Research

Publications, areas of focus and work in progress.

Focus

Research areas

Artificial Intelligence

Exploring Artificial Intelligence methods for developing adaptive systems and data-driven solutions to complex real-world challenges.

Machine Learning

Exploring ML techniques to develop intelligent systems, focusing on prediction, pattern recognition, and data-driven solutions.

Deep Learning

Reproducible training, drift detection and the engineering practice that keeps a deployed model honest over time.

Natural Language Processing

Retrieval, summarisation and extraction over domain documents, with a particular interest in evaluating what these systems get wrong.

Computer Vision

Detection and segmentation in settings where labelled data is scarce and the cost of a false negative is high.

Intelligent Diagnosis

Developing intelligent systems to analyze data, identify patterns, and support accurate, timely, and reliable diagnosis.

Publications

6 in total

2026

  1. Retrieval-Augmented Generation over Long Technical Documents

    Humza Imran. Placeholder venue — replace with the real conference. Conference paper

    Abstract

    Placeholder abstract. Chunking strategy tends to matter more than the choice of retriever once documents pass a certain length. Replace this entry with your own publication.

2025

  1. Practical Drift Detection in Production Machine Learning Pipelines

    Humza Imran. Placeholder venue — replace with the real workshop. Conference paper

    Abstract

    Placeholder abstract. Most drift alarms in production are caused by upstream schema changes rather than by genuine distribution shift. Replace this entry.