Summer Schools in Logic and Learning

An Open Invitation to attend the

Summer Schools in Logic and Learning

26 January to 6 February 2009

Australian National University, Canberra, Australia

One of the grand challenges in science and engineering is to build computer systems that are trustworthy and intelligent. While achieving this goal could be many decades away, computer systems are clearly getting smarter and more reliable year by year and human society is becoming more reliant on exploiting their increasing intelligence. Logic and machine learning are two indispensable parts of the efforts to meet this challenge.

Join us for a new summer school experience where you have a unique two week opportunity to combine the solid foundations of logic and machine learning, with an introductory track in artificial intelligence in the second week.

Courses are taught by some of the world’s leading computer scientists and blend practical and theoretical short courses with lectures and demonstrations in state-of-the-art computer facilities at ANU.

Courses and Speakers

Artificial Intelligence Courses
http://ssll.cecs.anu.edu.au/speakers/ai

Logic Courses
http://ssll.cecs.anu.edu.au/speakers/lss

Machine Learning Courses
http://ssll.cecs.anu.edu.au/speakers/mlss

Fees and Registration
http://ssll.cecs.anu.edu.au/registration

More information
http://ssll.cecs.anu.edu.au/

If you would like to discuss this invitation in more detail, including advice on suitable candidacy, please go to: http://ssll.cecs.anu.edu.au/about/contact

The Summer Schools in Logic and Learning are supported by ANU and NICTA.

Committee
Dr Tiberio Caetano, Convener
Professor John Slaney, Convener
Dr Alwen Tiu (Acting Convener)
Diane Kossatz
Michelle Moravec

Two Post-Doc Positions Available at Univ. Paris-Sud

We are seeking post-doc candidates with strong math or physics and programming skills:

Post-doc in Swarm Robotics; distributed learning, optimization and log mining for autonomous control of swarm robots
– within the European SYMBRION IP; coll. U. Stuttgart, Germany
– within the CNRS-JST cooperation in Robotics; coll. U. Kyushu, Japan
Background: Machine Learning, Robotics, Evolutionary Optimization

Post-doc in Brain Computer Interface: statistical learning for acquisition of new skills and detection of upcoming crises.
– Digibrain project; coll. CEA, France
Background: Machine Learning, Signal Processing.

Post-doc appointment is for 1 or 2 years, fully funded.
French salaries include medical coverage.

Selected students will be working in the TAO group (INRIA – CNRS – Department Computer Science, Universite Paris-Sud), at the crossroad of Statistical Machine Learning and Stochastic Optimization, supervised by senior researchers Michele Sebag and Marc Schoenauer.

Location: Universite Paris-Sud, France; 30′ from Paris down-town.
Working language: English.
Starting date: January 2009.

More: Job Offers, http://tao.lri.fr

SVM Technology Wins NSF-Sponsored Challenge at the World Congress for Computational Intelligence 2008

Health Discovery Corporation (OTCBB: HDVY), a leader in support vector machine (SVM) based molecular diagnostics development today announced that Yin Wen Chang, a student of Chih-Jen Lin from the National Taiwan University, distinguished herself in the first causality challenge organized for the World Congress on Computational Intelligence, WCCI 2008, which was held in Hong Kong, June 1-6, 2008.

The challenge, which is sponsored by the U.S. National Science Foundation, the European Network of Excellence PASCAL, and Microsoft Corporation, attracted over 50 participants. Yin Wen Chang ranked first on two tasks of the challenge and second and third on the two others, using SVM both for feature selection and for classification.

The four tasks proposed to the competitors were derived from real data in genomics, pharmacology and econometrics. The goal of the challenge was to uncover causes of a given outcome in order to make predictions of the result of future actions. For example, find genes to cure disease, find risk factors to control epidemics. Uncovering causes superficially resembles the problem of feature selection. But most feature selection algorithms emanating from machine learning like RFE-SVM do not seek to model mechanisms: they do not attempt to uncover cause-effect relationships between feature and target.

“We did not expect non-causal feature selection methods to do so well on these tasks,” explained Dr. Isabelle Guyon, co-organizer of the challenge and a member of HDC’s Science Team. “Causal discovery methods did very well at unraveling causal structure, and on average, we observed good correlation between the fraction of causally relevant features selected and the predictive power of learning machines on the tasks of the challenge. Yet, non-causal feature selection methods like RFE-SVM find feature subsets containing complementary features with high predictive power and SVMs are insensitive to the presence of false positive, so this is a combination that’s very hard to beat.”

“Solving problems of causality in order to predict the results of future actions is a critical component of identifying the right drug at the right dose for the right patient and is the cornerstone for the successful implementation of personalized medicine,” stated Stephen D. Barnhill, M.D., Chairman and CEO of Health Discovery Corporation. “We are thrilled that once again SVM technology has proven to be superior to other mathematical algorithms in solving these very difficult and unique problems. We congratulate Yin Wen Chang for her great accomplishment using SVMs to win the NSF-Sponsored Challenge at the World Congress for Computational Intelligence 2008.”

Dr. Barnhill continued, “With 32 issued patents around SVM technology and the only issued patents in the world on RFE-SVM, Health Discovery Corporation is in a unique position to capitalize on the proven success of these techniques to create and commercialize new diagnostic tests and play a significant role in bringing the promises of personalized medicine to reality.”

Further Infomation available at http://www.earthtimes.org/articles/show/svm-technology-wins-nsf-sponsored-challenge,430724.shtml

PhD Student / Postdoctoral Researcher in Machine Learning, Image Processing

Probabilistic Machine Learning and Medical Image Processing Saarland University, Saarbruecken, Germany

Fully-funded PhD/postdoc positions are available in the recently established Probabilistic Machine Learning group headed by Matthias Seeger (PhD). PhD training is conditional on acceptance to the International Max Planck Research School for Computer Science (based on evaluation of research proposal and oral presentation, after first six months).

Recent breakthroughs in large-scale approximate Bayesian inference for sparse continuous variable models allow nonlinear Bayesian experimental design (active learning) and compressed sensing to be applied to sampling optimization of magnetic resonance imaging. More detail about these projects can be found at the following link

http://www.kyb.tuebingen.mpg.de/bs/people/seeger/projects/ed_mri/main.html

Saarland University is among the leading computer science faculties in Europe, with world-class groups in computer graphics, theory of algorithms and programming languages, theoretical CS, and bioinformatics, among others. It features a unique accumulation of top-ranked CS research institutes (Max Planck Institute for Informatik, Max Planck Institute for Software Systems, DFKI). Within the recently established interdisciplinary MMCI Cluster of Excellence, 20 independent research groups are working in areas with strong overlaps to core machine learning application areas. Saarbruecken is dedicated to excellent postgraduate education, structured according to international standards in the International Max Planck Research School for Computer Science (courses taught in english).

The Probabilistic Machine Learning group focusses on theory and applications of approximate Bayesian inference, and its scalable reduction to standard methods of scientific computing (numerical mathematics, efficient algorithms, signal processing, parallel computing). We closely collaborate with the Center for High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen (with a range of MR scanners dedicated to basic research), and have close ties to the Empirical Inference group (headed by Bernhard Schoelkopf) at the same institute, beyond connections to top machine learning groups in the UK and US.

We are looking for highly motivated, research-oriented individuals with an excellent grasp of the mathematics underlying approximate Bayesian inference, or/and numerical optimization and mathematics, or/and image and signal processing. A strong theoretical background in a field relevant to analysis of statistical methods, or/and keen interest and capabilities in large-scale scientific programming are required.

Please be sure to include the following in your application:
– Curriculum vitae
– Statement of research interests (1 page)
– Letters of reference (1-3) from referees able to comment on your work and academic standing (PhD/MSc thesis advisor, supervisor for internships)
– Sample of your strongest work (first-author paper in peer-reviewed journal/conference, MSc or PhD thesis, term project paper (with official record attesting your authorship)) in the rough area of interest
– Transcript of studies (for PhD applicants)

Applications should be sent by e-mail to Matthias Seeger, mseeger@mmci.uni-saarland.de. If you happen to attend the forthcoming Neural Information Processing Systems conference (Vancouver, December 8-13, 2008; http://nips.cc/Conferences/2008/), please make yourself known to Matthias there.

Relevant Links

Project page
http://www.kyb.tuebingen.mpg.de/bs/people/seeger/projects/ed_mri/main.html

MMCI Cluster of Excellence
http://m2ci.dl01.de/index.php?id=1&L=0

International Max Planck Research School for Computer Science
http://www.imprs-cs.de/

Max Planck Institute for Informatik
http://www.mpi-inf.mpg.de/

Saarland University
http://www.uni-saarland.de/en/

Ph.D. position in Brain Computer Interaction, University of Glasgow

TOBI is a large European project which will develop practical technology for brain-computer interaction; i.e., non-invasive BCI prototypes combined with other assistive technologies that will have a real impact on improving the quality of life of disabled people. These non-invasive BCI are based on electroencephalogram (EEG) signals. The expected impact of TOBI is a wide-spread use of BCI assistive technology endowed with adaptive capabilities that augment those other assistive technologies they are combined with. TOBI will deliver short-term BCI assistive prototypes that will be tested and evaluated in real life situations by a large number of end-users.

The aim of this studentship is to use and develop interaction techniques which can work with noisy, uncertain input mechanisms such as machine learned classifiers. The project is at the intersection of a machine learning and HCI and will involve developing novel multimodal feedback techniques to enhance interaction with extremely restricted input channels. In particular, this will involve the display of uncertain, inferred states in such a way as to help users learn to use this novel form of interaction more reliably. This involves solving fundamental problems at the core of human-computer interaction, which are also relevant for a wide range of other interaction design issues such as sensor-based interaction and context-sensitive interaction.

The studentship is for 40 months and is only available to UK/EU nationals. The studentship is suitable for candidates with a good first degree (or Masters) in Computing Science, Electronics & Electrical Engineering or Mathematical subjects. Candidates should have strong software engineering and maths skills and ideally experience in signal processing and machine learning. The project will involve frequent travel to European project partners. The student will be jointly supervised by Prof. Roderick Murray-Smith and Dr. John Williamson.

The application procedure can be found at http://www.dcs.gla.ac.uk/phd/application.html.
Informal enquiries to Professor Roderick Murray-Smith: rod@dcs.gla.ac.uk,
http://www.dcs.gla.ac.uk/~rod/
Deadline for submissions is 5th January 2009

Gatsby Computational Neuroscience Unit, UCL: Faculty Position in Machine Learning/Statistics

The Gatsby Computational Neuroscience Unit at UCL is looking to recruit a junior or senior level faculty in machine learning or statistics. We are especially interested in candidates whose work in these fields complements the Unit’s focus on probabilistic and statistical machine learning, or its wider interests in the brain.

Along with the statistical machine learning focus at Gatsby, led by Yee Whye Teh, UCL offers a rich environment across the breadth of the field. Activities in these areas are anchored by the new Centre for Computational Statistics and Machine Learning which is directed by John Shawe-Taylor, involving the departments of Computer Science (Mark Herbster; Massimiliano Pontil; David Barber), Statistics (Trevor Sweeting; Ricardo Silva) and Gatsby itself.

The Gatsby Unit was set up at UCL in 1998 as a research institute devoted to theoretical neuroscience and machine learning. We have core funding for five faculty and for associated postdocs and PhD students. PIs can raise additional funds through grants. We have no undergraduate programme, so only teaching and supervision of graduate-level Gatsby students is required. We have close ties with the UCL Departments of Computer Science and Statistics, with research departments within UCL’s School of Life and Medical Sciences, and with groups in Engineering and Physics (Zoubin Ghahramani, David MacKay) at Cambridge and beyond. We are located in a leafy haven in Queen Square, London.

The Unit offers internationally competitive salaries. The salary ranges are: Lecturer (grade 7) £32,458 – £35,469 per annum, (grade 8) £36,533 – £43,622 per annum. Senior Lecturer/Reader (Grade 9) £47,667- £52,086 per annum. Professor posts will be appointed on grade 10 with a minimum starting salary of £55,259 per annum – salary is negotiable on the professorial scale. London Allowance of £2,781 per annum is payable in addition to these salaries. A market supplement is available for exceptional candidates in line with international ranges.

Applications, consisting of a CV, a statement of research interests and accomplishments, a teaching statement and full contact details for three academic referees should be sent by e-mail to Rachel Howes: asstadmin ‘at’ gatsby.ucl.ac.uk. Applicants are asked to provide standardised monitoring information by completing and returning the forms available at:
www.gatsby.ucl.ac.uk/vacancies/informationbycvapplicants.pdf

Applications must arrive no later than 5 January 2009.

For further information, please see www.gatsby.ucl.ac.uk/vacancies/FacultyJD.pdf; for informal enquiries, please contact Yee Whye Teh at ywteh ‘at’ gatsby.ucl.ac.uk

Gatsby Computational Neuroscience Unit, UCL 4 year PhD Programme

The Gatsby Unit is a centre for theoretical neuroscience and machine learning, focusing on unsupervised, semi-supervised and reinforcement learning, neural dynamics, population coding, Bayesian and nonparametric statistics and applications of these to the analysis of perceptual processing, neural data, natural language processing, machine vision and bioinformatics. It provides a unique opportunity for a critical mass of theoreticians to interact closely with each other, and with other world-class research groups in related departments at UCL (University College London), including Anatomy, Computer Science, Functional Imaging, Physics, Physiology, Psychology, Neurology, Ophthalmology and Statistics, with the cross-faculty Centre for Computational Statistics and Machine Learning, and also with other UK and overseas universities notably, at the present time, with Cambridge in the UK and Columbia, New York.

The Unit always has openings for exceptional PhD candidates. Applicants should have a strong analytical background, a keen interest in machine learning and/or neuroscience and a relevant first degree, for example in Computer Science, Engineering, Mathematics, Neuroscience, Physics, Psychology or Statistics.

The PhD programme lasts four years, including a first year of intensive instruction in techniques and research in theoretical neuroscience and machine learning.

Competitive fully-funded studentships are available each year (to students of any nationality) and the Unit also welcomes students with pre-secured funding or with other scholarship/studentship applications in progress.

Full details of our programme, and how to apply, are available at: http://www.gatsby.ucl.ac.uk/teaching/phd/

For further details of research interests please see: http://www.gatsby.ucl.ac.uk/research.html

Applications for 2009 entry (commencing late September 2009) should be received no later than 11 January 2009. Shortlisted applicants will be invited to attend interview in the week commencing 9 March 2009.

Research Openings at Telefonica Research in Madrid, Spain

Research scientists, visiting professors, post-docs, interns
Areas: Machine Learning, Data Mining, User Modeling, Personalization, Business Intelligence.

Telefonica Research in Madrid has several research openings at all levels in the new Data Mining and User Modeling research group, which focuses on human-centered approaches to data analysis for customer modeling, personalization, and decision support. A special emphasis of the group is on principled data analysis taking transdisciplinary approaches that consider sociocultural context and personal preferences.

Selected candidates are expected, and will have the opportunity to develop and lead their own area of research, with significant support from our engineering teams. Individuals must therefore be able to carry out leading independent research while working closely within an interdisciplinary team.

Requirements: Ph.D. degree in Computer Science or a related field, a strong publication
record, and experience in data mining, machine learning, or user modeling (see web). Interdisciplinary background and interests and/or experience in social aspects of computing considered favorably (technology for developing regions, culture-aware computing, etc.). Successful candidates will be highly motivated, creative, dynamic, fluent in English, have excellent communication skills (written and oral), and be able to interact well in international, multidisciplinary, R&D teams. Knowledge of Spanish is not necessary.

Telefonica Research offers an internationally competitive salary and benefits package (flexible working schedule, Spanish classes, lunch subsidy, full medical coverage, etc.) in an international, dynamic work environment in Spain’s largest and most international city. As one of the most important European capitals, Madrid offers a vast array of cultural activities, convenient international air connections and some of the best restaurants and nightlife in the continent. Telefonica is a world leader in the telecommunication sector, with presence in over 23 countries and over 218 million customer accesses (2007), offering Services such as mobile & fixed line phone, ISP, IPTV, web portals, and others.

Inquiries and applications should be sent to Dr. Alejandro Jaimes (email: ajaimes AT tid.es) with the subject line “TID Research Application-ML”. There is no deadline: positions will be open until filled.

http://research.tid.es/usermodeling

Back to Vacancies

Launch of the GREAT08 PASCAL Challenge

We are delighted to announce the launch of the GRavitational lEnsing Accuracy Testing 2008 (GREAT08) PASCAL Challenge.

The GREAT08 Challenge is an image analysis competition for gravitational lensing and cosmology, aimed at experts in statistical problems (non-astronomers). The competition runs for 6 months, until 30 April 2009.

Please find more information at the challenge website http://www.great08challenge.info

There are 200GB of simulated galaxy images to download http://docs.google.com/View?docid=dcrd4nqb_138dr64tf4r , a live leaderboard containing results from GREAT08 Team members https://great08.projects.phys.ucl.ac.uk/leaderboard and you can download the code we used to get these results http://great08challenge.pbwiki.com

You are invited to join us for an (IP) videoconference to introduce the challenge and answer your questions at 4pm GMT on Tuesday (4th Nov). Please reply to this message or email questions@great08challenge.info for the connection details.

Sarah Bridle and John Shawe-Taylor, on behalf of the GREAT08 Team

Harvest Programme: pilots seeking for participants

We are happy to announce the new PASCAL 2 Harvest Programme!

The Harvest Programme supports applied research projects between PASCAL groups and the industry, or with academic researchers in other disciplines. After a preparation period, a small team converges to work side by side for 45-120 days in a very focused way: think “start-up mode”, but working on a research subject. Sounds interesting? Read the complete description at:

http://pascallin2.ecs.soton.ac.uk/Programmes/HA/

We need to test the concept while we broaden the “Industrial Club” of companies entitled to participate. The Harvest programme is thus accepting applications for participating in one of two “pilot projects”, one with M-brain (Helsinki, Finland) and one with Xerox (Grenoble, France). Read all about these pilots (and post your comments!) on the Harvest wiki page:

http://pascallin2.ecs.soton.ac.uk/Wiki/HarvestWikiHome

You are in research because you like to be the first to do things: join one of the two pilots!