources for Research in Mathematics and Statistics”
  • 2010-2015 NSERC Discovery ($60,000)
    • Lafaye de Micheaux P, “Goodness-of-fit Testing and Independent Component Analysis with Applications to Cognitive Neuroscience”
  • Grenoble Institute of Technology (Bonus Qualité Recherche) grant (€122,880)
    • Achard S, Coeurjolly J-F, Lafaye de Micheaux P, Rivet B, Sato M, “MoDyC project (Modelisation of Dynamical Brain Activity)”.

Research Opportunities

⇥ RESEARCH OPPORTUNITIES FOR POSTGRADUATE STUDENTS

I consider myself a Statistician and Data Scientist. What does this mean to me? It means I am passionate about:

  • developing and applying new statistical techniques and software tools,
  • leveraging advanced mathematics and probability,
  • utilizing High-Performance Computing,
  • to address real-world practical problems,
  • often involving complex and large datasets.

I have been using Linux since 1997 kheops, including Bash scripting, sed, awk, emacs, etc. I use KATANA or GADI to analyse massive data sets or perform extensive Monte Carlo simulations. My main application interests lie in Science and Engineering, with particular expertise and enthusiasm for Neuroscience applications or IoT applications involving Raspberry Pi mini-computers. I am intrigued by any branch of Mathematics, provided you can demonstrate a connection to Statistics.

I encourage you to explore my website to get a sense of the topics I work on. Take a look at my publications as well as the Master’s and PhD theses topics studied by my former students.

Please note that I am not particularly interested in finance or economics applications. There are excellent researchers in our school who specialize in these areas, but I am not one of them. However, I could consider joint supervision if my colleague Professor Benjamin Avanzi, agrees to be part of the collaboration.

You must have a strong background in statistics, mathematics, or econometrics, along with solid programming skills—preferably in R, C/C++, or Python—with experience in Linux.

It is essential that you have studied matrix algebra, multivariate calculus, and optimization. Familiarity with reproducible research practices is also required, including tools such Seafile, Gogs and Rmarkdown.

If you are still interested in pursuing research (a Ph.D., Master’s, or Honour’s degree) with me and/or other members of our research projects and if you meet the necessary qualifications and background (see below), feel free to contact me via email. Please attach the following documents in PDF format (documents in any other format will not be considered):

  1. Your resume (including a photo—I like to put a face to a name—and a link to your personal webpage, such as LinkedIn).
  2. A copy of your academic transcripts.
  3. A description of your previous research (if applicable) in less than a page, including some technical details and its broader context.
  4. A brief statement outlining the areas or problems you would like to work on, and what attracts you to this group:
    • Why do you want to study here and with me? (e.g., refer to my publications)
    • What are your skills (e.g., computing, theory, applications)?
    • What are your main interests (e.g., computing, theory, applications)?
    • Why do you want to pursue a thesis? What are your plans afterward (e.g., academia, private sector)?
    • Discuss potential research topics you could work on with me.
  5. Contact details for at least one referee (e.g., former supervisor, teacher), including:
    • Name
    • Position
    • University
    • Email address
  6. Before our first meeting (face-to-face, phone, or video), think about the following:
    • What would you like to discuss with me?
    • How do you envision our collaboration?
    • Do you have any questions for me?

I will support your application as long as:

  • (a) you have addressed all the points outlined above,
  • (b) you are indeed an outstanding student, and
  • (c) your project proposal aligns with my research interests.

Please note that, unless specifically advertised, I do not offer scholarships myself.

So, please consult HDR Application Process, which will give you an indication of your admission eligibility and competitiveness for a scholarship.

Also read:

⇥ SCHOLARSHIP LINKS

Scholarships are available for talented and enthusiastic students pursuing a Ph.D. or a Master’s degree. However, these scholarships are highly competitive: to be considered, you must demonstrate that you are among the top students in your cohort.

If you wish to apply for a scholarship, you will need to do so separately. (Unless explicitly advertised, our group does not offer scholarships.)

⇥ HONOURS AT UNSW

If you are an undergraduate student interested in pursuing Honours in Statistics, we are looking for candidates with a strong background in statistics and solid computational skills. If this sounds like you, feel free to get in touch.

⇥ POSTDOCS AT UNSW

I expect my postdoc’s to challenge and teach and show me new things. This includes: finding interesting related literature, solving (at least partially) the methodological problems at hand, suggesting new related problems to work on and so forth. A postdoc should totally “own” their project.

My Teaching

Current

I am a teaching instructor for the following course(s) at UNSW Sydney:

2025 MATH1041: Statistics for Life and Social Science

In the past, I have taught the following courses at UNSW:

  • MATH3821: Statistical Modelling and Computing
  • MATH5806: Applied Regression Analysis

Previous years

2017 R/Shiny : Introduction to RShiny, at French National School of Statistics and Analysis of Information (ENSAI) (9h)
2016 MBDINF14 : Programming with Big Data in R using Distributed Memory, at ENSAI (45h)
2015 MBDSTA02 : Statistical Inference and Hypothesis testing, at ENSAI (18h)
STT2700 : Mathematical Statistics and Data Analysis, at Université de Montréal (39h)
STT6300 : Large Sample Techniques, at Université de Montréal (37h)
2013 STT2400 : Linear regression, at Université de Montréal (36h)
STT6415 : Regression Analysis, at Université de Montréal (42h)
2012 STT1700 : Introductory Statistics, at Université de Montréal (36h)

Industry

Consulting work for the private sector

Industrial Experience

  • 2021 Statistical consultant for Cluey Learning.

  • 2016 Statistical consultant for BNP Paribas, which is one of the largest banks in the world, with a presence in 75 countries. I applied Deep Learning algorithms.

  • 2014 University supervisor for a student working (under a Mitacs internship) at Odotech, which is an environmental company specialized in real-time monitoring of gas pollutants. Optimisation of an algorithm that analyses olfactory fingerprints.

  • 2011 Statistical consultant for Olea Medical. Identification of predictors for stroke.

  • 2011 Statistical consultant for Danone Research France. Clinical trials.

  • 2008 Co-founder member of the statistical consulting service of the SAGAG team. Operations have now ceased.

  • 2008 Shareholder and co-founder member (with J.-F. Robineau and others) of the start-up CQLS whose aim is to analyze biotechnology data. Operations have now ceased.

  • 2007 Statistical consultant for the company Minvasys, which conceives drug eluting stents. Computation of necessary sample size for clinical trials.

  • 2006–2007 Statistical consultant for the company BioArtificial Gel Technologies. This company, which was a dermo pharmaceutical canadian private company based in Montreal, developped and marketed systems for the progressive release of active agents based on a hydrogel technological springboard. Operations have now ceased.

Recent Posts

The conjecture is the following. If you can find a proof of it, please contact me!

Let \(X_1, X_2, \ldots, X_n\) be i.i.d. continuous random variables with \(E[X_i]=0,\) Var\([X_i]=1\) and \(E[\log(|X_1|)] < \infty\). Then

\[n^{-1}\sum_{i=1}^n\log|X_i-\bar{X}_n|1_{\{X_i\ne\bar{X}_n;i=1,\ldots,n\}}\stackrel{L}{\longrightarrow}E[\log|X_1|].\]

CONTINUE READING

Here are some nice references on Deep Learning:

Some summer schools with slides and videos:

CONTINUE READING

There are two great R packages:

  1. bookdown to write books using Markdown;
  2. blogdown to create blogs/websites using Hugo/Markdown.

Content can be written using Markdown, LaTeX math, and Hugo Shortcodes. Additionally, HTML may be used for advanced formatting.

CONTINUE READING

Media

Our research in the spotlight

Nothing yet!

Interesting Software

Interesting softwares under Microsoft Windows

R, R(DCOM), Acrobat Reader, Mozilla, Ghostview, Ghostscript, Antivir, Gimp, TeXLive, TeXnicCenter, Filezilla, Bitwise Tunnelier, PuTTY, WinSCP, Emacs, Openoffice, Hevea, Texconverter, Xming, rsync, Zone alarm, Lavasoft ad-aware, Spybot, Skype, PDFcreator, KPlan, VMware, Virtual Box, ImageMagick, CSDiff, vlc, Rename, Replace in Files, Gretl, writer2latex, StarDict, Jarnal, Harddisk search and stats, OOoLatex, PDF annotator, EasyPHP,Tinn-R, Cam Studio.

Interesting softwares under Linux

R, Acrobat Reader, Mozilla, Ghostview, Ghostscript, Gimp, TeXLive, Kile, Emacs, Openoffice, Hevea, rsync, Skype, ImageMagick, vlc, Konqueror, xmms, KOrganizer, KNotes, VMware, Virtual Box, Lynx, Xfig, Quanta, Gretl, writer2latex, StarDict, flpsed, Jarnal, Eqe, OOoLatex, KFileReplace, xvidcap, Gtkrecordmydesktop.

Contact

Current Position: Senior Lecturer

Affiliation: School of Mathematics and Statistics, UNSW Sydney

Meet in person: Office 2050, The Red Centre, Centre Wing, Kensington Google Map