Prof. Henry Mwambi, Course Oversight, Co-Principal Investigator University of Kawzulu-Natal

Associate Professor, School of Mathematics, Statistics, and Computer Science, UKZN, South Africa.
Email: mwambih@ukzn.ac.za

Henry Mwambi is a Professor at the University of KwaZulu-Natal. Leads research on applied statistics, biostatistics and infectious disease modelling at the individual and population level. Prof Mwambi’s main areas of research areas are in applied statistics, biostatistics and infectious disease modelling at the individual and population level. At the University of KwaZulu-Natal, he has taught theory and applied courses, among them a Biostatistics course covering key areas in biostatistics, namely general epidemiology principles, cohort studies, case-control studies, survival analysis and clinical trials. He is currently working with and has supervised a number of PhD and Masters students on various topics in biostatistics and epidemiology such as the analysis of non-Gaussian longitudinal and clustered disease outcome data, survival analysis, modelling recurrent events, longitudinal data analysis including missing data, and infectious disease modelling. Some of the students he has supervised hold senior positions as Biostatisticians and academics in leading medical and bioinformatics research institutes and centres and universities within South Africa, sub-Saharan Africa and abroad. He has authored and co-authored over 100 journal articles in reputable peer-reviewed international journals.

He is keen and passionate to develop and help enhance biostatistics and data science capacity in the Sub-Saharan Africa region and Africa at large. He is currently a Co-PI of a number of landmark projects among them the Sub-Saharan Africa Consortium for Advanced Biostatistics (SSACAB) Training Programme whose aim is to enhance capacity in Biostatistics through MSc, PhD and Postdoctoral training by involving both academic and research institutions to provide supervision and mentorship of fellows. Visit Prof. Mwambi's website for more information about his work.

Dr. Santiago Romero-Brufau, Lead Facilitator

Adjunct Assistant Professor, Department of Biostatistics, Harvard T.H. Chan School of Public Health, USA.
Assistant Professor of Otorhinolaryngology and Healthcare Systems Engineering, Mayo Clinic, USA.
Email: santiagoromerobrufau@hsph.harvard.edu

Santiago Romero-Brufau MD, PhD has a background in Medicine, Healthcare Systems Engineering, Informatics and Health Data Science. He is Assistant Professor of Otorhinolaryngology, and Healthcare Systems Engineering at Mayo Clinic. He is also Adjunct Assistant Professor of Biostatistics at the Harvard T. H. Chan School of Public Health, where he serves in the executive committee and teaches two of the core courses in the Master’s in Health Data Science. Dr. Romero-Brufau’s work is centered on developing and implementing machine-learning models and other data science solutions into clinical practice. His focus is on real-world applications as well as how to integrate advanced solutions into the clinical workflow and bridging the gap between model development and clinical and public health impact. Visit Dr. Romero-Brufau's website for more information about his work.

Dr. Sandra Barteit, Co-lead Facilitator, Training Director HIGH

Heidelberg Institute of Global Health, Germany.
Email: barteit@uni-heidelberg.de

My enthusiasm for Global Health led me to join the Heidelberg University Institute of Global Health as a full-time researcher in 2015. Currently, amongst many other projects, I am leading a project that implements novel measurements of wearables in vulnerable populations in Kenya and Burkina Faso to conduct cutting-edge climate change and health research, which helpd us gain new insights into the disease burden in poor populations facing exposure to climate change, such as heat or droughts. Furthermore, I lead the Blended Learning in Zambia (BLiZ) project that implements blended learning to strengthen medical education at the largest medical University in Zambia. In my research, I apply a variety of analytics models and cross-cutting analytics modeling concepts and cases. Visit Dr. Barteit's website for more information about her work.

Dr. Mandlenkosi Gwetu, Facilitator

Senior Lecturer and Academic Leader for Computer Science, UKZN, South Africa.
Email: Gwetum@ukzn.ac.za

Mandla Gwetu is a Computer Scientist with over 10 years of academic experience. He is also an alumnus of the Heidelberg Laureate Forum and holds a Doctoral Degree in Computer Science. He currently serves as the Academic Leader for Computer Science at UKZN. His research focus lies mainly in the computational aspects of computer vision and machine learning. He currently supervises several PhD & MSc students in these areas. Mandla holds industry certifications in Java and AWS. He is an experienced reviewer of several Computer Science Journal and international conferences. Visit Dr. Gwetu's website for more information about his work.

Dr. Palwasha Khan, Facilitator

Head of informatics at African Health Research Institute, Data Science Unit, South Africa.
Clinical Associate Professor in Infectious Disease Epidemiology at London School of Hygiene & Tropical Medicine.
Email: palwasha.khan@ahri.org

Palwasha Khan is a Clinical Associate Professor in Infectious Disease Epidemiology at London School of Hygiene & Tropical Medicine and a clinical epidemiologist with Interactive Research & Development primarily working at the clinical academia-implementation interface. She is currently seconded to the Africa Health Research Institute (AHRI) in Kwa-Zulu Natal as head of the Health Informatics section of the AHRI Data Science Unit. Her research interests include combining field epidemiological studies and programmatic electronic health data to further understanding of Mycobacterium tuberculosis transmission at a population-level. Visit Dr. Khan's website for more information about her work.

Dr. Gabriel Kallah-Dagadu, Teaching Assistant

WASHU Takwimu Postdoctoral Fellow, UKZN, South Africa.
Email: kallahdagadug@ukzn.ac.za

Gabriel Kallah-Dagadu holds a Ph.D. in Statistics from the University of Cape Coast, Ghana. Gabriel is Lecturer at the University of Ghana and has eight years of teaching and supervising experience in Statistics, Probability, and Data Science. He is currently a postdoctoral fellow in the DSI-Africa Training program on Health Data Science project hosted by the University of KwaZulu-Natal, South Africa, Harvard T. H. Chan School of Public Health, USA, and Heidelberg Institute of Global Health, Germany. His research interest are centered on applied probability, computational statistics, and machine learning with real-life applications to health, climate change, and finance. He has published scientific and peer-reviewed articles in local and international journals. Visit Dr. Kallah-Dagadu's website for more information about his work.

Dr. Mohanad Mohammed, Teaching Assistant

WASHU Takwimu Postdoctoral Fellow, UKZN, South Africa.
Email: MohammedmM1@ukzn.ac.za

Mohanad Mohammed earned his PhD from the School of Mathematics, Statistics, and Computer Science (SMSCS), specializing in Statistics at the University of KwaZulu-Natal (UKZN) in 2022. During his MSc and PhD studies, he worked as a tutor and an Adhoc lecturer in SMSCS at the same university, which continued from 2019 to 2022. He is currently a post-doc fellow working on a health data science project hosted by UKZN in collaboration with Harvard T. H. Chan, School of Public Health, USA, and Heidelberg University and Germany. His research has focused on developing and applying statistical methods in genomics, genetics, public health, and the environment. He is interested in contributing to a deeper understanding of cancer disease modeling using gene expression data to facilitate decision-making concerning diagnosis, treatment, and care. In addition, Mohammed has authored and co-authored many articles, ten of which have been published in reputable journals and conference papers. He has attended and presented at various international and local workshops and conferences and is an active member of the biostatistics team under the MASAMU program. Visit Dr. Mohammed's website for more information about his work.

Dr. Kennedy Chengeta, Teaching Assistant

Postdoctoral Researcher in Climate Change AI & Renewable Energy, School of Mathematics, Statistics and Computer Science, UKZN, South Africa.
Email:

Kennedy is a PostDoc Researcher in Climate Change AI focusing on Renewable Energy. He holds Phd in Computer Science/Artificial Intelligence and Computer Vision, three masters degrees in Advanced Software Engineering (Leicester, UK), ICT Management(University of Pretoria) and in Finance and Investments (NUST). He is also currently finalising dual Masters in engineering in Telecommunications, Autonomous and Self Driving Cars with University of Cape Town. He has years of FinTech, Automation (IBM, Camunda, Webmethods, Lotus Notes), Artificial Intelligence and Cloud Experience with banks, insurance and oil companies in South Africa and Zimbabwe. Visit Dr. Chengeta's website for more information about his work.

Dr. Ashenafi Yirga, Teaching Assistant

WASHU Takwimu Postdoctoral Fellow, UKZN, South Africa.
Email: yirgaa@ukzn.ac.za

scholar as part of the DS-I Africa Trainee program. He obtained his BSc degree in Statistics from Addis Ababa University, Ethiopia. He completed his BSc Honors degree, Master of Science, and Ph.D. in Statistics at the University of KwaZulu-Natal, South Africa. Ashenafi authored several articles, seven of which have been published in ISI-indexed and/or Q1-rated journals. Throughout his postgraduate study years, he consulted and worked as an assistant lecturer for undergraduate and postgraduate students. He also presented his work locally and internationally at conferences and attended several workshops and courses. Ashenafi is interested in being involved in biomedical, nutrition, HIV/AIDS and/or infectious diseases, socio-economical, demographic, epidemiological studies, and health data science in general. He is particularly interested in contributing to the growing body of research on public health that will improve the quality of life of affected individuals and the population at large. Visit Dr. Yirga's website for more information about his work.