Teaching & Pedagogy Philosophy
Emphasizing Active Learning, Project-Based Assessments, and Interdisciplinary Thinking.
Mathematics for Intelligent Systems-I
Linear Algebra, Ordinary Differential Equations, Probability Distributions, and basics of Quantum Computing.
Introduction to Material Informatics
Molecular Dynamics simulations, atomic structures, property screening, and materials discovery using AI models.
Mathematics for Intelligent Systems 3
Advanced intelligent systems modeling and applied mathematics.
Research Methodology
Developing robust frameworks for high-performance computing research pipelines and data integrity.
Academic Mentorship & Supervision
- Research Interns: A. Jagdish, School of Physical Sciences, Amrita (06/2025 onwards).
- Masters Thesis Co-supervision: Magnus Olsen, Norwegian University of Science and Technology (Co-advisor; NTNU, since 08/2025). Research Topic: Understanding Non-Newtonian Materials.
- Student Collaborators:
- A. Harish (Department of Mathematics, Amrita, 06/2025 onwards)
- A. Venkatraman (Department of Mathematics, Amrita, 06/2025 onwards)
- P. S. Mrudula (Department of Artificial Intelligence, Amrita, 11/2025 onwards)
Our Pedagogical Philosophy
"Education is not the learning of facts, but the training of the mind to think." — Albert Einstein
Our classroom environments focus heavily on **Active Learning** (moving past lectures to live notebooks, modeling sandbox workshops, and interactive coding) and **Interdisciplinary Bridging** (empowering students to observe identical mathematical operators connecting quantum computing states and deep neural networks).