Repoducible quantitative data analyses using frequentist and bayesian models (mixed-models, survival analysis, growth curves, nonlinear models...), as well as data wrangling, visualization, and management.
Design and coding of highly precise and user-friendly experiments (online & offline; using Python/ Psychopy, MATLAB, SR-Research...).
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I am a researcher and instructor broadly interested in learning, language, and quantitative data analysis. I am passionate about parsimonious theories and inventive methods. I am constantly working with empirical research and data science (wrangling, visualization, analyses...the whole package). I strive to do so following open science best practices. Whenever possible, I use open software and version controlled repositories that allows me to share my work in a transparent and reproducible way (R, Python, GitHub, OSF...).
My teaching is centered around active learners and problem solving. I believe that we are eager to learn things that ressonate with our personal experiences. Being trained with teams from Brazil, USA, and Canada, gives me a unique perspective of how cultural backgrounds permeate each and every step of research and teaching. I strive to welcome and recognize the diversity of experiences that each student brings to the classroom or to the lab.
In January 2024 I have joined the Dr. Marshall’s research team at the Cumming School of Medicine, University of Calgary, as a Senior Research Analyst. I am focused on Health economics data, developing sustainable data management practices, and reproducible data analysis pipelines. Prior to that, I worked as an Assistant Professor and Postdoctoral Fellow (teaching focused) at Ambrose University, Calgary. Where I taught research methods and quantitative data analysis. I also collaborated on projects involving open science in infant research and large-scale collaborations. Before that I was a Horizon postdoctoral fellow (research focused) at Concordia Infant Research Lab, Montreal, where I worked with Krista Byers-Heinlein.