Dr. Rajnesh Lal

Dr. Rajnesh Lal

Position: Assistant Professor
School: School of Mathematical & Computing Science
Email: rajnesh.lal@fnu.ac.fj
Campus: Natabua Campus – Lautoka
Phone: 6667355 ext. 7051

Biography:

Dr. Rajnesh Lal is an Assistant Professor of Mathematics in the School of Mathematical and Computing Sciences at Fiji National University. In addition to teaching undergraduate courses in applied mathematics, he supervises postgraduate research students in applied and computational mathematics. His research focuses on solving real-world problems through mathematical modelling, with applications in epidemiology, cardiovascular and fluid dynamics, climate and environmental systems, and economic modelling. He welcomes research proposals in interdisciplinary areas spanning mathematics, computer science, physics, and biology. In addition to his research and supervision roles, Dr. Lal serves as an academic editor and actively reviews manuscripts for several international journals.

Qualification:

  • Doctor of Philosophy in Applied Mathematics and Modelling, University of Montpellier (UM), Montpellier, France.
  • Master of Science in Mathematics, the University of the South Pacific (USP), Suva, Fiji.
  • Master of Business Administration in Finance and Marketing, the University of the South Pacific (USP), Suva, Fiji.
  • Bachelor of Science in Mathematics and Physics, the University of the South Pacific (USP), Suva, Fiji.

Area of Expertise:

  • Data assimilation
  • Inverse problems and machine learning
  • Hemodynamics
  • Computational fluid dynamics
  • Adaptive mesh refinement methods for fluid flows
  • Epidemiological modelling
  • Climate Data Analysis
  • Mathematical modeling
  • Numerical modelling, analysis and simulation

Journals:

  • Lal, R., & Li, Z. (2024). Further accuracy verification of a 2D adaptive mesh refinement method using a steady flow past a square cylinder, The ANZIAM Journal, 67, e3.
  • Sharma, S., Lal, R., & Kumar, B. (2024) Developing machine learning application for early cardiovascular disease (CVD) risk detection in Fiji: A design science approach. Applied Computer Science, 20(3), 132-152.
  • Lal, R., Li, Z., & Li, M. (2024) Accuracy verification of a 2D adaptive mesh refinement method by the benchmarks of lid-driven cavity flows with an arbitrary number of refinements. Mathematics, 12(18), 2831.
  • Li, Z., & Lal, R. (2023) An evaluation of accuracy and efficiency of a 3D adaptive mesh refinement method with analytical velocity fields. International Journal of Computational Methods, 2341001.
  • Lal, R., Huang, W., Li, Z., & Prasad, S. (2022) An assessment of transmission dynamics via time-varying reproduction number of the second wave of the COVID-19 epidemic in Fiji. Royal Society Open Science, 9(8), 220004.
  • Li, Z., & Lal, R. (2022) Application of 2D adaptive mesh refinement method to estimation of the center of vortices for flow over a wall-mounted plate. International Journal of Computational Methods, 2143012.
  • Singh, R., Lal, R., & Kotti, R. (2022) Time-discrete SIR model for COVID-19 in Fiji. Epidemiology and Infection, 150, E75.
  • Lal, R., Huang, W., & Li, Z. (2021) An application of the ensemble Kalman filter in epidemiological modelling. PLoS ONE, 16(8), e0256227.
  • Rao, K. R., Lal, R., & Singh, K. (2021) Hankel functional connected to lemniscate of Bernoulli. Australian Journal of Mathematical Analysis and Applications, 18(2), 1-5.
  • Rao, K. R., Lal, R., & Singh, K. (2021) Hankel determinant of second and third order for functions with derivative as positive real part associated with multivalent analytic functions. Advances in Dynamical Systems and Applications, 16(2), 559-564.
  • Lal, R., Nicoud, F., Bars, E.L. et al. (2017) Non invasive blood flow features estimation in cerebral arteries from uncertain medical data. Annals of Biomedical Engineering, 45, 2574-2591.
  • Lal, R., Mohammadi, B., & Nicoud, F. (2016) Data assimilation for identification of cardiovascular network characteristics. International Journal for Numerical Methods in Biomedical Engineering, 33(5), e2824.
  • Lal, R., & Li, Z. (2015) Sensitivity analysis of a mesh refinement method using the numerical solutions of 2D lid-driven cavity flow. Journal of Mathematical Chemistry, 53, 844-867.

Conference:

  • Lal, R & Li, Z. (2024) Further accuracy verification of the 2D adaptive mesh refinement method by the benchmarks of lid-driven cavity flow. Proceedings of the International Conference on Computational Methods. Scientech Publisher LLC, USA, 20-31.
  • Sharma, S., Lal, R., & Kumar, B. (2023) Machine learning for early detection of cardiovascular disease in Fiji. In 2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Nadi, Fiji, IEEE, 1-6
  • Li, Z., & Lal, R. (2022) Accuracy of a 3D adaptive mesh refinement method with analytical velocity fields. In G. R. Liu, & N-X. Hung (Eds.), Proceedings of the International Conference on Computational Methods. Scientech Publisher LLC, USA, 9, 31-41.
  • Lal, R., Nicoud, F., Bars, E.L. et al. (2017) Parameter estimation using ensemble Kalman filter for patient-specific hemodynamic computations. Paper presented at: CMBE2017. Proceedings of the 5th International Conference on Computational and Mathematical Biomedical Engineering; Pittsburgh, USA.
  • Lal, R., & Li, Z. (2013) Sensitivity analysis of a mesh refinement method using the numerical solutions of 2D lid-driven cavity flow. Proceedings of the 13th International Conference on Computational and Mathematical Methods in Science and Engineering: CMMSE2013; Rota, Spain.
  • Li, Z., & Lal, R. (2010) An application of a mesh refinement method based on the law of mass conservation. In 2010 International Conference on Computational and Information Sciences, IEEE, 226-229.

Teaching interest:

  • Numerical Analysis
  • Inverse Problems
  • Statistical and Machine Learning
  • Linear Programming
  • Differential Equations
  • Operations Research
  • Linear Algebra
  • Calculus

Research interest:

  • Mesh refinement for fluid flow
  • Patient-specific hemodynamic
  • Mathematical and computational epidemiology
  • Inverse problems, data assimilation and uncertainty quantification
  • Numerical analysis, simulations and scientific computing
  • Machine learning and data-driven modelling for health, environmental, climate and economic applications