04 · Emerging computational methods

Graph Learning & AI

Combining network science with machine learning to learn from relational data.

This area spans network representation learning, explainable AI, graph-based applications and machine learning for structured data.

Interactive research motif

Research perspective

The direction connects foundational network representations with contemporary graph learning and responsible AI.

Selected publications

2025

Teaching Explainable Machine Learning to Interdisciplinary Learners: A Pedagogical Model for Responsible AI

Raj, Ebin Deni; Lekha, Divya Sindhu; Job, Ashly Ann;
Computing Education Research: 18th Annual ACM India Compute Conference, COMPUTE 2025, Ropar, India, December 7–9, 2025, Proceedings · pp. 3
2025

Teaching Explainable Machine Learning to Interdisciplinary Learners: A Pedagogical Model for Responsible AI Education

Raj, Ebin Deni; Lekha, Divya Sindhu; Jo, Ashly Ann; Jose, Arun Cyril;
Annual ACM India Compute Conference · pp. 3-15
2025

3D Hole Detection with an Emphasis on Deep Learning

Thomas, Helena; Lekha, Divya Sindhu;
2025 IEEE 4th International Conference on Data, Decision and Systems (ICDDS) · pp. 112-117
2023

2023 International Conference on Innovative Trends In Information Technology (ICITIIT)

Kala, S; Lekha, Divya Sindhu;
2022

Network Representation Learning : A Survey

Paul, Rini T; Lekha, Divya Sindhu;
INTERNATIONAL CONFERENCE ON MATHEMATICS OF INTELLIGENT COMPUTING AND DATA SCIENCE ICMICDS-22

From published work to current questions

This page is grounded in the supplied Google Scholar publication export. Current projects, collaborators and new publications can be linked here as the research programme evolves.