Mr. Talal Taha , International Islamic University Islamabad , Pakistan.
Mr. Talal Taha is a passionate researcher in applied mathematics and fluid dynamics, with a strong interest in the intersection of artificial intelligence and engineering 🌊🤖. Currently completing his MS in Applied Mathematics from International Islamic University Islamabad 🎓, he has authored multiple publications in top-tier journals by 2025 📚. His research explores machine learning applications to complex flow systems, using tools like MATLAB, Python, and COMSOL Multiphysics 💻. With teaching experience in Islamabad and active participation in scientific conferences, Talal remains committed to academic excellence and collaborative growth 🌟.
Professional Profile
Education and Experience
🎓 Education:
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M.S. in Applied Mathematics, International Islamic University Islamabad (2022–2024) – GPA: 3.85/4.00
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B.S. in Mathematics, Khwaja Fareed University of Engineering and IT (2018–2022) – GPA: 3.55/4.00
💼 Work Experience:
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👨🏫 Mathematics Teacher, The Smart School Capital Campus, G13-4, Islamabad (Mar 2023 – Sep 2023)
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📘 Mathematics Lecturer, Seneta Vision School and College, G-13, Islamabad (Oct 2023 – Nov 2024)
Summary Suitability
Mr. Talal Taha stands as a highly promising researcher and a deserving candidate for the Best Researcher Award, demonstrating outstanding contributions in the fields of Applied Mathematics, Fluid Dynamics, and Artificial Intelligence-driven modeling techniques. His exceptional academic track record, advanced computational expertise, and impactful research publications in international journals reflect his commitment to advancing scientific understanding in engineering and thermal sciences.
Professional Development
Mr. Talal Taha continuously advances his skills through academic training and software mastery 🔍💡. He is proficient in MATLAB for fluid simulations and neural network modeling, Python for data analysis and predictive modeling, and COMSOL for Multiphysics simulations 🔧📊. He has actively participated in international conferences and seminars, including the 9th International Conference on Fluid Mechanics hosted by the Pakistan Academy of Sciences 🌐📅. Additionally, Talal has completed courses in time management and financial literacy, reinforcing both technical and soft skills necessary for academic and professional success 📈🎯.
Research Focus
Talal Taha’s research is primarily focused on fluid dynamics, applied mathematics, and machine learning 🔬🌊🧠. His work involves magnetohydrodynamics (MHD), bioconvection in non-Newtonian fluids, and thermal radiation using artificial intelligence and numerical techniques 🧪🔥. With tools like neural networks and regression modeling, he explores how complex boundary layer flows react to thermal and chemical influences, aiming to optimize real-world engineering systems 🛠️📉. His interdisciplinary approach connects physics, AI, and mathematics to simulate and solve challenges in engineering fluid systems efficiently and innovatively 🤖📐.
Honors and Awards
🏅 Awarded certificate for short course “Time Management Skills” – 2020
🏆 Certificate of participation in Mathematics Quiz Competition – KFUEIT
💰 Certificate in “National Financial Literacy Program for Youth” – 2021
Publication Top Notes
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Advanced intelligent computing ANN for momentum, thermal, and concentration boundary layers in plasma electro hydrodynamics Burgers fluid
Authors: M.I. Khan, R. Ghodhbani, T. Taha, F.A.M. Al-Yarimi, A. Zeeshan, N. Ijaz, N. Ben Khedher
Journal: International Communications in Heat and Mass Transfer
Volume: 159, Article ID: 108195, Year: 2024
Citations: 22
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Machine learning-based prediction of heat transfer enhancement in Carreau fluids with impact of homogeneous and heterogeneous reactions
Authors: N. Fatima, R. Ghodhbani, N. Khalid, M.I. Khan, T. Taha, N. Ijaz, N. Saleem
Journal: Multiscale and Multidisciplinary Modeling, Experiments and Design
Volume: 8(2), Pages: 145–158, Year: 2025
Citations: 12
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Novel investigation of Burger fluid with gyrotactic microorganisms over a sheet using Levenberg–Marquardt backpropagation (LMBP)
Authors: S. Abdal, T. Taha, N.A. Shah, S.J. Yook
Journal: Alexandria Engineering Journal
Volume: 117, Pages: 403–417, Year: 2025
Citations: 7
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Non-Newtonian fluid flow with bioconvection: The role of Rayleigh number and buoyancy over a sheet
Authors: J.A. Jugnoo, A. ur Rehman, T. Taha
Journal: Journal of Innovative Research in Mathematical and Computational Sciences
Volume: 3, Issue TBD, Year: 2024
Citations: 6
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Neural networking-based approach for examining heat transfer and bioconvection in Non-Newtonian fluid with chemical reaction over a stretching sheet
Authors: S. Abdal, T. Taha, L. Ali, R.M. Zulqarnain, S.J. Yook
Journal: Case Studies in Thermal Engineering
Volume: 69, Article ID: 106047, Year: 2025
Citations: 2
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Machine learning study for three-dimensional magnetohydrodynamic Casson fluid flow with Cattaneo–Christov heat flux using linear regression technique: Application in porous media
Authors: S. Abdal, T. Taha, N.A. Shah, S.J. Yook
Journal: Engineering Applications of Artificial Intelligence
Volume: 156, Article ID: 111159, Year: 2025
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Effects of thermal radiation on MHD bioconvection flow of non-Newtonian fluids using linear regression-based machine learning and artificial neural networks
Authors: S.M. Sait, R. Ellahi, N. Khalid, T. Taha, A. Zeeshan
Journal: International Journal of Numerical Methods for Heat & Fluid Flow
Volume: 35(5), Year: 2025
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Artificial intelligence approach to magnetohydrodynamic flow of non-Newtonian fluids over a wedge: Thermophoresis and Brownian motion effects
Authors: T. Taha, S. Abdal, L. Ali, R.M. Zulqarnain, S.J. Yook
Journal: Engineering Science and Technology, an International Journal
Volume: 66, Article ID: 102071, Year: 2025
Conclusion
Mr. Talal Taha exemplifies the qualities sought in a Best Researcher Award recipient. His work seamlessly integrates theoretical mathematics, fluid mechanics, and modern machine learning approaches to solve complex engineering problems. With a consistent publication record, advanced modeling skills, international collaboration, and a forward-looking research agenda, Talal’s contributions have already made a measurable impact in computational science and are poised to influence future developments across interdisciplinary domains.