Research Groups
The IFK focuses on five themes, each championed by a Research Group, which drives topical research and organises relevant events.
Data Science Across Disciplines
Head: Professor Charis Harley
The purpose of the research group, Data Across Disciplines, is to support the educational and intellectual growth of the field of Data Science at the University of Johannesburg. We aim to develop much needed, specialist educational programmes at postgraduate level, to support not only student skills development but the upskilling of current professionals in the field. Through cutting edge research, product development, and close collaboration with industry partners and professionals we will drive innovation in Data Science to support UJ as the driver of the 4IR.
The Future of
Health
Head: Professor Ben Smart
The Future of Medicine Research Group takes an interdisciplinary approach to contemporary medical research. Its broad mandate ranges from studying existing medical crises and the management thereof, to examining and critiquing the potential medical techniques and strategies of the future.
Green
Futures
Head: Professor Brett Bennett
Green Futures seeks to positively understand, appreciate and harness earth’s plant life and the socio- ecosystems they interact with for the betterment of humanity and nature. Green Futures draws on a range of disciplines, not limited to science, and has a strong grounding in culture, history, and policy.
The Future of Diplomacy
Head: Dr Oluwaseun Tella
The Future of Diplomacy research group seeks to promote the understanding of diplomacy, negotiation and statecraft in contemporary international politics. The group aims to support research in modern diplomatic practice by redefining diplomacy in contemporary and futuristic contexts in an increasingly complex and globalised world. Events
Metaphysics and
Machines
Head: Professor Alex Broadbent​
Machine learning is altering how we understand the world. The Metaphysics and Machines Research Group explores these implications of machine learning, including how ML changes or deviates from traditional scientific methodologies, whether ML can help develop new or alternative conceptual frameworks, and what ML can tell us about the relationship between prediction, explanation and causation.
