Call for papers: MLSP 2010, the Twentieth IEEE International Workshop on Machine Learning for Signal Processing

First Call for Papers for the

Twentieth IEEE International Workshop on Machine Learning for Signal Processing
(MLSP 2010)

August 29 – September 1, 2010, Kittila, Finland

Website: http://mlsp2010.conwiz.dk

IMPORTANT DATES:

Submission of full papers: April 1, 2010
Notification of acceptance: May 28, 2010
Camera-ready paper and author registration: June 18, 2010
Advance registration before: June 23, 2010

The 2010 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL
PROCESSING (MLSP 2010) will be held in Kittila, Finland, in
August-September 2010. MLSP 2010 is the twentieth workshop in the
series of workshops sponsored by IEEE Signal Processing Society. It
will present the most recent and exciting contributions in machine
learning for signal processing through keynote talks as well as
special and regular single-track sessions.

INVITED SPEAKERS:

– Prof. Zoubin Ghahramani, University of Cambridge
– Prof. Tom Mitchell, Carnegie Mellon University
– Dr. Henry Tirri, Head of Nokia Research Center

ORGANIZATION:

General chair: Erkki Oja
Program chairs: Samuel Kaski, David Miller
Special session chairs: Samy Bengio, Mikko Kurimo
Publicity chairs: Marc Van Hulle, Jaakko Peltonen
Web and publication chairs: Antti Honkela, Jan Larsen
Data competition chairs: Vince Calhoun, Kenneth Hild, Mikko Kurimo
Local arrangements: Tapani Raiko (chair), Francesco Corona, Ali Faisal, Mari-Sanna Paukkeri

VENUE:

MLSP 2010 will be held in the Levi Summit conference and exhibition
centre in Kittila, Finland. Levi is one of the largest resorts in
Finnish Lapland, north of the Arctic Circle. In the summer, Levi
offers many sports activities as well as lots of wild northern nature.
The conference centre is located high on the hillside of the Levi
fell, accessible by gondola from the main village.

CONFERENCE TOPICS:

Machine learning in signal processing is concerned with tasks such as
detection, estimation, prediction, classification, and optimization,
with a wide range of applications. The following is a non-exhaustive
list of topics for MLSP 2010:

– Bayesian learning and signal processing
– Cognitive information processing
– Graphical and kernel methods
– Information-theoretic learning
– Learning theory and algorithms, including bounds on performance
– Supervised learning, including signal detection, pattern
recognition and classification
– Unsupervised learning, reinforcement learning
– Source separation and component analysis
– Data fusion and integration
– Feature extraction, information visualization
– Sparse and structured representations
– Neural network learning
– Time-series analysis
– Adaptive filtering
– Data mining, information retrieval
– Sequential learning and sequential decision methods
– Hardware implementation of machine learning in signal processing
– Applications of machine learning: Bioinformatics, Biomedical and
neural signal processing, Neuroinformatics, Speech and audio
processing, Image and video processing, Computer vision,
Sensor networks, Robot control, Communications, Cognitive radio,
Multimodal interfaces and context modeling, Intelligent multimedia
and web processing

SPECIAL SESSION:

A special session “Towards multimodal proactive interfaces using
large-scale machine learning” is being organized. For more
information see http://mlsp2010.conwiz.dk .

DATA COMPETITION:

In conjunction with the workshop, a data and signal analysis
competition is being organized. Winners will present their works and
receive their award during the Workshop.

PAPER SUBMISSION PROCEDURE:

Authors are invited to submit a double column paper of up to six pages
using the electronic submission procedure described at
http://mlsp2010.conwiz.dk

Accepted papers will be published by IEEE Press and electronic
proceedings will be distributed at the Workshop.

SPONSORS: MLSP 2010 is supported by IEEE, by the IEEE Signal
Processing Society, and by the PASCAL2 Network of Excellence.

See http://mlsp2010.conwiz.dk for more details!

Call for participation: Workshop “µTOSS 2010 – Multiple Comparisons from Theory to Practice”

Call for Participation
Call for Poster Submissions

µTOSS 2010 Berlin –
Multiple Comparisons from Theory to Practice
February 15-16, 2010, Berlin, Germany

http://www.amiando.com/mutoss.html

** PASCAL2 Core Event **

Important Dates
===============

Submission Deadline: 2010-01-15
Notification of Acceptance: 2010-01-22
Registration Deadline: 2010-01-24
Workshop Date: 2010-02-15 – 2010-02-16

Organization
============

Bernstein Focus: Neurotechnology (BFNT-B)
Berlin Institute of Technology (TUB)
Weierstrass Institute for Applied Analysis
and Stochastics (WIAS) Berlin

Klaus-Robert Mueller, TUB and BFNT-B
Thorsten Dickhaus, TUB
Gilles Blanchard, WIAS
Matthias L. Jugel, BFNT-B
Imke Weitkamp, BFNT-B/Workshop Coordination

Description
===========

Under the scope of the “Harvest” programme
of the PASCAL2 European Network of Excellence,
Gilles Blanchard, Thorsten Dickhaus and Klaus-Robert Mueller,
together with five participants from three countries, will carry out
a software project for multiple testing at TU Berlin from
January 18th to February 12th, 2010
(for details see http://user.cs.tu-berlin.de/~dickhaus/mutoss.html).

As a round-off of the project, the workshop “µTOSS 2010 Berlin –
Multiple comparisons from Theory to Practice” will take place in
collaboration with the Bernstein Focus Neurotechnology Berlin on
February 15th and February 16th, 2010. The workshop shall serve two
purposes. On the one hand, we will have invited speakers who present
talks on multiple testing methodology and practice in a tutorial style
for an audience that is familiar with basic concepts of statistical
testing, but has no particular expertise in MCPs. Directly after such a
theoretical talk, we will present how the things which have just been
presented can be realized in our new software (this practical
presentation will be done by one of the coding team members).

In a separate time slot, contributed posters will be presented.

Submission
==========

Please send your abstracts (max. 1 page, PDF or plain text)
no later than 2010-01-15 to the poster submission email address

Postdoctoral position in machine learning, INRIA – Ecole Normale Superieure

Applications are invited for several 1-year or 2-year postdoctoral
positions within the joint INRIA/CNRS/Ecole Normale Superieure
computer science laboratory in downtown Paris, working with Francis
Bach. The position is funded by the European Research Council and is
part of the SIERRA project on sparse methods for machine learning.

The goals of this project are to explore sparse structured methods for
machine learning, with applications in computer vision and audio
processing. The project is expected to make contributions to all
aspects of machine learning, from learning theory to convex
optimization (see relevant publications at
http://www.di.ens.fr/~fbach/sierra/). Therefore candidates with
diverse backgrounds are expected.

The Sierra project is carried out within the INRIA Willow project-team
and the postdoc will have the opportunity to interact with the rest of
the team, which has a diverse and interdisciplinary expertise in
machine learning and computer vision (see
http://www.di.ens.fr/willow/).

Candidates must have a PhD in machine learning or related field, with
expertise in at least two of the following areas: sparse methods,
kernel methods, convex optimization, computer vision, audio
processing, signal processing.

Candidates should send a detailed CV, a statement of research
interests, and the names and full contact details (including e-mail
addresses) of at least two referees to Francis Bach
(francis.bach (at) ens.fr).

Workshop in Mixture Estimation and Applications, Edinburgh, March 3-5, 2010

WORKSHOP IN MIXTURE ESTIMATION AND APPLICATIONS
International Centre for Mathematical Sciences (ICMS)
Edinburgh, March 3-5, 2010
Organisers: Kerrie Mengersen, Christian Robert, Mike Titterington
http://www.icms.org.uk/workshops/mixture

Statistical mixture distributions are used to model scenarios in which certain variables are measured but a categorical variable is missing. For example, although clinical data on a patient may be available their disease category may not be, and this adds significant degrees of complication to the statistical analysis. The above situation characterises the simplest mixture-type scenario; variations include, among others, hidden Markov models, in which the missing variable follows a Markov chain model, and latent structure models, in which the missing variable or variables represent model-enriching devices rather than real physical entities. In the title of the workshop the term ‘mixture’ is taken to include these and other variations along with the simple mixture. The motivating factors for this three-day workshop are that research on inference and computational techniques for mixture-type models is currently experiencing major advances and that simultaneously the application of mixture modelling to many fields in science and elsewhere has never been so rich. We thus assembling top players, from statistics and computer science, in both methodological research and applied inference at this fertile interface. The methodological component will involve both Bayesian and non-Bayesian contributions and biology and economics will feature strongly among the application areas to be covered.

The following people have agreed to give talks or invited discussion contributions: John Geweke, Sylvia Fruhwirth-Schnatter, Gilles Celeux, Agostino Nobile, Murray Aitkin, Geoff McLachlan, Jihua Chen, Bruce Lindsay, Michael Jordan, Yee Whye Teh, Chris Williams, Mark Girolami, Katherine Heller, Christophe Andrieu, Paul Fearnhead, Arnaud Doucet, Olivier Cappe, Kim-Anh Do, Peter Mueller, Michael Newton, Chris Holmes, Robert Kohn, Hedibert Lopes and Richard Gerlach.

Attendance at this workshop is by invitation only and the total attendance will be strictly limited. If you wish to be considered for an invitation please contact Mike Titterington at m.titterington (at) stats.gla.ac.uk by November 30, 2009.

Call for Papers – 2nd Workshop on Cognitive Information Processing (CIP2010)

This call is also available as a pdf document at http://www.pascal-network.org/cfp/cip2010.pdf

Following the success of the first edition of the workshop on Cognitive Information Processing, we are pleased to announce the second one in this series. This workshop aims at bringing together researchers from the machine learning, pattern recognition, statistical signal processing,
communications and radar communities in an effort to promote and encourage cross-fertilization
of ideas and tools.

CIP2010 will take place on June 14-16, 2010, in Italy, in the island of Elba, at the Grand Hotel Elba International (http://www.elbainternational.it), which dominates the beautiful Bay of Naregno.

CIP2010 is sponsored by the International Association for Pattern Recognition (IAPR), the European Association for Signal Processing (EURASIP), and the Institution of Engineering and Technology (IET), and it has the technical co-sponsorship of the IEEE Aerospace and Electronic Systems Society (AESS) and the IEEE Signal Processing Society (SPS)

The workshop will feature six keynote addresses and technical presentations, oral (invited) and poster (invited & regular), all of which will be included in the workshop proceedings.

Prospective authors are invited to submit full length (six pages) papers via the conference website for presentation in any of the areas listed below. All submitted papers will be subjected to a blind peer-review process.

Theory:
. Learning theory and modeling;
. Bayesian learning and models;
. Information theoretic learning;
. Graphical and kernel methods;
. Adaptive learning algorithms;
. Ensembles: committees, mixtures, boosting, etc.;
. Data representation and analysis;
. Collaborative sensing techniques;
. Other topics for cognitive information processing.

Applications:
. Cognitive radio networks;
. Cognitive radio modulation techniques;
. Dynamic spectrum management;
. Opportunistic resource allocation;
. Cognitive radar and sonar;
. Knowledge based target detection, estimation, tracking and identification;
. Waveform agility design;
. Blind source separation;
. Cognitive dynamic systems;
. Distributed, cooperative, and adaptive processing;
. Remote sensing;
. Other application areas.

Accepted papers will be published in the Proceedings of CIP2010.

More information can be found on the workshop website at: http://www.conference.iet.unipi.it/cip2010/

Important deadlines:

Full six-page paper submission: January 10, 2010
Notification of acceptance: March 10, 2010
Final camera-ready papers and registration: April 10, 2010

The Organizing Committee:

General Co-Chairs
Fulvio Gini, University of Pisa, Italy
f.gini@iet.unipi.it
Sergios Theodoridis, University of Athens, Greece
stheodor(at)di.uoa.gr

Technical Program Co-Chairs
Maria Sabrina Greco, University of Pisa, Italy
m.greco(at)iet.unipi.it
Merouane Debbah SUPELEC, France
merouane.debbah(at)supelec.fr

Publication Chair
Robert J. Baxley, Georgia Tech Research Institute, USA
baxley(at)gatech.edu

Local Accomodation and Publicity Chair
Marco Martorella, University of Pisa, Italy
m.martorella(at)iet.unipi.it

Webmaster
Pietro Stinco, University of Pisa, Italy
pietro.stinco(at)iet.unipi.it

Far East Liaison
Hing Cheung SO, City University of Hong Kong, Hong Kong
hcso(at)ee.cityu.edu.hk

4-year PhD studentships: Statistical Mechanics approaches to Systems Biology

Statistical Mechanics approaches to Systems Biology

Several 4-year BBSRC-funded studentships available

We expect to have a number of BBSRC-funded studentships available in the area of Systems Biology, focussing on the mathematical analysis of dynamical processes on biological networks (e.g. protein interaction networks) using techniques from Statistical Mechanics. Projects will be supervised in close collaboration between members of the Disordered Systems group in the Department of Mathematics at King’s College London, and members of the Randall Division of Cell and Molecular Biophysics and of the Bioinformatics group in the Department of Medical and Molecular Genetics

The proposed projects cover topics including

* Mathematical theory of gene regulation networks – from interacting microscopic components to macroscopic behaviour

* Statistical mechanics of complex formation in large molecules

* How do we account for what we don’t know? – The effects of incomplete knowledge on the dynamics of protein interaction networks: from statistical physics and machine learning to experiment

Further details and project descriptions can be found at http://www.mth.kcl.ac.uk/~psollich/BBSRC/

Suitable candidates for these studentships should have a first or upper second class degree in mathematics, theoretical physics or a related discipline with a strong mathematical component. They must be U.K. citizens or have another relevant connection with the U.K., which normally amounts to having been resident in the U.K. for 3 years or more (see http://www.bbsrc.ac.uk/funding/studentships/studentship_eligibility.pdf). Studentships are fully funded and provide all course fees for the 4-year Ph.D. programme, as well as a stipend of around GBP15,000/year.

The deadline for applications is expected to be in late December 2009, or early January 2010. In the mean time, interested candidates are encouraged to contact the 1st supervisors for the proposed projects for further information (see http://www.mth.kcl.ac.uk/~psollich/BBSRC/); please attach a CV. At the time of application, students would also need to provide a list of three projects in order of preference. Studentships will be awarded competitively following shortlisting and interviews, after which allocation of students to projects will be finalized. Studentships would be expected to be taken up in Sept 2010.

Job openings at Statistical Lab in Cambridge, UK

Statistical Laboratory, Department of Pure Mathematics and Mathematical
Statistics

University Lectureship in Statistics – Ref LF05932
Salary: GBP 36,532-46,278 pa
Closing Date: 9 December 2009
Applications are invited for a University Lectureship in Statistics, to be
held in the Statistical Laboratory and filled by 1 September 2010 or by
negotiation. Appointment will be made at an appropriate point on the scale
for University Lecturers and will be for a probationary period of five
years with appointment to the retiring age thereafter, subject to
satisfactory performance.

Post-doctoral Research Associates in Statistics – Ref LF05879
Salary: GBP 27,183-35,469 pa
Closing date 9th December 2009
Applications are invited for Postdoctoral Research Associate positions in
Statistics, to be held in the Statistical Laboratory and to commence on a
date to be negotiated. These research posts, which are available for
between two and three years, are associated with a major expansion of
personnel and activities in core statistical methodology and its
applications.

For full details and how to apply, please see
http://www.statslab.cam.ac.uk/Vacancies

Postdoc in Feature Selection / Ensemble Learning at Manchester

Applications are invited for a 2 year postdoc position at the University
of Manchester, hosted in the Machine Learning and Optimization group,
working with Dr Gavin Brown.

The post is to work in the area of feature selection/extraction in
environments exhibiting concept drift. A potential direction is to
construct and maintain small, interpretable rule-sets (ensembles) which
have high predictive / explanatory power, under shifting data
distributions. Our current focus is on information theoretic methods,
though other well-founded approaches are welcomed.

The precise direction of the work is not set in stone, so stronger
candidates will be afforded a degree of research independence.

The successful candidate will have expertise in one or more of the
following areas:

– information theory,
– feature selection/extraction,
– ensemble learning,
– rule ensembles (e.g. Friedman et al)
– rule extraction/mining
– concept drifting problems

Interested candidates should mail Gavin Brown, with:

– your full academic CV in PDF format.
– your two best publications
– two reference letter(s) from appropriate academic sources

Applications close : 10th December
Interview invites : 17th December
Interviews : Early January

Applications will close on 10th December, however CVs sent before this
date will be considered for early interview.

Relevant sites:
MLO Group : http://intranet.cs.man.ac.uk/mlo/
Gavin Brown : http://www.cs.manchester.ac.uk/~gbrown/

Web version of PASCAL brochure now available

A web version of the PASCAL brochure can now be viewed at the following link:

http://www.pascal-network.org/webbrochure/

If you would like to receive a hardcopy of the brochure, please contact Rebecca Martin by email (rebecca.martin (at) cs.ucl.ac.uk)

MASH: Phd and post-doc positions in machine learning

* ABSTRACT

The MASH project is a three-year research initiative which brings
together five institutions with expertise in statistics, machine
learning, goal planning and computer vision to investigate the
collaborative design of complex hand-designed priors for machine
learning.

MASH is funded by the Information and Communication Technologies
division of the European Commission, Cognitive Systems and Robotics
unit, under the 7th Research Framework Programme.

Research will start in January 2010 and will be carried out in
Switzerland (IDIAP), France (CNRS and INRIA), Germany (WIAS) and
Czech Republic (CVUT). Open positions are listed below.

You can already register on http://www.mash-project.eu to get
updated by mail on the status of the project.

* SUMMARY

The goal of the MASH project is to create new tools for the
collaborative development of large families of feature extractors.
It aims at starting a new generation of learning software with
great prior model complexity.

The project is structured around a web platform which will be open
to external contributors early in 2010. It comprises collaborative
tools such as a wiki-based documentation and a forum, and an
experiment center which runs and analyzes experiments on a
continuous basis.

The applications targeted by the project are classical vision
problems, and goal-planning in a 3D video game and with a real
robotic arm.

Contributors will participate to the project by uploading the
source codes of “feature extractors” into the platform. Each one of
these extractors processes an input image to generate values
relevant to the system. This purposely broad definition spans from
classical vision processing such as edge detector or color
histogram estimation, to highly dedicated hand-designed templates
or event-based memory for the robotic applications. The system
concatenates all these extractors to create a very large feature
vector, which is used as an input signal for a machine learning
algorithm.

In practice, anybody can upload such a module at any time. It will
be immediately compiled and integrated in the next starting
experiment. Preliminary performance measures will be provided in a
matter of minutes, and complete results a few hours later. The
system encourages contributors to improve upon the work on other
and focus on the main weaknesses of the overall system.

The scientific issues to be tackled along the course of the project
are numerous, from standard machine learning questions such as
learning and prediction with very large feature spaces and tight
computational constraints, to original problems related to
clustering in a functional space.

* CONSORTIUM

– Idiap Research Institute, Switzerland (IDIAP)

– Centre National de la Recherche Scientifique, France (CNRS)

– Weierstrass Institute for Applied Analysis and Stochastics,
Germany (WIAS)

– Institut National de Recherche en Informatique et en Automatique,
France (INRIA)

– Czech Technical University in Prague, Czech Republic (CVUT)

* OPEN PHD POSITION AT IDIAP, SWITZERLAND

Contact point: Dr. François Fleuret,
francois.fleuret (at) idiap.ch,
http://www.idiap.ch/~fleuret/

On-line application at http://www.idiap.ch/~fleuret/hiring-mash.html

The selected candidate will be a doctoral student at EPFL EDEE
doctoral school. Research will be done at the Idiap Research
Institute, under the supervision of Dr. François Fleuret.

The research to be carried out will be the study of prediction
techniques for goal-planning with very large feature space. The
candidate will investigate prediction from images, mimicking to
learn policies provided by human operators, and extensions of
classical Markovian Modeling to the specificity of the MASH
project.

This work will mix theoretical developments in statistical learning
with the implementation of algorithms working on real-world data.

Applicants must have a strong background in mathematics and be
self-sufficient in programming. They must be familiar with several
of the following topics and interested in all of them:
probabilities, applied statistics, information theory, signal
processing, optimization, algorithmic, and C++ programming.

* OPEN POSITIONS AT CNRS, FRANCE

Contact point: Dr. Yves Grandvalet,
yves.grandvalet (at) utc.fr,
http://www.hds.utc.fr/~grandval/

We have open PhD and PostDoc positions to develop clustering and
block-clustering algorithms that will summarize heuristic behaviors
across tasks. We aim at providing feedback to the heuristic
designers by detecting similar heuristics across similar tasks,
thus empowering designers to analyze coexisting strategies, and to
detect critical failures.

We will develop clustering and block-clustering methods based on
probabilistic models and factorization techniques. We will also
study the relationships between these approaches.

The candidates will hold a Master/PhD in applied mathematics or
computer science, and should have interest in both areas. They will
work under the supervision of Y. Grandvalet and G. Govaert at the
Heudiasyc lab. http://www2.hds.utc.fr/ at University of Technology
of Compiègne http://www.utc.fr/the_university/index.php

* OPEN POSITIONS AT WIAS, GERMANY

Contact point: Dr. Gilles Blanchard,
gilles.blanchard (at) wias-berlin.de,
http://www.wias-berlin.de/people/blanchar/

The research will be carried out at the Weierstrass Institute,
Berlin, under the supervision of Dr. G. Blanchard; the selected
candidate will be a doctoral student at the Humboldt University,
Berlin.

The research will concentrate on theoretical and practical
developments of prediction techniques from a large set of
heterogeneous features: aggregation, sparsification, grouping and
reduction techniques, in particular under a strong limitation
constraint of the computational burden. Automated construction of a
similarity or distance measure between features will be also
addressed.

Specific Requirements: university degree (at least master/diploma)
in mathematics, computer, science or engineering. We expect from
potential candidates very good programming skills (C++) and at
least basic knowledge in mathematical statistics, theory of machine
learning and/or optimization.

* OPEN POSITIONS AT INRIA, FRANCE

Contact point: Dr. Olivier Teytaud,
olivier.teytaud (at) inria.fr,
http://www.lri.fr/~teytaud/

The research will be carried out at the LRI, Université Paris-Sud,
under the supervision of Olivier Teytaud (INRIA research
fellow). We have open PhD and PostDoc positions.

The research will focus on theoretical and practical developments
of planing techniques from a large set of heterogenous features.

Specific Requirements: university degree (at least master) in
mathematics, computer science or engineering. We expect from
potential candidates very good programming skills (C++) and at
least basic knowledge in machine learning and/or planning.