I am currently a final year honours student majoring in psychology at SUSS.

How can I help others? Like most psychology undergraduates, this question initially led me toward a career in clinical practice. It became readily apparent however, that there remain significant gaps in our field’s statistical rigour and methodological pedagogy, constraining our capacity for studying and intervening in the richness of complex human behaviour. The drilling of the biopsychosocial framework throughout undergraduate curricula reflects our field’s acknowledgement that the processes undergirding such emergent phenomena are dynamically interrelated components of some wider system; yet our field’s overreliance on traditional inferential paradigms often discards this complexity by reducing individuals to group-level averages, assuming ergodicity as the rule rather than the exception. Driven by this insight and a growing appreciation for quantitative methods, I was motivated to explore dynamical systems theory, develop (R, Python) programming skills, and further my understanding of traditional frameworks’ (e.g., SEM, GLM) limitations.

Currently, I address analytical demands on “big data” sets as a research assistant in PQRST Lab under the NCSS-NAK 360 Panel Study; our unpublished work informing ministerial policies on gender disparities in unpaid work, caregiving, and quality of life. Most notably, my applications using mixed graphical models culminated in my inaugural first-author presentation at the Conference on Complex Systems and was further acknowledged with a Student Travel Award covering all expenses. Attending the conference cemented my interest in dynamical systems and highlighted the infancy of network analytic methods in psychology, where concepts like higher order networks (Bianconi, 2021), temporal network theory (Holme & Saramäki, 2023), and uncertainty quantification (Grigoriu, 2012) have yet to be thoroughly adapted for our field. Since then, the psychology faculty at SUSS has afforded me invaluable opportunities to develop, apply, and refine my statistical expertise across intradisciplinary domains, including neuroscience, social psychology, and bilingualism.

Collaborating on these projects further underscored the sine qua non of quantitative mavens in psychology and the perpetual demand for methodological innovation in advancing our field; a role I intend to contribute to. A PhD in Quantitative Psychology represents the natural next step, providing the formal guidance and environment necessary to tackle problems on improving the methods we use rather than just applying them. Specifically, I am interested in addressing the dependence of existing methods on the chosen sampling frame and time intervals, which econometricians elucidated as stemming from “the discrete nature of our observations” (van Monfort et al., 2018). Crucially, continuous time modelling has been argued to be better addressed with (stochastic) differential equations (SDEs; Chen et al., 2023; Chow et al., 2018) as compared to unnecessary discretising in existing vector autoregressive models.

Finally, with a pragmatic philosophical stance, I also draw inspiration from unconventional sources to address practical issues. Key examples being: an independent qualitative study on psychology undergraduates’ experiences learning quantitative methods; and prototyping a physical slider response box for psychological experiments with an Arduino microcontroller. Outside of academics, I was an EXCO member of SUSS’ student psychology society, PsyConnect, striving to enhance psychology (and statistics) education for fellow undergraduates. In my free time, I enjoy gaming (lately Monster Hunter Wilds, Clair Obscur, Stellaris, Snowrunner, Dying Light 2 …), listening to K-Hip-Hop, and drinking tea (my favourites are pu-er, jasmine and matcha).