SMART workshop Call for Participation

CALL FOR PARTICIPATION

Statistical Multilingual Analysis for Retrieval and Translation – Barcelona 2009
http://patterns.enm.bris.ac.uk/smart-dissemination-workshop

Barcelona May 13, 2009
Venue: Aula Teleensenyament (Tele-teaching room) in building B3 of the Campus Nord of the UPC

A joint event of SMART project – PASCAL Network jointly-located with EAMT-2009

Co-organizers: Marco Turchi, Nello Cristianini, Xavier Carreras, Tijl de Bie

The aim of this workshop is to disseminate scientific results produced by the SMART project to the larger technical and scientific community working on Machine Translation. To facilitate this inter-exchange, it will be co-located with the EAMT 2009 – 13th Annual Conference of the European Association for Machine Translation that will be held May 14-15, 2009 Universitat Politècnica de Catalunya, Barcelona, Spain.

Conference web site: http://www.talp.cat/eamt09

Workshops page: http://www.talp.cat/eamt09/index.php/associated-workshops

Programme

Morning

9.30 – 10.00 Welcome, Nicola Cancedda, Xerox Research Centre Europe

10.00 – 11.00 Invited Talk: “Empirical Machine Translation and its Evaluation” – Jesus Gimenez, UPC

11.00 – 11.30 Coffee

11.30 – 12.00 – “Online learning for CAT applications” – Nicolo` Cesa-Bianchi, University of Milan

12.00 – 12.30 – “Sinuhe — Statistical Machine Translation with a Globally Trained Conditional Exponential Family Translation Model” – Matti T Kaariainen, University of Helsinki

12.30 – 1300 – “Large scale, maximum margin regression based, structural learning approach to phrase translations” – Sandor Szedmak, University of Southampton

LUNCH

Afternoon

14.00 – 14.30 “Learning to Translate: statistical and computational analysis” – Marco Turchi, University of Bristol

14.30 – 15.00 -“Detecting and exploiting Translation Direction” – Cyril Goutte, National Research Council – Canada

15.00 – 15.30 – “Multi-view CCA and regression CCA” – Blaz Fortuna, Jo¾ef Stefan Institute

Coffee

16.00 – 16.30 – “Large-Margin Structured Prediction via Linear Programming” – Zhuoran Wang, University College London

16.30 – 17.00 – “Confidence Estimation for Machine Translation” – Lucia Specia, Xerox Research Centre Europe

17.00 Closing Remarks

ABOUT THIS WORKSHOP

A joint event of SMART project – PASCAL Network

SMART (Statistical Multilingual Analysis for Retrieval and Translation, www.smart-project.eu) is a 3-year “Specific Target Research Project” (STReP) funded by the European Commission. SMART is
an attempt to address different problems of Machine Translation and Cross-Language Information Retrieval by the methods of modern Statistical Learning.

In the first two years of the project, the scientific focus has been on developing new and more effective statistical approaches while ensuring that existing know-how is duly taken into account. This was done by bringing together leading research institutions in Statistical Learning, Machine Translation and Textual Information Access.

PASCAL 2 (Pattern Analysis, Statistical Modelling and Computational Learning 2) is a 5-year “Network of Excellence” (NoE) funded by the European Commission, focusing on Machine Learning, Statistics and Optimization.

The aim of this workshop is to disseminate scientific results and share experiences produced by the SMART project to the larger technical and scientific community. The SMART consortium considers
this workshop to be a great opportunity for science investigations, creating both scientific and commercial opportunities as well as technological challenges to researchers.

Funded PhD post in data mining / machine learning – University of Bristol

Applications are invited for a fully funded PhD studentship (fees and stipend) in the Pattern Analysis and Intelligent Systems research group at the University of Bristol, UK.

Topic of the studentship:
“Statistical techniques for informative pattern mining in complex and structured data”
However, applicants interested in
“Machine Learning and Data Mining for Music Information Retrieval”
are also welcome to apply.

You will join a vibrant team working on the crossroads of data mining, machine learning, complexity science, and on applications in areas including bioinformatics, music information retrieval, web mining, news media analysis, and social network analysis.

The duration of the studentship is 3.5 years, and the starting date is 1 October 2009 or shortly after that (to be agreed).

The ideal candidate has a first class computer science / electrical engineering / mathematics / physics degree, with a strong background in mathematics as well as programming experience. (S)he is a loyal team player, and combines an interest in data mining and machine learning theory with a commitment to applying theoretical results in a real context, with a strong desire to make an impact.

Expressions of interest with a short CV, or any informal queries, should be sent to:
tijl.debie (at) bristol.ac.uk

Some links:
The Pattern Analysis and Intelligent Systems research group: http://patterns.enm.bris.ac.uk/,
part of the Intelligent Systems Lab: http://intelligentsystems.bristol.ac.uk/,
part of the University of Bristol: http://www.bristol.ac.uk/
part of the very enjoyable city of Bristol: http://en.wikipedia.org/wiki/Bristol

3 PhD positions in Natural Language Processing and Visualization

3 PhD positions in Natural Language Processing and Visualization

Institute for Natural Language Processing, University of Stuttgart
and
Computer Science Department, University of Stuttgart

The Institute for Natural Language Processing (IfNLP) and
the Computer Science Department of the University of
Stuttgart, Germany, invite applications for three PhD
positions.

IfNLP is one of the leading NLP research institutions
worldwide with four professors in different areas of NLP, a
research staff of 40 and an undergraduate program in NLP.
We offer the opportunity to work on cutting-edge research
projects in a dynamic and international research team and
up-to-date infrastructure and resources.

3 PhD positions are available immediately in two different
projects funded by Deutsche Forschungsgemeinschaft.

SEMISUPERVISED COREFERENCE RESOLUTION
2 PhD positions
Supervisors: Profs. Gunther Heidemann, Hans Kamp, and Hinrich Schuetze

This project will develop interactive visualization methods
for the semi-supervised annotation of large amounts of
training data for statistical coreference resolution.

INTERACTIVE VISUAL ANALYSIS OF COMPLEX INFORMATION SPACES
1 PhD position
Supervisor: Prof. Hinrich Schuetze

This project will integrate statistical NLP and
user-tailored interactive visual exploration methods and
apply them to the analysis of patents.

Candidates should have an excellent university degree in a
relevant field of study such as computational linguistics or
computer science.

To apply, send your CV in PDF format to sabine (at)
ims.uni-stuttgart.de by May 15, 2009. Please use the subject
line “PhD positions”. You should also provide two
references.

The University of Stuttgart is committed to increasing the
proportion of women in research and teaching. Qualified
women are encouraged to apply.

Call for Participation: ILP, MLG, SRL 2009

* SRL-2009 – International Workshop on Statistical Relational Learning
* ILP-2009 – 19th International Conference on Inductive Logic Programming
* MLG-2009 – 7th International Workshop on Mining and Learning with Graphs

**** Early registration closes May 15th ****

JOINT CALL FOR PAPERS

In 2009, three international conferences / workshops on learning from relational, graph-based and probabilistic knowledge will be co-located:
ILP-2009, the 19th International Conference on Inductive Logic Programming;
MLG-2009, the 7th International Workshop on Mining and Learning with Graphs;
SRL-2009, the International Workshop on Statistical Relational Learning.

These events are held as a Pascal 2 Network of Excellence event in Leuven, Belgium, on July 2-4, 2009. Time and location are highly compatible with the KDD-2009 conference in Paris, June 28 – July 1, at
only 2 hours train travel from Leuven.

AIM AND SCOPE

The ILP conference series has been the premier forum for work on logic-based approaches to learning for almost two decades. It has recently reached out to other forms of relational learning and to probabilistic approaches.

The MLG workshop series focuses on graph-based approaches to machine learning and data mining; since its conception in 2003, attendance numbers have consistently increased, and it now enjoys worldwide
recognition.

The SRL workshop series focuses on statistical inference and learning with relational and first-order logical representations. The combination of probability theory with relational (or first-order logic) knowledge
representations has been the subject of much recent research.

While the three series clearly have their own identity, there is a significant overlap in the topics covered by each of them. The aim of this co-location is to increase interaction between the three communities. The format of the joint event will stimulate such interaction by providing joint invited speakers and tutorials, joint sessions and poster sessions, and ample time and space for discussions in smaller groups, in addition to the regular programs of the three events.

Detailed calls for papers for the respective workshops, including submission instructions, are or will become available at http://www.cs.kuleuven.be/~dtai/sim/

Submissions to the events will be in the form of extended abstracts which can be accepted for either an oral or a poster presentation. The abstracts will be made available in an informal way (though more formal
post-conference publications are being considered as well, such as a post-conference proceedings for ILP and possibly special issues of journals for all events, more precise information on this will become
available at the website). As it is the goal that the very best work in the area be presented at the events, authors are explicitly encouraged to submit extended abstracts of high quality work in the area that has
recently been accepted or published at key venues.

IMPORTANT DATES

* April 3 Deadline for paper / abstract submissions
* April 30 Notification
* May 29 Deadline for camera ready copies
* July 2-4 Conference

ORGANIZATION

* ILP Program Chair: Luc De Raedt
* SRL Program Chairs: Pedro Domingos, Kristian Kersting
* MLG Program Chairs: Hendrik Blockeel, Karsten Borgwardt, Xifeng Yan
* General chair: Luc De Raedt

NIPS 2009 Call for Papers and Conference Poster Contest

NIPS 2009 PRELIMINARY CALL FOR PAPERS

Submissions are solicited for the Twenty-Third Annual Conference on Neural Information Processing Systems, an interdisciplinary conference that brings together researchers in all aspects of neural and statistical information processing and computation. The conference is a highly selective, single track meeting that includes invited talks as well as oral and poster presentations of refereed papers.
Submissions by authors who are new to NIPS are encouraged. Preceding the main conference will be one day of tutorials (December 7), and following will be two days of workshops at the Whistler/Blackcomb ski resort (December 11-12).

Deadline for Paper Submissions: Friday June 5, 2009,
23:59 Universal Standard Time (4:59pm Pacific Daylight Time).

A full description of the Call for Papers can be found here: http://nips.cc/Conferences/2009/CallForPapers

NIPS 2009 CONFERENCE POSTER CONTEST

This year, we’d like to capture the creativity of the NIPS community in 2009’s NIPS conference poster. The winner will receive full complimentary registration for the tutorials, conference, workshop, and bus transportation to Whistler. Please design a poster and upload it in PDF, PNG, or JPG format. This is open to any member of NIPS: students, postdocs, and faculty. The NIPS board will review and vote for the best one.

Upload your poster by July 15, 2009. Good luck and thank you for your valuable contributions. If you have any questions, please contact us at info@nips.cc.

Previous NIPS posters can be viewed here: http://nips.cc/Conferences/

Submission page: https://nips.cc/Conferences/2009/PosterContest/Upload/

Open PhD position in Machine Learning and Vision (Switzerland)

The Idiap Research Institute[1], affiliated with École Polytechnique Fédérale de Lausanne[2], seeks one PhD student in statistical learning to develop original techniques for vision with complex priors.

This position is funded by a grant from the Swiss National Science Foundation, and the candidate will be a doctoral student at EPFL EDEE doctoral school[3]. Research will be done under the supervision of Dr. François Fleuret[4].

Summary:

Object detection and recognition techniques based on machine learning have historically relied on crude prior representation of the image, far from the complexity and richness of biological systems.

This project will investigate an alternative approach using very rich feature extractors addressing multiple modalities of the signal. The objective is to create new tools to help the design of such feature extractors, and to investigate learning techniques able to cope with very large and heterogeneous families of features.

The objective is to design novel approaches to full-scene interpretation, aiming at detecting many objects visible in an image.

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 familiar with several of the following topics: probabilities, applied statistics, information theory, signal processing, optimization, algorithmic, and C++ programming.

About Idiap:

The Idiap Research Institute is located in Valais[5], a scenic region in the south of Switzerland, surrounded by the highest mountains of Europe, and within close proximity to Lausanne and Geneva. The working language of Idiap is English.

Please contact francois.fleuret (at) idiap.ch for additional information.

[1] http://www.idiap.ch
[2] http://www.epfl.ch
[3] http://phd.epfl.ch/page76428.html
[4] http://www.idiap.ch/~fleuret
[5] http://maps.google.com/maps?ll=46.11323,7.003784&z=9

First CFP StReBio’09: ACM-SIGKDD workshop on Statistical and Relational Learning and mining in Bioinformatics

StReBio’09 – ACM SIGKDD workshop on Statistical and
Relational Learning and mining in Bioinformatics
http://www.cs.kuleuven.be/~dtai/events/StReBio09/
Call for contributions

OBJECTIVES
Bioinformatics is an application domain where information is naturally represented in terms of relations between heterogenous objects. Modern experimentation and data acquisition techniques allow the study of complex interactions in biological systems. This raises interesting challenges for
machine learning and data mining researchers, as the amount of data is huge, some information can not be observed, and measurements may be noisy.

The StReBio workshop aims at bringing together researchers from both the field of statistical relational learning and the field of bioinformatics. Our main goals are to provide a common venue for the two communities where biologists can present novel complex problems arising in biological applications that computer scientists could tackle by developing new statistical relational approaches.

CONTRIBUTION TYPES
We invite contributions of the following types:

* Regular papers, describing work in the area of the workshop;
* Open problem papers, describing challenges and open problems;
* Challenge solution papers, describing solutions of open problems presented at StReBio’08. A list of these problems can be found on the workshop webpage.

TOPICS OF INTEREST

The purpose of the workshop is to provide a forum for presenting and discussing new methods, problem settings, applications and models, exploiting structured data in the field of biology. Methods include, but are not restricted to

* Statistical Relational Learning
* Relational Probabilistic Models
* Multi-relational Data Mining
* Graph Methods

The data, structures or models considered can include but are not limited to

* Sequences (DNA, RNA, protein)
* Pathways (chemical, metabolic, mutation, interaction pathways)
* 2D, 3D structures of proteins, RNA
* Chemical structures (e.g. QSAR, especially regarding interaction of compounds with proteins)
* Evolutionary relations (phylogeny, homology relations)
* Ontologies integration (gene, enzyme, protein function ontologies)
* Large networks (regulatory, co-expression, interaction, metabolic,…)
* Concept graphs (heterogenuous graphs linking information on articles, authors and biological entities such as compounds, proteins, genes, …

PROCEEDINGS

ACM-SIGKDD will provide informal workshop proceedings. (Extended versions of) selected papers will be published in a special issue of Fundamenta Informaticae. Details will be published on the workshop webpage in the beginning of April.

IMPORTANT DATES

* Submission: Apr 20
* Notification: May 15
* Camera-ready copy: May 22
* Workshop: June 28

Postdoctoral Fellowships in Robotics and Adaptive Control @ Edinburgh, UK

UNIVERSITY OF EDINBURGH SCHOOL OF INFORMATICS
TWO Postdoctoral Research Fellows in Robotics and Adaptive Control

Applications are invited for two Postdoctoral Research Fellows in the area of Learning Robotics and Adaptive Control as part of an EU-IST FP7 funded project. The posts are available from Mar. 2009 for a maximum of 34 months and located in the School of Informatics at the University of Edinburgh. Salary is on the UE07 scale (£29,704-£35,469) with annual increments and full staff benefits. Placement for the post is according to experience and qualifications.

Post One: Robotics and Adaptive Control: The candidate is expected to have good fundamentals in control theory and most importantly, hands on experience with writing software and controlling robotic hardware. The appointee will be responsible for direct implementation of adaptive control paradigms on biophysical simulations and on state-of-the-art novel variable impedance actuators.

Post Two: Statistical Learning and Adaptive Control Theory: The candidate is expected to have a strong background in optimization, statistical learning and adaptive control theory and some familiarity of concepts such as direct policy learning, stochastic dynamic programming and optimal feedback control. An interest and knowledge of variable impedance strategies in human motor control is a definite advantage. The appointee will be responsible for development of effective adaptive control strategies and algorithms that exploit variable stiffness paradigms.

Both posts will involve traveling to project partner meetings around Europe, periodic reporting at EU reviews as well as attending and disseminating work at international conference. The post also assumes leadership roles and some level of PhD supervision on topics relevant to the project.

The successful candidates will have a PhD (or expected completion) in the area of (learning) robotics, probabilistic machine learning and/or adaptive motor control; strong mathematical skills in the area of optimization, algebra and control theory; strong programming skills in C, C++, MATLAB or equivalent; some experience with writing software and control of real
hardware systems; a good understanding of adaptive control paradigms

More details of the job and the research group can be found at: http://www.ipab.inf.ed.ac.uk/slmc

Applicants are asked to submit your curriculum vitae including a statement of interest justifying your suitability for the post you are applying for and contact details of two referees using the online application procedure at:

Post1:
http://www.jobs.ed.ac.uk/vacancies/index.cfm?fuseaction=vacancies.detail&vacancy_ref=3010336 Post2:
https://www.jobs.ed.ac.uk/vacancies/index.cfm?fuseaction=vacancies.detail&vacancy_ref=3010337

Application Deadline: February 20, 2009

Informal enquiries may be addressed to:
Dr. Sethu Vijayakumar (sethu.vijayakumar [at] ed.ac.uk)

Faculty job in Machine Learning at the University of Edinburgh

The School of Informatics of the University of Edinburgh invites applications for a Lectureship in Machine Learning from outstanding candidates in any area of machine learning (including models, algorithms and applications).

We particularly welcome applications from candidates who are developing principled machine learning/statistical/data mining approaches to working with real-world, complex data. Example areas include (but are not limited to): machine learning methods in computer vision/multimodal sensing; models for multi-stream data; inference and prediction in networks of interacting elements (e.g. in systems biology); reinforcement learning approaches to acting under uncertainty.

Recent investment by the Scottish Funding Council has enabled leading researchers across Scotland, including Informatics at Edinburgh, and groups and individuals at the Computer Science Departments of nine other institutions, to establish the Scottish Informatics and Computer Science Alliance (SICSA) http://www.sicsa.ac.uk. Where relevant, candidates should relate their applications to the SICSA research themes for securing, interfacing, modelling and engineering the systems of tomorrow.

[A Lectureship is roughly equivalent to a US Assistant Professor]

Informal enquiries may be addressed to Prof Chris Williams c.k.i.williams (at) ed.ac.uk .

Vacancy Ref. No: 3010479
Closing Date: 20 March 2009

For further information and to apply see https://www.jobs.ed.ac.uk/vacancies/index.cfm?fuseaction=vacancies.furtherdetails&vacancy_ref=3010479

RA position in Sheffield: probabilistic modelling in Systems Biology

A Post Doctoral Research Associate position is available in the Department of Chemical and Process Engineering for a computational scientist working on probabilistic modelling for Systems Biology. The successful candidate will join the Institute of Chemical Engineering at the Life Sciences Interface (ChELSI) in the Department of Chemical and Process Engineering. Established in 2006 following a £4.4M award from
the EPSRC, the institute is at the forefront of the development and application of systems approaches to bio-engineering. Following the appointment of two lecturers in theoretical systems biology, the institute is now seeking to further expand its modelling base. The PDRA will work with Dr Guido Sanguinetti on probabilistic modelling of high-throughput experimental data, using and developing techniques from statistical machine learning to address new problems in systems biology.
The successful applicant will have, or be in the process of obtaining, a PhD (or have equivalent experience) in a quantitative discipline (maths, physics, engineering, computer science), and will be working in a highly interdisciplinary environment. Some background in statistical data modelling and computational biology would be desirable, but motivated applicants from other numerate backgrounds are also welcome.

To apply and for further details see
http://www.jobs.ac.uk/jobs/BO379/Post_Doctoral_Research_Associate/.
Closing date is April 9th.