Who am I? What do I do?
I am Reader in Machine Learning ... (Reader?! what's that?
I am also Director of Research for the School of Computer Science.
I'm based in the Machine Learning and Optimization Group
and my research interests vary between many topics, e.g. feature selection/extraction with information theoretic methods, ensembles (e.g. Boosting, Random Forests), and recently I am looking at efficient deep neural networks. Primarily I work on the theory behind these algorithms, so my applications are highly varied, not constrained to a single domain.... or, in less technical jargon, click here
0161 275 6190 /
* NEW *
Estimating Mutual Information in Underreported Variables
Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, Gavin Brown
Presented at Intl Conference Probabilistic Graphical Models (PGM 2016), Lugano, Sept 2016.
Published in Journal of Machine Learning Research JMLR: Workshop and Conference Proceedings
, volume 52, 2016.
1 June 2016) - Extremely pleased to announce a partnership with the Advanced Analytics Centre at AstraZeneca.
The AZ team, led by James Weatherall
is now funding Kostas Sechidis
on the very first AstraZeneca/Manchester Data Science Fellowship
Kostas will be working on statistical machine learning approaches to subgroup discovery and personalized medicine,
which is partly an extension of some key
from his PhD thesis
NEWS:(21st Oct 2015)
Check out my Bayes Theorem Tribute to Back to the Future
I will be giving a keynote at the Spanish National Conference on Artificial Intelligence (CAEPIA 2015)
Extremely pleased that Veronica Bolon
will be joining the group as a postdoc for a 2 year period starting in May, whilst her colleague Diego Fernandez is joining us for 3 months from April ... all courtesy of the Galician Regional Government of Spain. Thankyou Galicia!
Two new papers - published in the Intl Workshop on Multiple Classifier Systems by my students Nikolas Nikolaou and Sarah Nogeuira - well done!
NEWS: (26 Feb 2015) Interview for Children's BBC Newsround - explaining how a deep neural network built by Google Deepmind learnt how to play video games like Breakout, Pacman and Pong.... and won!
: (November 2014): My student Kostas Sechidis won Best Student Paper at ECML 2014. His work, on positive unlabelled learning
, shows how to perform statistical tests in semi-supervised scenarios, very common in Big Data.
Children's BBC appearance again - explaining swarm robots this time. The Newsround article is HERE
, possibly only available for a short time.
For all you 7-year olds out there, I just appeared on Children's BBC explaining what 'artificial intelligence' is. See the full clip HERE.
VERY GOOD NEWS! :
Very pleased to announce that my PhD student Adam Pocock has just won
the BCS Distinguished Dissertation Award
2013! Read a BCS press release here
! Read his thesis HERE
! The judging panel said of the thesis: "The judges were very impressed by the fact that the thesis not only makes a major advance of the state of the art, but also illustrates the context of the problem and motivates the work in a way that a general audience would be able to understand. One reviewer observed that he would use it as a standard reference in the area, and recommend it to his students as a model to be aspired to."
VERY GOOD NEWS!
: One of my undergraduate project students, Laura Howarth-Kirke, just won the award for Best
Undergraduate Science, Engineering and Technology Student in the UK! Very proud of her. Read
the full story HERE
As a nice follow up, I won Best
. Very nice indeed!
NEWS (Nov 2013)
: I am co-chairing SPR 2014
along with Marco Loog. Please submit your best papers! Note the special journal spotlight track, for articles published in JMLR/PAMI/TNNLS the past year.
Submissions are open til March 1st 2014.
NEWS (July 2013):
I presented a lecture for children, on the topic of "Making Computers Think". The version below is on YouTube, but there is a higher resolution copy available if you email me.
NEWS (June 2013):
New grant. I am Co-I, along with Mikel Lujan (PI) on a new EU-funded project, AXLE, Analytics on Xtremely Large European Databases
. The principle here is to explore how to study large scale data analytics
(including machine learning algorithms as a special case) on very large data. We want to get to the point where 10tb
is considered "normal". This is in collaboration with various EU partners, in particular Janez Demsar and friends
who developed the Orange
Richard Stapenhurst PhD
My PhD student Richard recently finished his thesis. You can see it here
Talks on the JMLR paper
Adam Pocock PhD
My PhD student Adam Pocock recently finished his thesis. You can see it here
We presented our work on feature selection at ICML 2012 in the ML-journaled special sessions.
You can watch the talk here
. And... thanks to Charles Sutton, the "wordcloud" is this....
I have been touring somewhat, giving talks about our recent JMLR paper -
thanks for the invites everyone!
Visiting... Surrey Elec Eng, Birmingham Computer Science, Manchester Medical School.... next scheduled talk: Oxford (Mathematics Dept) in May.
REUNITE project featured by BBC World Service
The BBC's flagship technology programme "Click" recently featured our
project. You can hear the podcast here, or watch the
Papers accepted to AISTATS 'Informative Priors for Markov Blanket Discovery', and to UAI "Boosting as a
Product of Experts"
AstraZeneca MSc Research Bursaries
I am currently investigating biomarkers for lung cancer analysis with AstraZeneca Research. AZ have sponsored our students this year,
under their predictive safety science initiative.
Invited Doctoral Lecture Course University of Cagliari, Sardinia
I delivered a series of 8 invited lectures in Cagliari - see the course webpages here.
Invited lecture at IEEE symposium
I am delivering a keynote at the 2011 IEEE symposium on Computational Intelligence, on the topic
of computational intelligence in dynamic and uncertain environments.
New book chapter - Ensemble Learning
I wrote an invited
book chapter for the
Springer Encyclopedia of Machine
You can see also the typeset article here.
"The study of ensemble methods, with model outputs
considered for their abstract properties rather than
the specifics of the algorithm which produced them,
allows for a wide impact across many fields of study.
If we can understand precisely why, when, and how particular
ensemble methods can be applied successfully,
we would have made progress toward a powerful new
tool for Machine Learning: the ability to automatically
exploit the strengths and weaknesses of different learning
New PhD (Dec 2010) : - Manuela Zanda completed her PhD, entitled ``A Probabilistic Perspective on Ensemble
Diversity''. A copy of her thesis can be downloaded here.
New Grant (9th Sept 2010) - EPSRC KTA, Reuniting Refugees with Computational Intelligence.
REUNITE is a research project aiming to utilise
crowdsourcing and machine
techniques to help reunite those separated by conflict and natural disaster.
Imagine the following scenario. A disaster occurs in a remote part of the developing world. The local
population are forced to flee their homes. Many are separated from their family and friends. With no
mobile or Internet communication, finding loved ones in the aftermath of a disaster is incredibly
difficult. Relief organisations go to great lengths to help people find those they are missing. The
system we are developing aims to make this process easier, faster and more secure.
Article in THE (3 June 2010): I had an article about computer
science education in the Times Higher this week. Are you looking for the Computing at
School group? Or for the Manchester Schools' Animation
Competition? The Animation competition is an effort led by Toby Howard, to
encourage schoolchildren to learn the concepts of computational thinking, and I strongly encourage all to
Invited plenary talk at MCS 2010
I gave an invited talk at the Intl
Multiple Classifier Systems 2010, entitled Some Thoughts at the Interface of Ensemble Methods
and Feature Selection. It was repeated with (slightly) adapted slides for Microsoft Research Cairo,
New PhD (Nov 2009) : - Amir Ahmad completed his PhD, entitled ``Data Transformations for
Decision Tree Ensembles''. A copy of his thesis can be downloaded here.
AISTATS 2009 paper - Feature Selection with Information Theory
The traditional approach to so-called filter methods in feature selection is to construct
a criterion to measure the utility of any given feature. The more sophisticated methods penalize
feature-feature correlations (`redundancy') with various penalty terms. The last 15 years have produced a flood
of papers advocating different penalty terms. My recent work shows that the vast majority of these can be naturally
derived from a single framework, using multivariate information theory. The work reveals
that there exists a natural, smooth space space of feature selection criteria, where each paper over
the last 15 years corresponds to one point. Most of the
space has never been explored. See the AISTATS 2009 paper
Invited plenary at UK-KDD 2009 - Feature Selection by Filters, a Unifying Perspective
I gave an invited talk at UK-KDD 2009.
New Grant - Dynamic Ensemble Techniques (EPSRC grant EP/F023855/1)
With colleagues at Bristol, I am investigating how dynamic ensemble techniques can
tackle multi-step (control) and
nonstationary problems. This is in collaboration with Tim Kovacs,
James Marshall and
conducted under our EPSRC funded ADEPT project.
New Grant - Machine Learning for Multi-Core Computers (EPSRC grant EP/G000662/1)
The computer industry is undergoing the "multi-core" revolution. When you buy a PC off the shelf
these days, it is inevitably "dual-core" or "quad-core". This idea of more and more
CPU "cores" executing in parallel is expected to continue to the hundreds and thousands. The problem of
coordinating these cores is challenging and unsolved. With Mikel Lujan and
Jeremy Singer I am working on applying Machine Learning to this
problem, conducted under our EPSRC funded iTLS project.
IEEE TNN paper on Sparse Distributed Memories
In a project with Steve
Furber I found that sparse distributed memory models like the correlation matrix memories of Wilshaw and Kanerva could give
significant insights into the design of fault tolerant computer architectures. This resulted in a IEEE TNN paper
I worked for a long while on the issue of diversity in ensembles, with Jeremy Wyatt. A summary of the work can be found on
this page. A slightly less optimistic (but rather
insightful) take on the field is found here.
Image Feature Extraction
I did a nice project with Honda several years ago, which turned into a patent,
on image feature extraction - I follow
up little avenues on this occasionally. Throughout this time I have maintained an interest in
evolutionary speciation and optimisation, which has spun off into several useful
Wow, you read all the way to the bottom.
Please don't click here.