I am currently accepting applications from potential postdoctoral researchers.

Experience in the statistical aspects of causal inference, meta-analysis, or a field related to my areas of interest is necessary. Postdoctoral researchers at UdeM are unionized and salary and benefits will be commensurate with the collective agreement.

Please contact me by email if interested. Include your CV and a statement of interest. Specifically, it is important to indicate how your expertise and interests are related to my work.

Earlier this summer, I was honoured with a Canada Research Chair (CIHR, tier 2).

https://nouvelles.umontreal.ca/article/2019/06/18/l-udem-obtient-vingt-nouvelles-chaires-de-recherche-du-canada-et-huit-renouvellements/

The name of the chair is "Causal Inference and Machine Learning".

I am deeply appreciative of the support of Daniel Lévesque and the Faculté de pharmacie who nominated me and continue to promote and encourage my research.

Underlying my nomination was an initiative to make the CRC program more representative by encouraging the nomination of women, indigenous people, and visible minorities. I am very grateful to be able to benefit from such a program and also of the continued efforts to encourage a diversity of voices in academia.

I'm very happy to say that I received a CIHR Project Grant with Andrea Benedetti (McGill) for our project:

"Evaluating treatment effect heterogeneity and optimal regimens in multidrug resistant tuberculosis using causal inference modeling".

Résumé en Français

Lay Abstract:
With the increase in antibiotic resistance, the treatment of certain strains of tuberculosis (TB) is becoming more challenging. TB caused 1.4 million deaths in 2014 making it the single most deadly bacterial infection worldwide. Multidrug resistant tuberculosis (MDR-TB) is a condition afflicting an estimated 480 000 globally, and is defined as TB that is resistant to the primary antibiotics used to fight the disease. MDR-TB is treated using different combinations of alternative antibiotics. What is not known is which types of patients respond better to which combinations of antibiotics, and which patient characteristics are most closely related to successful treatment. In order to use statistics to answer this question, a large amount of data is needed. Previous studies (with limited numbers of patients sharing their data) did not have enough information for investigators to unravel the relationships between patient characteristics and the probability of curing the disease. The objectives of our study are to 1) Learn which patient characteristics affect treatment success for different combinations of antibiotics, and 2) Design treatment strategies to maximize the success of treatment for MDR-TB. Our team has previously performed a large review of existing studies and has combined all available data into a much larger dataset. This dataset will allow us to investigate which patient characteristics (such as age, sex and antibiotic resistance) can help predict the success of specific treatments. We will use this information to statistically construct patient-specific treatment guidelines, which will ideally be used by doctors to treat their patients.

We greatly appreciate the support of CIHR and are excited to continue our work on this project!