Dr. Saiful Khan is a Senior Computer Scientist at the Rutherford Appleton Laboratory, STFC, where he leads research and development of data-driven software infrastructure for large-scale scientific applications. He holds a DPhil (PhD) from the University of Oxford, where he conducted postdoctoral research under Prof. Min Chen. His work spans critical infrastructure including FAIR-MAST (nuclear fusion data), RAMPVIS (pandemic data visualization), SKA (radio astronomy), VBAS (seismological data visualization), DAFNI (national data & analytics infrastructure ), etc. With industry experience at Oracle, ABB, and other organizations, he bridges advanced research with real-world impact.

Research Interests:

• Data-driven software infrastructure
• Data visualization infrastructure
• Applied Machine Learning
• Large language models (LLM) & Agents

Major Projects and Contributions:

VIS4ML4HD: Visualization for Machine Learning for Human-centered Decision Making — Current AI/ML practice often focuses on selecting a single “best” model while discarding non-optimal alternatives. This leads to missed opportunities because we lack effective ways to understand models’ skill profiles, use them productively, or combine them meaningfully. Visualization can help address this challenge by leveraging the strengths of human visual perception to convey complex, high-volume information more efficiently than statistics or algorithms alone. It reduces cognitive load while preserving rich data context, enabling faster and more reliable reasoning. This project develops a visualization-enabled infrastructure and toolset that manages large pools of ML models and their performance profiles, supports the construction of effective model ensembles, and empowers decision makers to interpret model anomalies and reconcile conflicting predictions. Dr. Khan is a co-investigator and researcher on this EPSRC-funded project, conducted in collaboration with the University of Oxford.

Bridging the Gap: Correlative Imaging Across Length Scales — Understanding protein mechanisms in cancer progression requires integrating data across imaging scales and modalities. This UKRI-funded project combines AI/ML with advanced imaging to analyze patient biobank samples, developing automated workflows to accelerate discovery of mechanisms behind cancer progression and treatment resistance. Dr. Khan is a Co-Investigator of this project in collaboration with the Central Laser Facility (CLF), STFC.

DAFNI: Data & Analytics Facility for National Infrastructure — UK infrastructure research increasingly depends on modelling and analysis at unprecedented scale. DAFNI is a national computing platform that enables researchers to run advanced simulations across transport, water, energy, and city-scale systems, generating insights that help make infrastructure more efficient, reliable, resilient, and affordable. Dr. Khan serves as a user liaison and leads several initiatives on this project.