Dr. Muhammad Zubair Iqbal

PhD Theme/Topic: AI-Driven Predictive Decision Intelligence for Next-Generation Business Ecosystems

Supervisor: Dr. Muhammad Zubair Iqbal, Assistant Professor
Contact #: 0303-9822786
Email: mzubairiqbal.bukc@bahria.edu.pk
Campus/School/Dept: BUKC/BBS/MS
RAC Approved Supervisor for Research Areas: Business Analytics, Data Analysis, Machine Learning

Supervisory Record:
PhD Produced:
PhD Enrolled:
MS/MPhil Produced:
MS/MPhil Enrolled: 

Topic Brief Description: 

With the rising complexity of modern business environments, organizations are rapidly adopting AI-driven decision intelligence to predict outcomes, optimize operations, and automate strategic decision-making. This research focuses on developing advanced predictive analytics and machine learning models that help businesses anticipate market changes, customer behavior, supply chain disruptions, financial anomalies, and operational risks.

The study will explore the integration of machine learning, and data mining to create intelligent decision-support systems capable of adapting to dynamic business conditions. Furthermore, the research will investigate real-time analytics, and automated business decision pipelines to support strategic, tactical, and operational decisions across business functions, including marketing, finance, HR, supply chain, and operations.

Research Objectives/Deliverables:

1. To develop a unified AI-driven predictive decision framework integrating machine learning, data mining, and real-time analytics for enterprise-level decision-making.
2. To design intelligent ML models capable of predicting business KPIs such as customer churn, demand fluctuations, financial fraud, and operational risks.
3. To evaluate the impact of AI-based decision intelligence on organizational performance, efficiency, and cost reduction.

Research Questions: 

1. How can machine learning and predictive analytics be integrated into a unified decision intelligence system to support multi-domain business decision-making?
2. Which ML/AI models are most effective for predicting complex business events such as demand surges, customer churn, financial anomalies, and supply chain disruptions?
3. What is the measurable impact (ROI, cost reduction, operational efficiency) of adopting AI-powered prescriptive decision systems in real-world business operations?

Candidate’s Eligibility Profile:

1. The applicant must have an MS/MPhil degree in Business Analytics, Data Science, Computer Science, IT, Management Sciences, or related fields with CGPA > 3.0
2. Strong background in machine learning, data analysis, statistics, and data mining is required. Experience with programming languages such as Python, R, or related ML tools is highly advantageous.
3. Applicants should possess strong analytical thinking and problem-solving abilities and be capable of working with real-world business datasets.
4. Excellent written and spoken English is essential. The candidate should be self-motivated, capable of independent research, and also work collaboratively within a research team.