Muhammad Imran Khan | Machine Learning | Young Scientist Award

Dr. Muhammad Imran Khan, International Islamic University Islamabad Pakistan, Pakistan.

Muhammad Imran Khan is a dedicated Ph.D. scholar in Applied Mathematics at the International Islamic University Islamabad, Pakistan. With a strong passion for advancing mathematical understanding and fostering global collaboration , his research delves into partial differential equations, machine learning, and their interdisciplinary applications . Muhammad is skilled in computational tools like MATLAB, OpenFOAM, and DUNE PDELab, with expertise in Python and C++ programming . Committed to sharing knowledge and promoting mathematics, he aspires to make impactful contributions to both research and society .

Publication profile

Scopus

Education And Experiance

Ph.D. in Applied Mathematics (Expected August 2024): International Islamic University Islamabad, Pakistan.
M.Sc. in Computational Mathematics (2019): COMSATS University Islamabad, Pakistan.
Bachelor’s in Applied Mathematics (2016): University of Sargodha, Pakistan.
FSc (2012): Federal Board of Intermediate and Secondary Education, Islamabad, Pakistan.
Metric (2010): Sargodha Board of Intermediate and Secondary Education.

Suitability For The Award

Dr. Muhammad Imran Khan is an outstanding candidate for the Young Scientist Award, characterized by his profound academic journey, versatile skill set, and commitment to advancing mathematical research. His focus on applied mathematics, specifically in the area of partial differential equations (PDEs) and computational methods, positions him as a promising young researcher. His proficiency in machine learning, deep learning, and advanced scientific software highlights his ability to integrate modern computational tools into mathematical problem-solving, making him an asset to the scientific community.

Professional Development

Muhammad Imran Khan thrives on leveraging mathematics to address real-world challenges. His proficiency spans advanced numerical analysis, machine learning, and deep learning , alongside extensive experience with scientific software tools such as DUNE PDELab and ANSYS . Skilled in Python and C++, he applies computational methods to explore innovative solutions for diverse fields. Muhammad actively advocates for mathematical research , engaging with decision-makers and fostering collaboration to enhance knowledge dissemination. He envisions a future where mathematics drives practical advancements, supporting both academic growth and societal progress .

Research Focus

Muhammad Imran Khan’s research centers on applied mathematics, with a particular interest in partial differential equations (PDEs) . His work explores their applications across interdisciplinary fields such as machine learning, deep learning, and computational modeling . Muhammad delves into the synergy between mathematical theory and real-world challenges, leveraging tools like MATLAB and OpenFOAM to uncover novel insights . Passionate about advancing mathematical methodologies, he aspires to contribute solutions that bridge theoretical research and practical innovation for the betterment of society .

Awards and Honors

Merit-Based Scholarship: For outstanding academic performance during M.Sc. at COMSATS University.
Best Research Poster Award: Recognized at a national mathematics conference for innovative work on PDE applications.
Distinction in FSc: Achieved top honors in Federal Board examinations.
Programming Excellence Certificate: Awarded for proficiency in Python and C++ during Ph.D. coursework.
Recognition of Contribution: For active participation in research collaboration projects at International Islamic University Islamabad.

Publoication Top Notes

Integrated Artificial Intelligence and Non-Similar Analysis for Forced Convection of Radially Magnetized Ternary Hybrid Nanofluid of Carreau-Yasuda Fluid Model Over a Curved Stretching Surface (2024)
Advanced Intelligent Computing ANN for Momentum, Thermal, and Concentration Boundary Layers in Plasma Electro Hydrodynamics Burgers Fluid (2024) – Cited by: 0
Analysis of Nonlinear Complex Heat Transfer MHD Flow of Jeffrey Nanofluid Over an Exponentially Stretching Sheet via Three Phase Artificial Intelligence and Machine Learning Techniques (2024)
Modeling and Predicting Heat Transfer Performance in Bioconvection Flow Around a Circular Cylinder Using an Artificial Neural Network Approach (2024)
Advanced Computational Framework to Analyze the Stability of Non-Newtonian Fluid Flow Through a Wedge with Non-Linear Thermal Radiation and Chemical Reactions (2024) – Cited by: 1
Computational Intelligence Approach for Optimising MHD Casson Ternary Hybrid Nanofluid Over the Shrinking Sheet with the Effects of Radiation (2023) – Cited by: 17
Artificial Neural Network Simulation and Sensitivity Analysis for Optimal Thermal Transport of Magnetic Viscous Fluid Over Shrinking Wedge via RSM (2023) – Cited by: 20