PhD student HSRI: Lisette Sibald

Can you tell us something about you?

My name is Lisette Sibbald. I am 26 years old, currently live in Utrecht, and enjoy playing field hockey in my spare time. Before starting my PhD in 2022, I completed a Master's program in Behavioral Data Science at the University of Amsterdam and a Bachelor's program in Psychology at Utrecht University. Given my dual passion for data science and (clinical) psychology, this PhD project offers the perfect convergence for my interests.

Did cross-departmental research play a role during your bachelor's/master's studies? To which extent?

During my academic journey, I didn't actively engage in cross-departmental research in the traditional sense. However, the interdisciplinary nature of my studies, particularly during my Master's program, was unmistakably evident. Such an approach is vital for the accurate prediction of human behavior and psychopathology. Through psychology, we grasp the nuances and motivations behind human actions and emotions. This understanding helps assess the validity of our data, ensuring that we measure and predict what we genuinely intend to. Programming enables the effective implementation of prediction models, while mathematics lays the foundation for rigorous analysis, guaranteeing the precision and reliability of our predictions. Together, these disciplines form a comprehensive framework that, in my view, is indispensable for in-depth studies focused on understanding and predicting human behavior and psychopathology.

What excited you about this multidisciplinary project you applied for?

During my Master's program, there was an expectation that students had a solid grounding in psychology. Hence, the curriculum heavily emphasized enhancing our programming skills and delving deeper into mathematical concepts. Although I enjoyed this focus, I felt a gap in deepening my insights into human behavior, especially psychopathology. This PhD project offers an opportunity to broaden my understanding in both areas and make meaningful contributions to women's (mental) health, a subject I find very important.

Why is cross-departmental research important for your PhD project? What does it add to the project?

My PhD project, centered on predicting postpartum depression, greatly benefits from the expertise from multiple departments: Cognitive Neuropsychology, Methodology & Statistics and Tranzo. The Cognitive Neuropsychology department possesses extensive in-house knowledge on maternal perinatal mental health. This builds the foundation for our statistical models. Transitioning from this foundational knowledge to its application, the Methodology & Statistics department steps in. A part of this project is about predicting postpartum depression with advanced statistical methods that have not been used for this goal before. The expertise of the Methodology & Statistics department plays a crucial role in achieving this. Yet, a bridge between the theoretical and the practical is vital. This is where the Tranzo department expertise compliments the project. Tranzo is dedicated to building a bridge between science and real-world care. They ensure that our machine learning predictions have implications and truly benefit women at risk of developing postpartum depression.

What do you like about cross-departmental research?

What I like about cross-departmental research so far is that I get to learn from many interesting academics from multiple fields and see how different departments operate. Each department brings to the table its distinct methodologies and perspectives. It is inspiring to be able to learn from them and apply methods from one discipline to another discipline.