Subject: NA Digest, V. 19, # 33 NA Digest Sunday, August 25, 2019 Volume 19 : Issue 33 Today's Editor: Daniel M. Dunlavy Sandia National Labs dmdunla@sandia.gov Today's Topics: Software Release: MORLAB 5.0 Domain Decomposition Methods, Hong Kong, China, Dec 2019 Winter School, Low-rank Models, Switzerland, Jan 2020 Postdoc Position, Numerical methods for data assimilation with big data Postdoc Position, Optimization for Statistical Learning Postdoc Position, Statistical Learning with Sparsity and Beyond Postdoc Position, Stochastic Traffic Networks, Gothenburg PhD Position, Lund Univ, Sweden Contents, Information and Inference: A Journal of the IMA, 8 (3) Subscribe, unsubscribe, change address, or for na-digest archives: http://www.netlib.org/na-digest-html/faq.html Submissions for NA Digest: http://icl.utk.edu/na-digest/ ------------------------------------------------------- From: Steffen W. R. Werner werner@mpi-magdeburg.mpg.de Date: August 23, 2019 Subject: Software Release: MORLAB 5.0 Version 5.0 of the MORLAB, Model Order Reduction LABoratory, toolbox has been released. The toolbox is a collection of MATLAB and Octave routines for model order reduction of linear dynamical systems based on the solution of matrix equations. The toolbox contains implementations for standard, descriptor and second-order systems, as well as systems theoretic subroutines. New core features are: - model reduction methods for discrete-time standard and descriptor systems - new matrix equation solvers for dual continuous-time Lyapunov and Riccati equations - matrix equation solvers for discrete-time Lyapunov, Sylvester and Riccati equations - frequency and time evaluation and visualization routines for all supported system classes - computation of projection matrices for model reduction - parallel computation of different reduced-order models at the same time For more details on this software, see: http://www.mpi-magdeburg.mpg.de/projects/morlab ------------------------------------------------------- From: Felix Kwok felix_kwok@hkbu.edu.hk Date: August 23, 2019 Subject: Domain Decomposition Methods, Hong Kong, China, Dec 2019 DD26: Early-bird registration ends soon The 26th International Conference on Domain Decomposition Methods (DD26) will be held at the Chinese University of Hong Kong (CUHK) from December 2 to 6, 2019. The purpose of the conference is to bring together mathematicians, computational scientists and engineers who work in the main themes of Domain Decomposition, including theoretical, algorithmic and implementation aspects of domain decomposition methods, solvers for multiphysics problems, parallel-in-time methods, multigrid and multilevel methods, fast solvers and preconditioning, and applications of such methods in physics and engineering. This is a gentle reminder that early registration is now open until August 31, 2019 at https://www.math.cuhk.edu.hk/conference/dd26/?Conference-Registration After this date, the fees will increase in each category. If you are a minisymposium speaker, the organizer(s) of your session will be in touch with you soon to collect your abstract in LaTeX format. We continue to invite contributed talks and posters that fit the themes of DD26. If you wish to present your work, please send your name, institution, talk title and abstract to dd26@math.cuhk.edu.hk by September 15, 2019 (extended deadline). In order to help participants save on accommodation costs, we have set up a page to facilitate room sharing among conference participants. Please contact us at dd26@math.cuhk.edu.hk if you wish to access this page. More information can be found under "Accommodation" on the conference website. ------------------------------------------------------- From: Daniel Kressner daniel.kressner@epfl.ch Date: August 21, 2019 Subject: Winter School, Low-rank Models, Switzerland, Jan 2020 Winter school on low-rank models Villars, Switzerland, January 12-17, 2020 http://www.lowrank2020.ch The school will take place in the Swiss Alps at Villars-sur-Ollon on January 12-17, 2020. Its topic is low-rank models and their use in numerical optimization and approximation. Specific focus will be given to modern applications in data science. The school is aimed primarily at PhD students, post-docs, and young researchers. Participants will attend lectures and hands-on tutorials by leading experts. The lecturers are Nicolas Boumal (Princeton University), Lieven De Lathauwer (KU Leuven), Ivan Oseledets (Skoltech), Reinhold Schneider (TU Berlin), and Madeleine Udell (Cornell University). The deadline for application is October 1, 2019. The fee for participation fee is 400 CHF, which includes local accommodation, all meal expenses, and one excursion. ------------------------------------------------------- From: Sarah Dance s.l.dance@reading.ac.uk Date: August 22, 2019 Subject: Postdoc Position, Numerical methods for data assimilation with big data The University of Reading invites applications for a postdoctoral research position, funded by the EPSRC project "Data Assimilation for the Resilient City" (DARE) - (https://research.reading.ac.uk/dare/). Data assimilation is an emerging mathematical technique for improving predictions from large and complex forecasting models, by combining uncertain model predictions with a diverse set of observational data in a dynamic feedback loop. As we move towards the era of exascale computing, a key issue is the ability to process large volumes of uncertain observation data efficiently to improve predictions of natural hazards such as storms and floods. The research undertaken by the post holder will develop new methods, , using ideas from fast multipole methods, initially in an idealized system, to underpin quantitative use of a diverse range of large observation datasets. Closing date 4 September Further details: https://jobs.reading.ac.uk/displayjob.aspx?jobid=5298 ------------------------------------------------------- From: Armin Eftekhari armin.eftekhari@gmail.com Date: August 24, 2019 Subject: Postdoc Position, Optimization for Statistical Learning The Department of Mathematics and Mathematical Statistics at Umea University has an opening for a postdoctoral researcher in mathematical statistics with an emphasis on optimization for statistical learning. The appointment is for two years (subject to satisfactory performance), starting in Fall 2019. The successful candidate is expected to conduct excellent research, actively engage with collaborators, and to participate in the daily activities of the research environment. Last day to apply is September 30, 2019. The expansion of Artificial Intelligence (AI), in the broad sense, is one of the most exciting developments of the 21st century. This progress opens up many possibilities but also poses grand challenges. The centre Wallenberg AI, Autonomous Systems, and Software Program (WASP) is launching a program to develop the mathematical side of this area. The aim is to strengthen the competence of Sweden as a nation within the area of AI and we are taking part of this program through this specific project. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry. For more information about the research and other activities conducted within WASP, please visit http://wasp-sweden.org/ . Optimization theory is vital to modern statistical learning and at the forefront of these advances, and the main objective of this postdoctoral position is to develop the next generation of optimization tools to address the above challenges in the context of modern statistical learning, and potentially explore their applications in AI, including medical imaging, automated quality control, and self-driving cars, evaluated on both simulated and real data. Within this broad framework, the successful candidate is encouraged to develop their own research agenda, in close collaboration with mentors and colleagues. Potential areas of interest include, but not limited to 1) Training generative adversarial networks, 2) Nonconvex algorithms for linear inverse problems (such as compressive sensing), 3) Robust optimization and defense against adversarial examples in deep neural nets, 4) Role of over-parametrization in training and generalization of deep neural nets, 5) Global geometry of nonconvex problems, 6) Efficient and scalable algorithms for constrained nonconvex optimization, 7) Application of Langevin dynamics and other Monte-Carlo techniques in optimization 8) Online and storage-optimal algorithms for large scale convex optimization. For more details and information on how to apply, please visit: https://umu.mynetworkglobal.com/en/what:job/jobID:284855/ ------------------------------------------------------- From: Armin Eftekhari armin.eftekhari@gmail.com Date: August 24, 2019 Subject: Postdoc Position, Statistical Learning with Sparsity and Beyond The Department of Mathematics and Mathematical Statistics at Umea University has an opening for a postdoctoral researcher in mathematical statistics with an emphasis on statistical learning with sparsity. The appointment is for two years (subject to satisfactory performance), starting in Fall 2019. The successful candidate is expected to conduct excellent research, actively engage with collaborators, and to participate in the daily activities of the research environment. Last day to apply is September 30, 2019. The expansion of Artificial Intelligence (AI), in the broad sense, is one of the most exciting developments of the 21st century. This progress opens up many possibilities but also poses grand challenges. The centre Wallenberg AI, Autonomous Systems, and Software Program (WASP) is launching a program to develop the mathematical side of this area. The aim is to strengthen the competence of Sweden as a nation within the area of AI and we are taking part of this program through this specific project. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry. For more information about the research and other activities conducted within WASP, please visit http://wasp-sweden.org/ . Modern statistical learning is at the forefront of these advances, and the main objective of this postdoctoral position is to develop cutting-edge mathematical and statistical theory and the next generation of computational tools to address the above challenges with an emphasis on learning with sparsity and other emerging data models, and to potentially explore their applications in AI, including medical imaging, automated quality control, and self-driving cars, evaluated on both simulated and real data. Understanding and exploiting sparsity and other data structures to extract useful information from big datasets with the purpose of making optimal decisions will be the main research thrust for this position. Within this broad framework, the successful candidate is encouraged to develop their own research agenda, in close collaboration with mentors and colleagues. Potential areas of interest include, but not limited to 1) Learning with multiple structures (such as sparsity and rank constraints), 2) Intelligent data sampling and uncertainty quantification in medical imaging, 3) Time-data tradeoffs in nonconvex learning, 4) Statistical learning with generative adversarial networks and their geometry, 5) Streaming and distributed algorithms for dimensionality reduction (such as sparse principal component analysis), 6) Defense against adversarial examples in deep neural nets. For more information and instructions on how to apply, please refer to: https://umu.mynetworkglobal.com/en/what:job/jobID:284612/ ------------------------------------------------------- From: Annika Lang annika.lang@chalmers.se Date: August 23, 2019 Subject: Postdoc Position, Stochastic Traffic Networks, Gothenburg Postdoctoral position in stochastic traffic networks Temporary position for 1+1 years (1 year at the Department for Mathematical Sciences, 1 year at the Department for Electrical Engineering) Starting date: January 1, 2020 or asap Application deadline: September 22 Link to the announcement: https://www.chalmers.se/en/about-chalmers/Working-at- Chalmers/Vacancies/Pages/default.aspx?rmpage=job&rmjob=7818&rmlang=UK The goal is to use stochastic partial differential equations to model traffic flows and to estimate parameters based on data from real measurements. For questions and more details on the project, please contact Annika Lang (annika.lang@chalmers.se). ------------------------------------------------------- From: Tony Stillfjord tony.stillfjord@math.lth.se Date: August 23, 2019 Subject: PhD Position, Lund Univ, Sweden A PhD position is available at Lund University, Sweden, in the division of Mathematics LTH and Numerical Analysis at the Centre for Mathematical Sciences [1]. This position is part of our effort to create a new research group at the intersection of the areas of partial differential equations and machine learning. The specific project aims to construct and analyze new numerical methods for optimization problems arising from machine learning applications. The position is funded by WASP [2], and the PhD student will be enrolled in the WASP Graduate School [3]. The deadline for application is 19th September 2019. For more details, please see the announcement at [4] and contact either Tony Stillfjord (tony.stillfjord@math.lth.se) or Eskil Hansen (eskil.hansen@math.lth.se). [1] http://www.maths.lu.se/english/ [2] https://wasp-sweden.org/ [3] https://wasp-sweden.org/graduate-school/ [4] https://lu.mynetworkglobal.com/en/what:job/jobID:284064/ ------------------------------------------------------- From: Information and Inference: A Journal of the IMA charlotte.parr@oup.com Date: August 20, 2019 Subject: Contents, Information and Inference: A Journal of the IMA, 8 (3) Contents, Information and Inference: A Journal of the IMA, 8 (3) Information and Inference: A Journal of the IMA Links to all articles in this issue are available online at: https://academic.oup.com/imaiai/issue/8/3 Exact solutions of infinite dimensional total-variation regularized problems, Axel Flinth and Pierre Weiss Matrix decompositions using sub-Gaussian random matrices, Yariv Aizenbud and Amir Averbuch Solving (most) of a set of quadratic equalities: composite optimization for robust phase retrieval, John C Duchi and Feng Ruan Sparsity/undersampling tradeoffs in anisotropic undersampling, with applications in MR imaging/spectroscopy, Hatef Monajemi and David L Donoho Near optimal sample complexity for convex tensor completion, Navid Ghadermarzy, Yaniv Plan, and Ozgur Yilmaz Bayesian sparse linear regression with unknown symmetric error, Minwoo Chae, Lizhen Lin, and David B Dunson ------------------------------------------------------- End of Digest **************************