This is our last feature for our 1st year doctoral students! We hope everyone has been able to get to know this great new group.
I am Isabella Nogues (though my given name is Isabelle-Emmanuella, I generally go by Isabella). I was born and raised in the Washington, D.C metro area. My parents being originally from Côte d’Ivoire, I am fluent in French, and attended a French International School from kindergarten through the twelfth grade.
I then went on to Princeton University, where I obtained a bachelor’s degree in mathematics. Along with mathematics, I developed a strong interest in biomedical topics, and sought to work in a field where mathematical concepts and biological phenomena converged. That interest evolved during my studies in the Integrated Science Curriculum at Princeton, an unconventional interdisciplinary approach to Mathematics, Computer Science, Physics, Chemistry, and Biology that explored and provided insights into the connection between mathematics and practical sciences.
Shortly after graduation, I joined the Radiology and Imaging Sciences Computer-Aided Detection (CAD) Laboratory at NIH. During my two-year fellowship in the CAD lab, I contributed to eye-opening research in statistics applied to medical imaging problems.
I am very much looking forward to joining the department of Biostatistics at Harvard, as it will allow me to continue such biomedical and statistical research with more precision and rigor, and to potentially tackle unresolved real life science problems. My PhD studies will allow me to utilize my math skills to tackle unresolved real life sciences issue, and to participate in the development of tools needed to address such problems with greater accuracy.
My principal hobbies include music – as an avid, classically trained violinist, foreign languages – namely fluent Italian and conversational Chinese as well as Latin, reading, distance running, Sudoku, and traveling.
I was born and raised in Montreal, Canada, where I received my undergraduate degree in Pharmacology, and completed a master’s degree in Biostatistics at McGill University. In my master’s at McGill, I became interested in the theory of causal inference, especially in how it elucidates the necessary assumptions for making causal conclusions from observational data. My master’s thesis was a large simulation study that compared a collection of methods for estimating causal treatment effects in repeated measures data in the presence of time-dependent confounders. At Harvard, I am eager to learn from and work with the leading causal inference experts. Beyond this, I am interested in learning about semiparametric theory, statistical and machine learning algorithms, clinical trial design, as well as applying statistical methods to interesting medical and epidemiological data, and I am excited to see how my research interests evolve in my time in the program.
Outside of academics, I enjoy playing sports, including baseball, badminton, squash and table tennis. I also pursue a few amateur hobbies in my spare time, such as speed solving Rubik’s cubes, and playing chess. Finally, I perform my civic duty as a Canadian by rooting for my hometown hockey team, the Montreal Canadiens.