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Call for Papers: Machine Learning and Its Application Stream in the 23rd European Conference on Operational Research (EURO)

Machine Learning and Its Application” Stream in the 23rd European Conference on Operational Research (EURO), Bonn, Germany
July 5-8, 2009
http://www.euro-2009.de/home.htm

IMPORTANT DATES
Submission for abstracts starts: October 2008
Deadline for abstract submission: March 1, 2009
Notification of acceptance: March 31, 2009
Deadline for early registration: April 1, 2009
Deadline for author registration (for inclusion in the programme): April 15,2009
Conference: July 5-8, 2009

AIMS AND SCOPE
A subfield of Artificial Intelligence (AI), machine learning, is concerned with the development of algorithms that allow computers to “learn”. It is the process of training a system with a large number of examples, extracting rules and finding patterns in order to make predictions on new data points (examples). Common machine learning problems include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, etc.. There are different kinds of applications in this field, including natural language processing, search engines, medical diagnosis, bioinformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, and object recognition in computer vision to name a few.

TOPICS OF INTEREST Topics of interest include, but are not limited to:

* Mathematical foundations of Learning Theory
* Data mining and machine learning algorithms and methods in OR areas
* Machine learning applications in Fraud Detection, Healthcare systems
* Optimization methods in machine learning
* Supervised and unsupervised learning methods and applications
* Clustering methods and its application to OR
* Kernel learning and its applications to OR, business, healthcare.

ABSTRACT SUBMISSION: Abstracts must be written in English and contain no more than 600 characters (no formulas or mathematical notations are allowed). Each attendee is allowed to present ONE paper at the conference. Abstract submissions can be done
(http://www.euro-2009.de/abstract_submission.htm) according to the guidelines posted there. At least one author of each accepted abstract is expected to participate in the conference and present his/her work.

PAPER SUBMISSION: We invite all researchers, academicians, practitioners, as well as students interested in all branches of operational research, mathematical modelling and economic analyses to participate in the conference and to present their papers in the following areas:

1. Continuous optimization and control
2. Data mining; knowledge discovery; artificial intelligence
3. DEA and performance management
4. Decision analysis; decision support systems; modelling languages
5. Discrete optimization; graphs & networks
6. Energy, environment & climate
7. Financial modelling; risk management; banking
8. Fuzzy sets; softcomputing
9. Game theory; mathematical & experimental economics
10. Health, life sciences & bioinformatics
11. Location; logistics; transportation; traffic
12. Metaheuristics & biologically inspired approaches
13. Multiple criteria decision making, optimization & group decision
14. OR education, history & ethics
15. OR for developing countries
16. OR in agriculture & natural resources
17. OR in industries & software applications
18. Production management; supply chain management
19. Revenue management & managerial accounting
20. Scheduling, time tabling & project management
21. Stochastic programming; stochastic modelling; simulation
22. System dynamics; dynamic modelling
23. Telecommunication & network analysis

Special Journal Issues:
European Journal of Operational Research Journal Organizacija

STREAM ORGANIZERS
Kristiaan Pelckmans, University College London (UK) Kristiaan.Pelckmans@esat.kuleuven.be
Jacob Kogan, University of Maryland Baltimore County (USA) kogan@math.umbc.edu
Süreyya Özöðür-Akyüz, Middle East Technical University & Sabancý University(Turkey) sozogur@metu.edu.tr

PhD student positions in Helsinki on mining and learning networks

Applications are invited for (up to) four-year fully-funded

PhD student positions

at the Department of Computer Science at the University of Helsinki, Finland. The selected students will receive well-supervised PhD training in a world-class research environment on the topics of data mining and machine learning. The starting date is flexible and to be negotiated, at the earliest March 1st 2009.

Specifically, the selected students will be working on methods for learning networks and graphs from a variety of data and utilizing the found structures for visualization, explanation, and prediction. The research will be carried out in the context of the Finnish Centre of Excellence for Algorithmic Data Analysis (Algodan) and the Helsinki Institute for Information Technology (HIIT). Students will be supervised by senior members of the recently established Discovering Network Structures (DiNS) collaboration:

– Jaakko Hollmen, chief research scientist
– Patrik Hoyer, academy research fellow
– Aapo Hyvarinen, professor
– Mikko Koivisto, academy research fellow
– Heikki Mannila, academy professor
– Petri Myllymaki, professor
– Juho Rousu, professor
– Hannu Toivonen, professor
– Esko Ukkonen, professor

Applications are welcome from candidates with a master’s degree or equivalent (students close to finishing can also apply) in a relevant quantitative topic, such as (for instance) computer science or statistics. Strong mathematical skills, adequate programming skills, and a good command of English are essential.

The salary for a starting doctoral student is based on level 2 of the demands level chart for teaching and research personnel. With the salary component based on personal work performance the overall starting salary is typically between 2000 – 2200 euros per month. There is no tuition fee at the university.

Applications with full contact information, a brief (one-page) statement of research interests, a CV and a transcript of studies (including all courses and grades) should be sent by email to Patrik Hoyer, patrik.hoyer@helsinki.fi. All applications received no later than Dec 8th 2008 will receive equal and full consideration.

Finland has a high standard of living and a well-developed democratic welfare state, has been a member of the EU since 1995, and consistently ranks at or near the top in various international comparisons of national performance. English is widely spoken, particularly in Helsinki.

For further information please contact Prof. Heikki Mannila, +358 9 191 51246, heikki.mannila@cs.helsinki.fi, or see the DiNS project webpage and the links therein:

http://www.cs.helsinki.fi/group/dins/

Further information on the research environment and on working in Finland:

http://www.cs.helsinki.fi/research/algodan/
http://www.cs.helsinki.fi
http://www.hiit.fi
http://www.helsinki.fi/intstaff/
http://en.wikipedia.org/wiki/Finland

One Research Fellow Position Available at RSISE@ANU

We are seeking an outstanding Research Fellow with excellent mathematical background and research expertise in

– Machine Learning or
– (Algorithmic) Information Theory or
– (Bayesian) Statistics or
– Artificial Intelligence or
– related area.

Possible backgrounds are a PhD, or near completion of a PhD, in mathematics, physics, computer science, engineering, or related. The initial appointment will be for 2-3 years.

The new employee will interact with Dr. Marcus Hutter and other people in the RSISE at the ANU.

Information for applicants:
– http://www.hutter1.net/rsise/postdoc09.htm
– http://jobs.anu.edu.au/PositionDetail.aspx?p=380

Closing Date: 16 January 2009

The Australian National University (ANU) is located in the city of Canberra, the Federal Capital of Australia. The ANU consistently ranks top among all Universities in the southern hemisphere, third in the Asia/Pacific region, and in the top 50 worldwide.

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.