School of Electronic Engineering and Computer Science

Dr William Marsh


Senior Lecturer

Telephone: +44 20 7882 5254
Room Number: Peter Landin, CS 420a
Office Hours: Wednesday 10:00-12:00


Embedded Systems (Postgraduate/Undergraduate)

This module provides a practice-oriented introduction to embedded real-time systems. The main topics are (1) Modelling and simulation in UML and state-of-the-art tools; (2) Basic concepts of micro-controllers; (3) Real-time systems with interrupts and schedulers; (4) Real-time operating systems: processes and communication; (5) Energy aware design and construction; (6) Debugging and testing as part of software development processes.

Real-Time and Critical Systems (Postgraduate)

Covers real-time systems, include scheduling

Real-Time and Critical Systems (Undergraduate)

Most computer systems do not sit on desks but are inside machine such as cars and medical devices. Building on modules in operating systems and software engineering, this module introduces techniques for the safety analysis of such systems and for real-time system development.


Research Interests:

Medical Decision Support Models: Data, Knowledge and Evidence

Can data be used for decision-making? In many applications there are not enough data, key values are not directly observed or the problem requires reasoning about change. In these cases, it is better to combine data and knowledge for building a decision model.

Many medical decision problems fit this pattern. However, given the long history of clinical trials, clinicians are reluctant to assume an understanding of causes even when trials are completely impractical. Recent work on decision making in trauma surgery has shown the potential of causal models implemented using Bayesian networks. However, there are still many challenges before the use of these models can become routine.

Safety, Reliability And Risk: Modelling Accidents & Incident Causes

Analysing what can go wrong is fundamental for assessing risk in safety systems. Existing approaches have a number of deficiencies: (1) human behaviour and technical failures are poorly integrated (2) model created for system approval are often not used when a system is in operation (3) information on incidents and procedural compliance is not used to update risks.

The aim of the research is to extend existing accident-based modelling techniques to resolve these problems. Recent work has proposed a new model structure using a Bayesian network for causal modelling from accident / incident data, with the aim of predicting the likely safety / reliability improvement that would be achieved by changes in the operation of a system at a specific location.