1. Name and Academic Designation

Dr. Radhakrishnan V. K.
Assistant Professor (Senior Grade) of Mathematics, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya (SCSVMV), Kanchipuram, Tamil Nadu.

2. VIIM Resource Category

• Mathematical Modelling

• College and University Mathematics

• Computational Mathematics and Mathematical Software

• Artificial Intelligence and Machine Learning

• Applied Mathematics and Interdisciplinary Applications

• Research Methodology and Research Mentoring

3. Profile

Dr. Radhakrishnan V. K. is a mathematician with over 17 years of university teaching and research experience in Operations Research, Graph Theory, network optimisation, mathematical modelling, and simulation. He holds a Ph.D. in Mathematics, with research on optimal network and queueing models for temple tourism and queue management in Kanchipuram. His research interests include Steiner tree problems, queueing theory, Monte Carlo simulation, linear programming, scheduling, and mathematical modelling. He has guided 16 M.Phil. and four M.Sc. dissertations and currently guides three Ph.D. scholars. In addition to his mathematical research, he has designed and developed AI-enabled learning platforms, academic workflow systems, and digital learning resources. His technical expertise includes Python, MATLAB, Open Solver, Microsoft Excel, and AI technologies such as retrieval-augmented generation, AI tutoring, and automation. His work focuses on integrating mathematical thinking, computational tools, and responsible AI into teaching, research, and academic administration.

4. Areas of Contribution to VIIM

• Mathematical Modelling: Workshops on formulating, solving, and interpreting mathematical models based on real-life problems.

• Operations Research and Optimisation: Practical applications of linear programming, integer programming, network optimisation, scheduling, and decision-making.

• Graph Theory and Network Models: Hands-on exploration of graph colouring, graph labelling, Steiner trees, network design, and optimisation.

• Simulation: Applying Monte Carlo simulation and queueing models to problems involving service systems, tourism, logistics, and resource allocation.

• Computational Mathematics: Solving mathematical problems using Python, MATLAB, Microsoft Excel, Open Solver, and other suitable tools.

• AI-Assisted Mathematical Learning: Exploring AI tutoring, AI-assisted problem-solving, and responsible use of AI while preserving independent mathematical reasoning.

• AI for Teaching and Research: Workshops on AI tools for teaching materials, research workflows, academic writing, and interactive learning resources.

• Mathematical Software and Open-Source Tools: Demonstrating how accessible software can support mathematical visualisation, computation, modelling, and optimisation.

• Digital Learning Platform Development: Sharing approaches to developing interactive Mathematics courses, self-paced learning systems, online assessments, and AI-supported educational platforms.

• Research Methodology and Mentoring: Guiding UG/PG projects and research scholars in mathematical modelling, computational investigation, research problem formulation, and technical writing.

• Mathematics in Indian Knowledge Systems: Introducing mathematical ideas from the Śulba Sūtras and exploring their historical and mathematical significance.

• Faculty Development: Conducting hands-on training for teachers and faculty in AI-assisted teaching, mathematical software, and digital academic tools.