last call for contributions - TCMM 2014

last call for contributions - TCMM 2014

Posted by Rebecca Martin on Tue, 29/07/2014 - 14:51

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

Workshop homepage:

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 are solicited for the main event. Submission of demo presentations are solicited for the two days additional event. For further information (including Registration, Location and Venue) see details at the workshop website.

Important dates:

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

Confirmed invited speakers (talks and tutorials):

James Bergstra, Center for Theoretical Neuroscience, University of Waterloo:
Theano and Hyperopt: Modelling, Training, and Hyperparameter Optimization in Python

Jeff Bezanson, MIT:

Luis Pedro Coelho, European Molecular Biology Laboratory (EMBL):
Large Scale Analysis of Bioimages Using Python

Steven Diamond, Stanford University
Convex Optimization in Python with CVXPY

Stefan Karpinski, MIT

Graham Taylor, School of Engineering, University of Guelph:
An Overview of Deep Learning and Its Challenges for Technical Computing

Ewout van den Berg, IBM T.J. Watson Research Center:
Tools and Techniques for Sparse Optimization and Beyond

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