01
Past
MSc Data Science & Analytics at the University of Westminster (2023–2024). Dissertation in NLP and sentiment analysis on Reddit data.
Research & Education
From enterprise systems to data-driven decision support.
An academic trajectory grounded in enterprise practice — postgraduate work in data science and NLP, current interests in operating-model intelligence, and a future direction toward AI-driven decision support for industrial systems.
Research Trajectory
02
Current
Operating-model intelligence: using process and operational data to support evidence-led decisions inside large industrial enterprises.
03
Future
AI-driven decision support in ERP, digital twins for operations, enterprise intelligence, and uncertainty-aware industrial systems.
Education
MSc, Data Science and Analytics
University of Westminster, London · Aug 2023 — Oct 2024
Postgraduate study in statistical learning, data engineering, natural language processing, and applied analytics — completed alongside concurrent practitioner roles in the United Kingdom.
BSc Computer Software Engineering
University of Applied Science and Technology
Final Project: User Support System — developed using C# and SQL Server to manage and track user requests within an organisational environment.
MSc Dissertation
Reddit-based sentiment analysis of the 2024 U.S. Presidential Election and its impact on Middle East tensions.
This dissertation examined how public online discussion responded to the 2024 U.S. Presidential Election, and how that discussion tracked alongside developments in Middle East tensions. I collected discourse from the Reddit API and built an end-to-end NLP pipeline — cleaning the text, scoring sentiment with VADER, and extracting features with TF-IDF across unigram, bigram, and trigram representations. I then compared several machine-learning classifiers to detect shifts in tone, topic, and polarity across the election period, with Random Forest and Support Vector Machine among the strongest performers. Alongside the classification work, I tracked sentiment distribution and discussion volume over time to see how the conversation moved as events unfolded.
Methods: Reddit API data collection · NLP · VADER sentiment scoring · TF-IDF · unigram/bigram/trigram features · ML classification (Random Forest, SVM).
Publication
Conference Paper
A Conceptual Model for Intelligent Knowledge Management in the Big Data Era: Synergizing Artificial Intelligence and Business Analytics.
Philip Worrall · Mohammad Mehrabani · Hadi Shafiee Bafti
Accepted for presentation at the Second International Conference on Advances in Artificial Intelligence in Engineering and Humanities — Melbourne, Australia, 23 August 2026.
A conceptual framework that examines how Artificial Intelligence and Business Analytics can be integrated within intelligent knowledge management systems to support decision-making, knowledge sharing, and organisational learning in data-intensive environments.
Status — Accepted for Presentation
Research Interests
A research trajectory shaped by enterprise practice — open to collaboration with academic groups working in adjacent areas.
AI-Driven Decision Support Systems
Digital Twins for Enterprise Operations
Uncertainty-Aware ERP Systems
Operational Intelligence
Data-Driven Decision Making
Enterprise Knowledge Systems
Future Direction
My current interests sit at the intersection of enterprise systems, operational decision-making, and applied artificial intelligence.
After many years working on ERP implementations, business process integration, and operational transformation programmes across multiple industries, I became increasingly interested in a recurring challenge: organisations often possess large volumes of data yet still struggle to make timely and effective decisions under uncertainty.
This observation has led me towards exploring AI-driven decision support, demand sensing, inventory digital twins, uncertainty-aware planning, and operational intelligence within complex organisational environments.
My longer-term objective is to contribute to practical approaches that help organisations move beyond transaction processing and reporting toward more adaptive, evidence-informed, and operationally effective decision-making.
Current Research Direction
Current research interests focus on AI-driven decision support, uncertainty-aware ERP systems, inventory digital twins, demand sensing, and operational intelligence for complex organisational environments.