Overview

This page presents selected project domains and representative work that illustrate my experience in applied data science, machine learning, optimisation, and decision intelligence, across both academic and industry contexts.

Rather than cataloguing individual tools or isolated experiments, the emphasis is on how analytical methods are formulated, integrated, and operationalised to support real-world decision-making under uncertainty and constraints.

Across projects, my role typically spans:

Problem formulation from loosely defined, real-world decision contexts

Methodological and system design, combining ML, optimisation, and domain knowledge

Implementation, validation, and scalability considerations

Communication of results to both technical and non-technical stakeholders

Decision Support Systems (DSS)

AI-Driven Decision Support for Complex, Constrained Environments

A core theme of my project work is the design and implementation of AI-driven decision support systems (DSS) for environments characterised by uncertainty, multiple objectives, and operational constraints.

These projects move beyond standalone predictive models, focusing instead on decision-centric systems where analytics directly inform planning, prioritisation, and strategic choices.

Typical characteristics of these DSS projects include:

Formalising ambiguous, real-world problems into structured decision models

Identification and modelling of:

Decision variables

Hard and soft constraints

Multiple, often conflicting objectives

Sources of uncertainty and risk

Integration of:

Predictive machine learning models

Optimisation algorithms

Rule-based and domain-driven logic

Design of interpretable outputs to support trust and adoption by decision-makers

These systems have been applied to problems such as:

Operational planning and scheduling

Resource allocation under constraints

Scenario evaluation and trade-off analysis

Strategic decision support in large-scale industrial and energy systems

The overarching goal is to ensure that analytical outputs are actionable, transparent, and decision-relevant, rather than technically sophisticated but operationally disconnected.

Optimisation-Focused Projects

Large-Scale and Multi-Objective Optimisation Applications

Many of my projects centre on optimisation-driven decision-making, particularly in settings where classical exact methods alone are insufficient due to scale, uncertainty, or problem complexity.

Representative optimisation-focused work includes:

Large-scale linear, nonlinear, and mixed-integer optimisation

Multi-objective optimisation and Pareto-based trade-off analysis

Constraint handling techniques for infeasible or noisy real-world data

Hybrid approaches combining exact optimisation with metaheuristics

In several projects, optimisation models are embedded within broader DSS frameworks, allowing decision-makers to:

Explore alternative solutions under varying assumptions

Understand trade-offs between competing objectives

Evaluate feasibility, risk, and performance simultaneously

This work reflects a strong emphasis on practical solvability and interpretability, rather than purely theoretical optimality.

Machine Learning & Forecasting Projects

Predictive Analytics for Planning and Operations

I have designed and delivered machine learning and forecasting solutions intended to directly support planning, operational decision-making, and performance monitoring.

These projects typically involve:

Development of predictive analytics pipelines aligned with operational and business KPIs

Time series forecasting for short-, medium-, and long-term planning horizons

Use of machine learning and deep learning models to capture non-linear dynamics

Incorporation of uncertainty estimation and scenario analysis

A key design principle across these projects is that forecasts are decision inputs, not final outputs. As such, models are evaluated not only on predictive accuracy, but also on:

Stability under changing conditions

Interpretability and explainability

Usefulness for downstream optimisation or policy decisions

Results are communicated through clear reports, dashboards, and scenario summaries, enabling stakeholders to translate predictions into concrete actions.

Integrated ML–Optimisation Projects

Learning-Enhanced Decision Systems

Several projects explicitly integrate machine learning with optimisation, reflecting my broader research agenda of combining learning and decision-making.

In these projects:

Machine learning models are used to:

Forecast demand, cost, or system behaviour

Learn representations or surrogate models

Capture uncertainty and variability

Optimisation models consume these outputs to:

Recommend decisions

Allocate resources

Evaluate trade-offs under constraints

This integration enables:

Adaptive decision-making

Scenario-based planning

More robust and flexible decision policies

Such hybrid ML–optimisation systems are particularly valuable in large-scale industrial, energy, and infrastructure contexts, where decisions must be revisited as new data becomes available.

Data Intelligence (Text Mining & Bibliometrics)

Data-Driven Insight from Unstructured Information

In addition to numerical and operational data, I have led and contributed to data intelligence projects focused on extracting insight from unstructured textual data.

This work includes:

Text mining and topic modelling to identify emerging themes

Bibliometric and citation analysis

Collaboration and network analysis

Research landscape mapping and evidence synthesis

These projects support:

Strategic research planning

Policy-relevant analysis

Systematic literature reviews

High-level decision support based on large document collections

Emphasis is placed on reproducibility, transparency, and interpretability, ensuring that insights can be trusted and reused.

High-Performance & Scalable Computing

Computationally Intensive Analytics and Optimisation

Several projects involve computationally demanding models, requiring careful attention to scalability and performance.

This includes:

Large-scale optimisation problems

High-dimensional machine learning models

Parallel and GPU-accelerated computation

Efficient algorithmic design for real-world deployment

The focus is on achieving a balance between computational efficiency and decision quality, particularly in time-sensitive or large-scale applications.

Cross-Cutting Methodological Philosophy

Across all projects, a consistent methodological philosophy is applied:

Decision-first design, rather than model-first development

Tight integration of machine learning, optimisation, and domain knowledge

Emphasis on robustness, scalability, and interpretability

Clear articulation of assumptions, limitations, and trade-offs

This approach ensures that analytical solutions deliver practical value, not just technical novelty.