Biography

Kabelo Mahloromela is a lecturer in the Department of Statistics at the University of Pretoria

(UP). He recently completed his PhD in Mathematical Statistics at UP, where his research

focused on spatial statistics and the analysis of spatial point patterns. His broader research

interests include machine learning and statistical methodology, particularly in the analysis of

complex spatial data. As a member of UP’s spatial statistics research group,

SpatialLab@UP, he is involved in developing and applying statistical methodology to

address challenges in fields such as criminology, epidemiology, ecology, and human

demography. Through his research and teaching, he is committed to advancing statistical

science and data analytics while contributing to the development of the next generation of

statisticians.

Beyond academia, Kabelo balances his analytical work through creative and physical

pursuits. As a self-taught pianist and newly minted runner, he appreciates both artistic

expression and the discipline of long-term challenges. Whether analysing complex data,

playing music, or logging his first kilometres on the road, Kabelo is driven by curiosity and a

commitment to lifelong learning.

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Kabelo's 2 cents (keep the change) for students pursuing statistics amidst the modern AI

hype: foundational statistics has never been more relevant.

I believe that as automated models and black-box AI tools become increasingly pervasive,

the ability to understand uncertainty, assess assumptions, evaluate evidence, and critically

interrogate models remains essential. Statisticians bring the mathematical rigour, critical

thinking, and contextual understanding needed to determine whether a model is not only

accurate but also reliable and meaningful. As the field continues to evolve, embrace the

steep learning curves that come with it and remain willing to continually expand your skills.

Look beyond theoretical formulas to tackle real-world problems, and learn to work with, not

against, modern AI while resisting the temptation to outsource your thinking to it. In my view,

deep statistical understanding is what elevates a data science practitioner from simply

running models to genuinely understanding what the data are saying.