Personal profile
Dr Thomas Wetere Tulu received his PhD from Harbin Institute of Technology(HIT) in Engineering Mathematics and worked at City University of Hong Kong(Hong Kong), Yildiz Technical University(Turkey), City University of New York, University of Macau, Beijing Institute of Mathematical Sciences, Addis Ababa University, Addis Ababa Science & Technology University and Mekelle University. His favorite quote is: " It is only through mathematics that we can thoroughly understand what true science is." - Augustus Comte
Research Interest
Biomathematics, Machine Learning, Data Science, Artificial Intelligence, Numerical Differential Equations, Epidemiology, Applied Analysis of PDE, Bioinformatics, Applied Stochastic Methods, Scientific Computing, Image Processing, Decision Theory, Optimization and High-Performance Computing
Educational Background
PhD in Engineering Mathematics, Harbin Institute of Technology, 2014-2017.
M.Sc in Computational Data Science, ictp, Italy and AAU, 2009-2011.
B.Sc in Mathematics, Mekelle University, 2002-2006
Work Experience
Assistant Professor, HIMIS, 2026 to now
Postdoctoral Research Fellow, Beijing Institute of Mathematical Sciences, 2024-2026.
Postdoctoral Research Fellow, City University of Hong Kong, 2022-2023
Postdoctoral Research Fellow, University of Macau, 2020 -2022
Assistant Professor, Addis Ababa University, 2018-2024.
Honour and Awards
City University of Hong Kong Postdoctoral Scholarship, 2022.
UM Macao Research Fellowship & UM Research Associateship UM Macao Talent programme, University of Macau, 2020.
Talented Young Scientist Program (TYSP), 2018.
TUBITAK Research Fellowship winner, Yildiz Technical University, 2017.
Outstanding International Student award, Harbin Institute of Technology, 2017
Publications(Selected)
Shibiru Temesgen, Rahel Kedir, Yu wang and Thomas Wetere Tulu*:Estimating time-to-death and determining risk predictors of heart failure patients:Statistics Innovation, 2026.
Yiming Du, Zhuotian Li, Qian He, Thomas Wetere Tulu*, Kei Hang Katie Chan,
Lin Wang, Sen Pei, Zhanwei Du, Xiao-Ke Xu∗ and Xiao Fan Liu∗ : A pre-trained deep learning model for predicting cross-immunity between drifted strains of Influenza A/H3N2: Elseviour, Journal of Automation and Intelligence, 2025.
Thomas Wetere Tulu, Tsz Kin Wan, Chun Hei Wu, Ching Long Chan, Peter
Yat Ming Woo, Cee Zhung Steven Tseng, Asmir Vodencarevic, Cristina Menni and Kei Hang Katie Chan: Machine Learning-Based Prediction of COVID-19
Mortality Using Immunological and Metabolic Biomarkers: Springer Nature: BMC Digital Health, 2023.
Tsz Kin Wan, Rui Xuan Huang, Thomas Wetere Tulu*, Jun Dong Liu, AsmirVodencarevic, Chi Wah Wong, Kei Hang Katie Chan: Identifying predictors of COVID-19 mortality using Machine learning: Life Journal, 2022.
Thomas Wetere Tulu, Tian Boping and Zunyou Wu: Modeling the effects of quarantine and vaccination on Ebola Epidemics, Springer Nature: Advances in difference equations, 2017
Thomas Wetere Tulu, Tian Boping and Zunyou Wu: Mathematical Modeling,analysis and Markov Chain Monte Carlo Simulation of Ebola Epidemics. Elseviour,Results in Physics, 2017