International Workshop on Technical Computing for Machine Learning and Mathematical Engineering

International Workshop on Technical Computing for Machine Learning and Mathematical Engineering

Posted by Rebecca Martin on Sun, 29/06/2014 - 15:25

TCMM 2014
International Workshop on Technical Computing for Machine Learning and Mathematical Engineering
8 - 12 September, 2014 - Leuven, Belgium

Workshop homepage: http://www.esat.kuleuven.be/stadius/tcmm2014/

The workshop will provide a venue for researchers and practitioners to interact on the latest developments in technical computing in relation to machine learning and mathematical engineering problems and methods (including also optimization, system identification, computational statistics, signal processing, data visualization, deep learning, compressed sensing and big-data). A special attention will be paid to implementations on high-level high-performance modern programming languages suitable for large-scale, parallel and distributed computing and capable to efficiently handle structured data. The emphasis is especially on the open-source alternatives, including but not limited to Julia, Python, Scala and R.

The 3 days main event (8-10 September) will consist of invited and contributed talks as well as poster presentations. It will be followed by a 2 days additional event (11-12 September) including software demos and hands-on tutorials on selected topics.

Attendees can register to the main event only or to the full workshop. Submission of extended abstracts (no longer than 2 pages) are solicited for the main event. Accepted abstracts will be presented either in the format of poster presentation or as contributed talks. Submission of demo presentations will be solicited for the two days additional event.

Topics of special interest:

Grid/Cloud/GPUs for technical computing
high-performance/parallel computing and use of related libraries (e.g., Theano)
efficient handling of structured data and use of related libraries (e.g., Pandas)
data visualization and related libraries
methods for exploiting sparsity in implementations
templates for customized solvers for (convex/distributed) optimization
integration of high-level languages and use of related libraries (e.g., PyCall)
implementation case studies:
performance comparison of different high-level languages for technical computing
application of machine learning methods on challenging problems (e.g., Kaggle challenges)
libraries for machine learning and mathematical engineering

Important dates:

Deadline extended abstract/demo submission: 31 July 2014
Deadline for registration: 1 September 2014

Confirmed invited speakers:

James Bergstra, Center for Theoretical Neuroscience, University of Waterloo
Jeff Bezanson, MIT
Luis Pedro Coelho, European Molecular Biology Laboratory (EMBL)
Stefan Karpinski, MIT
Graham Taylor, School of Engineering, University of Guelph
Ewout van den Berg, IBM T.J. Watson Research Center

Organizing committee:

Marco Signoretto, Department of Electrical Engineering, KU Leuven
Johan Suykens, Department of Electrical Engineering, KU Leuven
Vilen Jumutc , Department of Electrical Engineering, KU Leuven

For further information (including Registration, Location and Venue) see http://www.esat.kuleuven.be/stadius/tcmm2014/