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  • Dr Fred Kaggwa (Participant)


    Dr. ir. Fred Kaggwa Holds a PhD in Computing and serves as a Lecturer in the Department of Computer Science, Faculty of Computing and Informatics at Mbarara University of Science and Technology. Fred also doubles as the Chairperson of the University's Quality Assurance Committee with an experience of 14 years in the quality assurance matters of the university. Dr. Kaggwa's research interests are in Data Science for healthcare (including big data security), Biometrics, Computer/Network Security, Legal, Social and Ethical Issues in Computing as well as Quality Assurance in Higher Education. Fred supports the University Computing Services Unit to not only embrace new technologies, but also have them securely operate. He was delighted to be among the initiators of the data science research group which is at its early development stages for both staff and students. This research group has been started to bridge the data science gap that has existed for long at the university. Fred has a strong history of teaching computer security related courses, modeling and simulation and post graduate supervision/ mentorship. Since 2007, he has taught over 1000 undergraduate Information technology, Computer Science and Computer Engineering students. 10 Masters students and 1 PhD student completed and graduated under his guidance and he is currently supervising and mentoring close to 10 graduate students. Fred was also the lead guide of the textbook chapter from Mbarara University of Science and Technology released under an open access license. This textbook "Leveraging Data Science for Global Health" was published in August 2020 and with funding from MIT Libraries, the digital copy of the book is available for free. Their chapter that highlights the data science innovations, challenges and limitations towards real-world implementations in global health, has been downloaded 3600 times since its publication. Fred is the co-investigator for the Data Management and Analysis Core under the Mbarara University Data Science Research Hub (MUDSReH).