UJ Institute for the Future of Knowledge

Research Groups

The IFK focuses on five themes, each championed by a research group: Data Science Across Disciplines, The Future of Health, Green Futures, The Future of Diplomacy, and Metaphysics and Machines.

Data Science Across Disciplines

Head: Professor Charis Harley

The purpose of the research group, Data Science Across Disciplines, is to support the educational and intellectual growth of the field of Data Science in the region, and further afield. The Group takes an interdisciplinary approach to both the application of data science and the sense-making theoretical frameworks that shape its development. At the University of Johannesburg, the Group aims 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 DSAD drives innovation in Data Science to support UJ as the driver of the 4IR.

Upcoming Events

Data Science Across Disciplines
Wednesday 2 November 2022

Ethics and Explainability for Responsible Data Science (EE-RDS) (2022)

Data Science Across Disciplines (DSAD), a research group within the University of Johannesburg’s Institute for the Future of Knowledge (IFK), and the Perception Robotics and Intelligent Machines Research Group (PRIME) at the University of Moncton (Canada) invite you to participate in the Ethics and Explainability for Responsible Data Science (EE-RDS) Conference which will be held from 2–3 November, 2022. This event brings together reflections on both the actual and potential impact of Data Science across disciplines and sectors. Submissions are welcome from any disciplinary background, with a focus on scientific contributions, conceptual themes, and reflections within the areas of: 1. Responsible Data Science: Reliable and Trustworthy approaches for data engineering, data science and modern machine learning; 2. Algorithmic Fairness, Transparency, and Explainability; 3. Social and Ethical aspects of Responsible Data Science; and 4. Use cases illustrating the cross-disciplinary nature of the field of Data Science. This conference provides presenters with an opportunity to submit a final article for publication in IEEE Xplore.
Data Science Across Disciplines
Wednesday 2 November 2022

Ethics and Explainability for Responsible Data Science (EE-RDS) (2022)

Data Science Across Disciplines (DSAD), a research group within the University of Johannesburg’s Institute for the Future of Knowledge (IFK), and the Perception Robotics and Intelligent Machines Research Group (PRIME) at the University of Moncton (Canada) invite you to participate in the Ethics and Explainability for Responsible Data Science (EE-RDS) Conference which will be held from 2–3 November, 2022. This event brings together reflections on both the actual and potential impact of Data Science across disciplines and sectors. Submissions are welcome from any disciplinary background, with a focus on scientific contributions, conceptual themes, and reflections within the areas of: 1. Responsible Data Science: Reliable and Trustworthy approaches for data engineering, data science and modern machine learning; 2. Algorithmic Fairness, Transparency, and Explainability; 3. Social and Ethical aspects of Responsible Data Science; and 4. Use cases illustrating the cross-disciplinary nature of the field of Data Science. This conference provides presenters with an opportunity to submit a final article for publication in IEEE Xplore.

Our People

Pushpendra Kumar

Research Associate

Pushpendra Kumar is a young mathematician from India. He holds a master's degree from the Central University of Punjab, Bathinda. He has published nearly 50 research papers in more than 20 reputed international journals. His research works are related to the mathematical modelling of various real-world problems, fractional calculus, and computational methods. More specifically, Mr Kumar has done novel work on the modelling of infectious diseases. He is a reviewer for several international journals.

Pushpendra Kumar

Research Associate

Charis Harley

Head of Data Science Across Disciplines

Professor Charis Harley is Head of the Data Science Across Disciplines Research Group at the IFK and 4IR Professor in the Faculty of Engineering and the Built Environment at UJ. She is an accomplished researcher in the field of computational mathematics with over 30 articles, experience at a range of elite universities, and multiple research grants. She also has experience in industry as an analyst and quantitative analyst, with practise in data analysis ranging from the use of filters; Auto-Correlation; Principal Component Analysis; Exploratory Data Analysis, to the use of more advanced methods of data analytics.

Charis Harley

Head of Data Science Across Disciplines

Pushpendra Kumar

Research Associate

Pushpendra Kumar is a young mathematician from India. He holds a master's degree from the Central University of Punjab, Bathinda. He has published nearly 50 research papers in more than 20 reputed international journals. His research works are related to the mathematical modelling of various real-world problems, fractional calculus, and computational methods. More specifically, Mr Kumar has done novel work on the modelling of infectious diseases. He is a reviewer for several international journals.

Pushpendra Kumar

Research Associate

Charis Harley

Head of Data Science Across Disciplines

Professor Charis Harley is Head of the Data Science Across Disciplines Research Group at the IFK and 4IR Professor in the Faculty of Engineering and the Built Environment at UJ. She is an accomplished researcher in the field of computational mathematics with over 30 articles, experience at a range of elite universities, and multiple research grants. She also has experience in industry as an analyst and quantitative analyst, with practise in data analysis ranging from the use of filters; Auto-Correlation; Principal Component Analysis; Exploratory Data Analysis, to the use of more advanced methods of data analytics.

Charis Harley

Head of Data Science Across Disciplines

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