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NADIGEST Home

NADIGEST Home

NADIGEST  August 2019, Week 4

NADIGEST August 2019, Week 4

Subject:

NA Digest, V. 19, # 33

From:

NA Digest Editor <[log in to unmask]>

Reply-To:

NA-Digest <[log in to unmask]>

Date:

Sun, 25 Aug 2019 11:00:15 -0400

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (336 lines)

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
	[log in to unmask]

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 [log in to unmask]
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 [log in to unmask]
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 [log in to unmask] 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 [log in to unmask] if you wish
to access this page.  More information can be found under
"Accommodation" on the conference website.

-------------------------------------------------------

From: Daniel Kressner [log in to unmask]
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 [log in to unmask]
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 [log in to unmask]
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 [log in to unmask]
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 [log in to unmask]
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 ([log in to unmask]).


-------------------------------------------------------

From: Tony Stillfjord [log in to unmask]
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 ([log in to unmask]) or
Eskil Hansen ([log in to unmask]).

[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 [log in to unmask]
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
**************************

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