When I was a postdoc at McGill University, I had the chance to teach the course BIOS602 Epidemiology:Regression models to a wonderful group of students in biostatistics. I am so thankful and glad for recently receiving the 2021-2022 Award for Excellence in Teaching Biostatistics at McGill University, an Award voted by the students in the Department of Epidemiology, Biostatistics and Occupational Health and given by the Epidemiology, Biostatistics and Occupational Health Student Society (EBOSS). I thank EBOSS for this Award, and the students with whom I had the chance to work as part of the course BIOS602. I had a great time teaching this course and this makes me even more excited to pursue my career as a Professor! I also want to thank Dr. Erica Moodie who kindly shared her course notes with me for BIOS602.
I am thrilled to announce that I will start a new position of Assistant Professor (Professeure Adjointe) at the Université de Montréal in June 2022, working in the Département de mathématiques et statistique.
I am also happy and honored to have received the Pierre Robillard Award 2022, an Award given by the Statistical Society of Canada that “recognizes the best PhD thesis defended at a Canadian university in a given year and written in the fields covered by The Canadian Journal of Statistics” (https://ssc.ca/en/award/pierre-robillard-award). I will give a talk entitled “Causal Inference on the Marginal Effect of an Exposure: Addressing Biases due to Covariate-Driven Monitoring Times and Confounders” during the SSC Annual Meeting 2022. Thanks to everyone who supported me in the past few years and made this possible, and in particular, to my PhD supervisors, Dr. Moodie and Dr. Platt.
In September 2021, I started a postdoctoral fellowship at McGill University working in collaboration with Professor Erica E. M. Moodie in the Department of Epidemiology, Biostatistics and Occupational Health.
In the Fall 2021, I was supposed to visit North Carolina State University (NCSU) for 6 months as a research scholar and to work in the Department of Statistics under the supervision of Professor Marie Davidian. Due to the covid19 pandemic, McGill University banned non-essential travels for a few months, and my collaboration turned to virtual. I am very pleased to have this opportunity.
My doctoral research was focused on causal inference, and the development (or transfer to the causal framework) of statistical methods that account for covariate-dependent monitoring times. In May 2021, I defended my doctoral thesis in biostatistics at McGill University - I had the privilege to be supervised by Professor Erica EM Moodie and co-supervised by Professor Robert Platt during my doctoral studies.
In previous work, we considered (biasing) imbalances in the data due to both confounding factors and outcome-dependent monitoring times when making causal inference on the marginal effect of an intervention on a continuous, longitudinal outcome. We also extended that work to the setting where the covariate process affecting visit times is endogenous; endogeneity may create long-term dependencies between the outcome and the monitoring processes (in press, work accepted for publication in Annals of Applied Statistics). In a more recent manuscript, we extended that work to the scenario where the exposure is continuous, and the outcome is ordinal. In that setting, a generalized inverse probability of treatment weight was used to account for confounding, and the proportional odds model was used to model the association between the exposure and the outcome. The methodology was applied to assess the marginal effect of an increase in the time spent playing video games on suicide attempts, in the Add Health study in the US.
Other broad interests are in semiparametric theory (multiply robust estimators), optimal adaptive treatment strategies, and stochastic processes/multistate model theory for causal inference.
Besides my doctoral research, in 2019-2020, I have had the chance to be involved on a project in which we developed an optimal adaptive treatment strategy for patients suffering with depression. That rule was developed with the aim to choose between citalopram and fluoxetine (two commonly prescribed antidepressant drugs) based on patients’ characteristics. See the commentary by Drs Shiner and Watts and our response, as well as a link to the McGill Reporter short interview.
More details on previous publications can be found on my Google Scholar account. Please feel free to touch base if you would be interested in a collaboration!
I hold a Bachelor’s degree in Mathematics (Université de Montréal), a Master’s degree in Statistics (Université de Montréal), and a Doctorate in Biostatistics (McGill University).
Prior to my doctoral studies, I worked for two years as an analyst statistician in the McGill Pharmacoepidemiology Unit at the Lady Davis Research Institute, Jewish General Hospital, Montreal, CA, under the supervision of Dr. Samy Suissa. There, I worked extensively with observational data (from e.g. electronic health records data and administrative databases) and I learned a great deal through conducting complex data analyses and collaborating with world-renowned experts in pharmacoepidemiology. Following this, I have become more and more interested in research questions that are of interest to the scientific community of epidemiologists and pharmacoepidemiologists.