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Prof Fernando Buarque, Visiting Professor, IIS, UJ

Fernando Buarque de Lima Neto is Brazilian, married, one lovely daughter and “globetrotter-bookworm-DIY-motorbiker guy” and Fellow of the Academy of Science of Pernambuco-Brazil. A short biosketch can be downloaded here.

Mr Adedayo M. Farayola

Adedayo M. Farayola is a PhD candidate (researcher) at the department of Electrical and Electronics Engineering Science (Auckland Park Campus), University of Johannesburg, South Africa. His area of research includes power systems optimisation, machine learning, artificial intelligence, renewable energy systems and smart grid. He is currently working on the optimisation of power from photovoltaic systems using artificial intelligence (machine learning techniques). These machine learning techniques (metaheuristic techniques) will be applied on photovoltaic systems under both uniform irradiance and partial shading conditions with the aim of extracting maximum power and thus reduce power losses commonly experienced with photovoltaic systems under partial shading conditions.


Mr Patrick S. Pouabe Eboule

Patrick S. Pouabe Eboule was born in Cameroon in 1987. He is a Ph.D. candidate in Electrical and Electronic Engineering at the University of Johannesburg. He received its M.Eng. degree in electrical and electronic engineering at the University of Johannesburg, South Africa, in Sept 2017. His Master research was based on fault detection in power transmission line very high voltage using artificial intelligence techniques. For his PhD degree, he studied the implementation of a nine-phase power transmission line using Matlab/Simulink and the use of artificial intelligence techniques to detect, classify and locate faults in such a power transmission line. He is interested in power systems, machine learning, artificial intelligence. Patrick EBOULE is an engineer, member of the Institute of Intelligent Systems (IIS) in the school of electrical and electronic engineering, University of Johannesburg, member of the Engineering Council of South Africa (ECSA), member of the Association for Computing Machinery (ACM) in the Institute of Electrical and Electronics Engineers (IEEE).


Mr Thembinkosi Nkonyana

Thembinkosi Nkonyana is currently pursuing his PHD degree in Artificial intelligence at the University of Johannesburg. His research has been focused in application of Artificial Intelligence, Machine Learning, and Deep Learning in Remote Sensing and Big Data. He has published in reputable ISI conferences, Scopus Indexed, IEEE Explorer, and Springer Book chapters. In the era of Forth Industrial Revolution (4IR), advanced algorithms are needed for analysis and manipulation of Big Data as is it Unstructured, Semi Structured or Structured. His latest research is to conduct studies on how to apply Big Data Analytics, Artificial Intelligence, Machine Learning, and Deep Learning to solve complex problems which help in planning and decision making that is based on data driven information. His research interest also include arears of Internet of Things (IoT), Drone Technology, Predictive Maintenance, Blockchain, Cloud Computing, Edge Computing, Fog computing and Dew computing.


Prof. Yanxia Sun

Prof. Yanxia Sun received her joint qualification: DTech in Electrical Engineering, Tshwane University of Technology, South Africa and PhD in Computer Science, University Paris-EST, France in 2012. She has therefore an approach that brings together computing and electrical engineering.

She has more than 10 years teaching and research experience. Currently she is serving as an Associate Professor and Head of Department: Electrical and Electronic Engineering Science (2015 Dec. to 2018 Nov.), University of Johannesburg, South Africa. She has lectured seven courses in the universities. Currently she is supervising fifteen D-Tech/PhD students and six MTech/MSc students in the areas of Electrical Engineering.

Yanxia Sun has 67 papers published/accepted including 25 ISI master indexed journal papers. She was awarded the National Research Foundation (NRF) rating Y2 (2015-2020) and obtained the NRF incentive grant. She is an IEEE and South African Young Academy of Science (SAYAS) member. She is/was the principal or co-principle investigator of 11 projects including national research grants and industrial projects.

For example, I am the principle investigator for one industrial project, using the data provided from the company to develop an algorithm that can predict the “end of life” (predictive maintenance) of sensor ; and determine whether a sensor has been poisoned” instead of reaching “end of life”.

Related the Industry 4.0, I am the co-investigator of the NRF GRANT for 2018-2020: High Tech Automated Real-time SCADA System for Bottling Process Control Using Basic Industry 4.0 Concept. In this project, we develop a high tech automated bottling process, with very little human intervention, using the advantages of today’s current trend of automation, the Industry 4.0, known as “smart factory”.


Mr Dustin van der Haar

Dustin van der Haar is an Associate Professor in the Academy of Computer Science and Software Engineering at the University of Johannesburg. He has published articles both nationally and internationally in pattern recognition. His research interests include biometrics, computer vision and machine learning and have collaborated with multiple universities. The vision of his research intends on empowering users by bridging the gap of interaction between people and technology in their daily lifestyle using pattern recognition.

He is currently engaged in the following projects:

  • Using sentiment analysis for language learning teaching (with the University of Aizu, Japan)
  • Using computer vision-based food recognition to derive nutritional value (with Western Sydney University Australia)
  • Face antispoofing methods for face recognition systems
  • Sports and recreation assistance using computer vision
  • Threat and violence detection in public spaces using computer vision
  • Computer Assisted Diagnostics in healthcare
  • Fraud and threat detection using machine learning
  • Source code plagiarism detection using n-grams and Jaccard indices

Mr Nnamdi Nwulu

Nnamdi Nwulu is a researcher, educationist and engineer. He holds BSc and MSc degrees in Electrical & Electronic Engineering and a PhD degree in Electrical Engineering. He is currently an Associate Professor in the Department of Electrical & Electronic Engineering Science at the University of Johannesburg. He is also a Professional Engineer registered with the Engineering Council of South Africa (ECSA), a Senior Research Associate in the SARChI Chair in Innovation Studies at the Tshwane University of Science and Technology and Associate Editor of the African Journal of Science, Technology, Innovation and Development (AJSTID). His recent research interests include application of mathematical optimization techniques and machine learning algorithms in the Food, Energy and Water spheres.


Prof. Suné von Solms

Prof. Suné von Solms is an Associate Professor at the Faculty of Engineering and the Built Environment at the University of Johannesburg, South Africa. Suné is a registered professional engineer with the Engineering Council of South Africa (ECSA) and a National Research Foundation (NRF) rated researcher. Her research interests include networks, engineering education and the social and human aspects of engineering.

She is actively involved in engineering and community engagement projects within rural communities. Suné is part of a research team responsible for developing and maintaining relationships with partners in rural communities, exploring the needs of those partners, and designing and developing solutions for the rural communities. Currently, she is involved in the establishment of a Smart Rural Village in Limpopo, where the technologies and benefits of Industry 4.0 is brought to a rural community to assist in rural development. The team is exploring how connecting a village with modern information and smart data collection methods can support livelihood opportunities, provision of services (healthcare, education, clean water, and sanitation), empowerment and sustainability.

Suné is also involved in research relating to skills and competency development of the Industry 4.0- ready engineer. The education needs of engineers working in modern environments will change as the landscape adapt to the Industry 4.0 changes. Currently, her research looks at the cybersecurity-related knowledge and skills which will be required by an engineer in the light of Industry 4.0.


Tawanda Mushiri

Born on 4 April 1983, Tawanda Mushiri is a holder of BSc Mechanical Engineering (UZ), Master of Science in Manufacturing Systems and Operations Management (MSOM) (UZ) a D.Eng at U.J. of machinery monitoring systems. He is currently a lecturer a Senior Research Associate at the University of Johannesburg in the Department of Quality and Operations Management, also a Senior Lecturer at the University of Zimbabwe teaching Machine Dynamics, Robotics, Solid Mechanics and Finite Element Analysis. His PhD thesis was on fuzzy logic and Condition Monitoring Techniques to machinery which is under Industry 4.0. He is the Chairman of Research in the Faculty of Engineering at UZ and is into Industry 4.0 research group. His research activities and interests are in Artificial intelligence, Robotics, IoT and Dynamics. He has also published 2 books, 3 chapters in a book, 13 journals in highly accredited publishers and 80 conferences in peer reviewed publishers. He has done a lot of commercial projects at the University of Zimbabwe. He is a reviewer of 4 journals highly accredited. He has been invited as a keynote speaker in workshops and seminars.

Tawanda has supervised 165 students’ undergraduate projects and 5 Masters Student to completion and 2 PhD students in progress. Beyond work and at a personal level, Tawanda enjoys spending time with family, travelling and watching soccer.