Bacha Khan University Charsadda
Dr. Muhammad Sohaib
Lecturer (BPS-18) | Department of Mathematics & Statistics
Dr. Muhammad Sohaib is a Lecturer in Mathematics at Bacha Khan University Charsadda, Pakistan, specializing in computational and applied mathematics. His research focuses on numerical methods for partial differential equations, computational fluid dynamics, phase-field modeling, two-phase flow, fractional PDEs, and physics-informed machine learning. He has published research on diffuse-interface methods, interfacial dynamics, fractional Allen–Cahn equations, and efficient numerical algorithms. His broader research goal is to develop accurate, efficient, and structure-preserving computational methods for complex physical and mathematical models.
Lecturer (BPS-18)
Educational Services
Department of Mathematics & Statistics
| Degree / Certificate | Institute / University | Year | Division / CGPA |
|---|---|---|---|
| Ph.D Mathematics Computational Mathematics (Two-phase Flow, AI Driven Modeling and Scientific Computing, Numerical Analysis) | COMSATS University Islamabad | 2024 | 3.8/4 |
| MS Applied Mathematics Computational Mathematics | Ghulam Ishaq Khan Institute of Engineering Sciences & Technology, Topi-Swabi, KP, Pakistan | 2015 | 3.75/4 |
| MSc Mathematics Applied Mathematics | Quaid-I-Azam University Islamabad | 2013 | 2347/3000 |
| BSc | University of Peshawar | 2010 | 431/550 |
| HSSC Pre-Engineering | BISE Peshawar | 2008 | 802/1100 |
| SSC Science | BISE Mardan | 2006 | 815/1050 |
Lecturer (BPS-18)
2016 - PresentBacha Khan University Charsadda, Pakistan
Teaching and Research
- Numerical solution of sixth-order boundary-value problems using Legendre wavelet collocation method. Results in Physics (2018). Volume 8, March 2018, Pages 1204-1208 DOI/Link: https://doi.org/10.1016/j.rinp.2018.01.065
- A numerical approach to solve volume-based batch crystallization model with fines dissolution unit. Processes (2019). 7(7), 453 DOI/Link: https://doi.org/10.3390/pr7070453
- Neural Networks and Deep Learning - DeepLearning.AI (via Coursera) (2025)
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization - DeepLearning.AI (via Coursera) (2025)
- Linear Algebra for Machine Learning and Data Science - DeepLearning.AI (via Coursera) (2025)
- Calculus for Machine Learning and Data Science - DeepLearning.AI (via Coursera) (2025)
- Programming for Everybody (Getting Started with Python) - University of Michigan (via Coursera) (2025)
- Mathematics for Machine Learning: Multivariate Calculus - Imperial College London (via Coursera) (2026)
- Discover the Art of Prompting - Google (via Coursera) (2026)