TCMM 2014
International Workshop on Technical Computing for Machine Learning and
Mathematical Engineering
September 8-12, 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.
Additional details will be announced shortly.
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:
Registration opens: June 11, 2014
Deadline extended abstract/demo submission: July 31, 2014
Deadline for registration: September 1, 2014
Invited speakers (confirmed):
James Bergstra, Center for Theoretical Neuroscience, Univ. 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 Submission, Registration, Location
and Venue) see http://www.esat.kuleuven.be/stadius/tcmm2014/
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