Teaching
🏛️ Academic Courses Taught
At the university level, I have been responsible for delivering comprehensive lectures across several core mathematical disciplines. My curriculum is designed to bridge the gap between abstract theory and applied computation.
- Engineering Mathematics: Focusing on differential equations, linear algebra, and vector calculus for applied sciences.
- Probability and Statistics: Fundamentals of stochastic processes, data distributions, and statistical inference.
- Numerical Optimization: Deep dive into classical optimization techniques and modern metaheuristic algorithms.
- Soft Computing: Introducing students to neural networks, fuzzy logic, and evolutionary computation.
🧠 Teaching Philosophy
Mathematics is often perceived as an abstract and intimidating subject. My core teaching philosophy revolves around demystifying complex theories through visual intuition and computational validation.
- Conceptual Foundations First: Before introducing complex formulas, I ensure the geometric or physical intuition behind the mathematics is clear.
- Computational Integration: I actively encourage students to use tools like Python and Scilab. If a student can code a mathematical model, they truly understand its mechanics.
- Real-World Application: Abstract algebra and optimization are connected to tangible real-world case studies, such as supply chain logistics and inventory control.
- Visual Learning: I utilize Manim (Python) and digital whiteboarding (Samsung Notes) to create dynamic, animated explanations that static textbooks cannot provide.
🎓 Mentorship & Guidance
Beyond the standard curriculum, I provide dedicated guidance for students aiming for higher education or research:
- M.Sc. Dissertation Supervision: Guiding postgraduate students in research methodologies, particularly in operations research.
- Competitive Exam Strategy: Structuring study plans for CSIR NET, GATE, and IIT JAM aspirants.