Dr. Imran Javaid
Professor of Mathematics (Tenured)
Centre for Advanced Studies in Pure and Applied Mathematics (CASPAM) · Bahauddin Zakariya University, Multan, Pakistan
Two decades of research in graph theory and rough-set–based granular computing — now applied to machine learning, financial analytics and supply-chain decision systems. Former Director of CASPAM and supervisor of 87 research scholars.
From the structure of networks to decisions under uncertainty.
A research programme that started in pure graph theory and now carries its tools — resolvability, symmetry, granulation — into machine learning, finance and operations.
Graph & Network Theory
Metric dimension, resolvability, fault tolerance, fixing and distinguishing parameters, controllability of networks.
Rough Sets & Granular Computing
Information granulation, attribute reduction and similarity-based rough sets in graphs, algebraic structures and information tables.
AI, Machine Learning & Decision Science
Feature selection, explainable decision support, graph-based learning and knowledge-based systems.
Quantitative Finance & Business Analytics
Portfolio optimisation, risk management, forecasting, operations research and supply-chain analytics.
Published in
Recent and representative work
69 refereed journal articles since 2007, co-authored with doctoral scholars and collaborators in Pakistan and abroad. See all publications →
- 2026
Metric-based granular computing in networks
H. Arshad, I. Javaid
Applied Soft Computing, 202, Part C (October 2026), 115965.
Rough Sets & Granular ComputingDOI ↗ - 2025
Symmetry-based granulation in networks associated with commutative rings: application in social network dynamics
I. Javaid, A. Fatima, M. Akram
Journal of Applied Mathematics and Computing, (2025), 1–26.
Rough Sets & Granular ComputingFind paper ↗ - 2024
Rough set theory applied to finite dimensional vector spaces
A. Fatima, I. Javaid
Information Sciences 659 (2024) 120072.
Rough Sets & Granular ComputingFind paper ↗ - 2019
Locating-dominating sets of functigraphs
M. Murtaza, M. Fazil, I. Javaid
Theoretical Computer Science, 799(2019), 115–123.
Graph & Network TheoryFind paper ↗ - 2013
On the metric dimension of generalized Petersen graphs
S. Ahmad, I. Javaid, M. A. Chaudhry, M. Salman
Quaestiones Mathematicae, 36(2013), 421–435.
Graph & Network TheoryFind paper ↗ - 2012
On the metric dimension of circulant graphs
M. Imran, A. Q. Baig, S. A. Bokhary, I. Javaid
Applied Mathematics Letters 25(2012) 320–325.
Graph & Network TheoryFind paper ↗
13 doctoral and 74 MPhil scholars.
Doctoral work spans resolvability, symmetry and granular computing; recent MPhil theses cover stock-trading strategies, portfolio optimisation, pricing with machine learning and supply-chain forecasting.
8 graduated · 5 in progress
Resolvability, distinguishing and fixing parameters, controllability, granular computing and attribute reduction.
74 theses supervised
Graph theory, rough sets, financial analytics, decision science and supply-chain management.
Director, CASPAM 2019–22
Led the Centre; serves on boards of studies and tenure committees at more than ten institutions.
5 funded projects
HEC Pakistan and BZU grants in graph labeling, network controllability, supply-chain BI and portfolio optimisation.
Writing on mathematics, AI and education
Short essays and programme notes shared publicly on LinkedIn.
The Mathematical Blueprint of AI
Explains AI through approximation, optimization, geometry, linear algebra, calculus, probability, representation learning and generalization.
Read on LinkedIn ↗AI EducationBS Mathematics with Artificial Intelligence at CASPAM
Positions mathematics as the language of intelligence and presents a curriculum bridge from calculus, linear algebra, probability, optimization and programming to modern AI.
Read on LinkedIn ↗Financial AnalyticsBS Financial Analytics — Mathematics, Finance and Analytics
Highlights the BS Financial Analytics initiative and its emphasis on analytical thinking, financial decision making and quantitative career pathways.
Read on LinkedIn ↗Ways to collaborate
Open to research partnerships, doctoral enquiries, invited talks and applied analytics engagements.
Research collaboration
Joint papers and projects in graph theory, rough sets, granular computing, explainable AI and decision analytics.
Doctoral & MPhil supervision
Prospective scholars in networks, granular computing, financial analytics and supply-chain decision science.
Invited talks & examining
Keynotes, seminars, faculty development workshops, external examination and boards of studies.
Industry & executive training
Analytics, operations research, portfolio and risk modelling, and AI capability programmes for teams.
Start a conversation
For collaboration, supervision or speaking invitations, email is the fastest route.