Chris Williams: Research Interests
I am interested in a wide range of theoretical and practical issues in
machine learning, statistical pattern recognition, probabilistic
graphical models and computer vision. This includes theoretical
foundations, the development of new models and
algorithms, and applications. At a high level my interests can be
summarized as "finding structure in data".
My main areas of interest are described below:
Further information on
past research grants/projects is available.
- Models for Understanding
Time-Series/Condition Monitoring: Data that comes
from a set of sensors recording through time can have rich
structure. For example, patients in intensive care
are monitored by many sensors. Our goal is to carry out
condition monitoring, to identify
different types of artifact and pathology in real time based on
characteristic patterns in the data. See
condition monitoring of premature
babies for more details.
This approach was extended to
monitoring in an adult neuro ICU
in the Southern General Hospital, Glasgow.
Most recently we have studied how
signs are affected by drug infusions.
- Image Interpretation. Object
recognition can be cast in a statistical framework. This approach
argues for image understanding using generative models,
i.e. explaining an image by instantiated objects. My most recent
vision work is in the "vision as inverse graphics" (VIG) framework,
with PhD students Charlie Nash, Pol Moreno, and Lukasz Romaszko,
Work with Ali Eslami looked at the
Shape Boltzmann Machine for modelling object shape and parts
Other work looked at lower-level edge-based and region-based
models of images. The paper with Jyri Kivinen on
Transformation Equivariant Boltzmann Machines considers learning
to group edges while imposing rotation equivariance (i.e. the system
will detect a specified configuration of edges at any rotation), and the paper
with Nicolas Heess on
Learning generative models texture models with extended
Field-of-Experts learns higher-order random field models of
visual texture. These contour- and region-based models can be combined
to form models of textured regions.
I was also one of the organizers of the influential
Visual Object Classes challenges concerning the
recognition of object classes (e.g. cars, cats, etc) in images.
Titsias we learned sprite models of multiple objects that occur in a many
images; for further information and movies
etc click here.
In earlier work (with Nick Adams, Steve Felderhof, Xiaojuan Feng, and
Amos Storkey) we studied
the use of tree-structured belief networks (TSBNs) and Dynamic Trees
(DTs) as models of images. DTs are
TSBNs that reconfigure themselves to a given input image or image
Click here and
here for futher information.
- Artificial Intelligence for Data
Analytics: In the real world of data science, a very large
fraction of the time taken on a project is taken up with data
engineering or data wrangling. This includes data
integration, data transformation, metadata discovery, structural
variation, detection and repairing missing and anomalous data, and
The goal of the
project at the Alan Turing Institute is to
draw on new advances in artificial intelligence and machine
learning to produce technology that will help automate each stage of
the data analytics process.
- Gaussian Processes.
Since 1995 I worked on the use of Gaussian processes (GPs) for
supervised learning along with several collaborators. An overview of this work can be obtained
from the book
Gaussian Processes for
Machine Learning (C. E. Rasmussen and C. K. I. Williams,
MIT Press, 2006). Subsequent work with Edwin Bonilla
and Kian Ming Adam Chai
focussed on multi-task learning for GPs, and work with Krzysztof
addressed approximations for large-scale GPs.
- Unsupervised Learning: In addition to the work on
learning learning multiple object from images, I also worked on GTM, the Generative Topographic Mapping (along with Chris
Bishop and Markus
Svensen). I have also worked on hierarchical mixture
models, and probabilistic minor components analysis/extreme components
analysis (with Felix
Max Welling). Recent work with Cian Eastwood is on
the quantitative evaluation of disentangled representations.
- PROTEUS, an EPSRC-funded
aims to develop technology that will provide quick, in situ, in vivo diagnoses and management of lung diseases in the clinical environment.
- Identifying Malaria
Parasites in Microscopy Images. Work started with Carlos
Sánchez Sánchez as a MSc project, in collaboration with
John Quinn and his research group in Uganda.
- Machine Learning for Compiler Optimization (
Mike O'Boyle and
Harmonising chorales in the style of J S Bach using HMMs (with
Moray Allan). See also the
HMM Bach demo.
Detecting satellite tracks in sky survey data (with
We are also interested in other astronomical
tasks, e.g. star/galaxy classification.
- Haplotype reconstruction (with Michael Schouten).
- Wind field modelling using Gaussian Processes,
see Dan Cornford's pages on Neurosat at Aston Univeristy.
- Modelling spike firing in oxytocin cells (with Duncan McGregor
and Gareth Leng).
- Other: I am also interested in trying to understand the
processing and representations used in animal visual systems.