ELIMBOTO M. YOHANA

Lecturer, Dar Es Salaam University College Of Education

Education:

Ph.D. in Applied Mathematics (Cosmology)

Teaching:

• Abstract Algebra
• Functional Analysis
• Partial/Ordinary Differential Equations
• Linear Algebra

Research:

Synergy of Mathematics, Physical and Computational Sciences to address forecasting, analysis and the modeling challenges in surveying extremely largescale structures of the universe to consequently, constrain various cosmological parameters, such as composition densities of dark energy and dark matter in the universe, and asses the universe's expansion and growth rates.

Projects:

• Web-based System for Examination Invigilation Timetable Generation in Higher Learning Institutions. Researchers: Michael Joseph Ryoba (PI), Linus John, Elimboto Yohana and Cecilia Swai. Amount: 20,000,000.00 TZS. Duration: 1 year (April 2023 to March 2024). Funding: UDSM the 5^th Call for competitive research and innovation grants 2022/20223.
• Open Source Software and Web-based Technologies to Enhance Early Grade Teaching and Learning. Funding: University of Dar Es Salaam Competitive Research and Innovation Grant for the year 2020/2021. Project Registration Number: DUCE-20122

Publications:

• Elimboto M. Yohana and Mapundi K. Banda, High-order relaxation approaches for adjointbased optimal control problems governed by nonlinear hyperbolic systems of conservation laws, J. Numer. Math. 2016; 24 (1):45–71. https://www.degruyter.com/document/doi/10.1515/jnma2013-1002/html
• Elimboto Yohana, Yi-Chao Li, Yin-Zhe Ma, Forecasts of cosmological constraints from HI intensity mapping with FAST, BINGO and SKA-I, RAA Journal, Vol. 19, No. 12 (2019). https://iopscience.iop.org/article/10.1088/1674-4527/19/12/186/meta
• Elimboto Yohana, Yin-Zhe Ma, Di Li, Xuelei Chen, and Wei-Ming Dai. Recovering 21-cm signal from simulated FAST intensity maps. Accepted for publication to Monthly Notices of the Royal Astronomical Society. https://arxiv.org/abs/2104.10937synergy of Mathematics, Physical and Computational Sciences to address forecasting, analysis and the modeling challenges in surveying extremely largescale structures of the universe to consequently, constrain various cosmological parameters, such as composition densities of dark energy and dark matter in the universe, and asses the universe's expansion and growth rates.