Rooh Ali | mathematical modeling | Best Researcher Award

Dr. Rooh Ali, Government College University Faisalabad, Pakistan.

Dr. Rooh Ali is a dedicated Ph.D. candidate in Mathematics at Government College University, Faisalabad, under the supervision of Dr. Khadija Tul Kubra. With a strong academic background, he holds an M.Phil. in Mathematics, where he developed a novel mathematical model addressing the COVID-19 pandemic. His research interests span mathematical modeling, dynamical systems, and computational biology. An active researcher, he has contributed significantly to various journals and conferences, showcasing his expertise in infectious disease modeling. Dr. Ali is committed to leveraging mathematics for real-world applications, making a meaningful impact in his field.

Publication profile

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Education and Experience

Ph.D. in Mathematics (2022 – Present)
Government College University, Faisalabad, Pakistan
Supervisor: Dr. Khadija Tul Kubra.
M.Phil. in Mathematics (2020 – 2022)
Government College University, Faisalabad, Pakistan
CGPA: 3.65/4.00
Thesis: A Novel Mathematical Modelling of COVID-19 Pandemic.
BS in Mathematics (2016 – 2020)
Government College University, Faisalabad, Pakistan
CGPA: 3.79/4.00.

Suitability For The Award

D. Rooh Ali is a promising candidate for the Best Researcher Award, demonstrating significant academic and research achievements in the field of mathematics, particularly in mathematical modeling and its applications to infectious diseases. His educational background, research contributions, and active engagement in academic discourse position him as a leading figure among emerging researchers.

Professional Development ()

Dr. Rooh Ali has completed several prestigious training programs, enhancing his skills in advanced mathematical tools. He is certified in MATLAB, Simulink, Deep Learning, and Machine Learning through MathWorks. His participation in workshops and conferences, such as the International Conference on Mathematical Modeling, showcases his commitment to professional growth and staying current in his research area. These experiences have equipped him with analytical skills crucial for his research endeavors.

Research Focus

Dr. Rooh Ali specializes in mathematical modeling, particularly in the realm of infectious diseases. His work encompasses dynamical systems, computational biology, and machine learning applications in modeling various health-related phenomena, including COVID-19 and the Nipah virus. By integrating advanced mathematical concepts with real-world issues, he aims to provide insights that can inform public health strategies and interventions. His innovative approaches help unravel complex biological dynamics and contribute to the field of applied mathematics.

Awards and Honors ()

Reviewer for Journals (2024 – Present): Alexandria Engineering Journal, Environmental Modelling and Software, Heliyon.
Key Achievements in MATLAB and Machine Learning: Completed courses on MATLAB Onramp, Simulink Onramp, Deep Learning Onramp, and Machine Learning Onramp.
Presented Research at International Conferences: Participated in workshops and conferences, including the International Workshop on Combinatorial Image Analysis.

Publications

Modeling and analysis of novel COVID-19 outbreak under fractal-fractional derivative in Caputo sense with power-law: a case study of Pakistan – Cited by: 13, Year: 2023
An Atangana–Baleanu derivative-based fractal-fractional order model for the monkey pox virus: A case study of USA – Cited by: 8, Year: 2024
Exploring the Influence of Burial and Cremation Practices on Nipah Virus Transmission: A SIRD Model Analysis – Cited by: 2, Year: 2024
Analysis and comparative study of a deterministic mathematical model of SARS-COV-2 with fractal-fractional operators: a case study – Cited by: 2, Year: 2024
Modeling and Analysis of Corruption Dynamics in Society under Fractal-Fractional Derivative in Caputo Sense with Power-Law – Cited by: 1, Year: 2023
Analysis of Monkey Pox Transmission Dynamics in Society with Control Strategies Under Caputo-Fabrizio Fractal-Fractional Derivative – Cited by: 1, Year: 2023

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