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SIAM Activity Group on Linear Algebra

SIAG LA digest

Jan 7, 2019 • Jen Pestana

Happy New Year! This edition of the SIAG/LA digest will be my last as secretary. I am sure that Christine Klymko will do a great job as your new SIAG/LA secretary.


New SIAG/LA Officers

On behalf of the SIAG/LA we would like to welcome the new officers, whose term started on January 1:

Chair: Valeria Simoncini

Vice Chair: Melina Freitag

Program Director: Miroslav Tůma

Secretary: Christine Klymko

We wish them the very best for their upcoming term!

Also a big thank you to those who participated in the election by running for office or being part of the nomination committee.

Rising Stars in Computational and Data Sciences

The Institute for Computational Engineering and Sciences (ICES) at UT Austin and Sandia National Laboratories (SNL) are partnering together to host Rising Stars in Computational and Data Sciences, an intensive workshop for women graduate students and postdocs who are interested in pursuing academic and research careers. The workshop will be held April 9-10, 2019 at ICES.

We are seeking nominations for outstanding candidates in their final year of PhD or within three years of having graduated. We will select approximately 25 women to come to ICES for two days of research presentations, poster sessions, and interactive discussions about academic and research careers, with travel expenses fully covered.

Full details, including the nomination form, are at

Please consider nominating one of your outstanding current/recent PhD students or postdocs. Nominations are due January 22, 2019.

On behalf of the organizing committee: Tammy Kolda (SNL), Jim Stewart (SNL), Rachel Ward (ICES), Karen Willcox (ICES)

HMI Workshop - Beyond the discrete: iterative methods from the continuum perspective

The Hamilton Mathematics Institute (HMI) at Trinity College Dublin will be hosting a workshop next summer titled “Beyond the discrete: iterative methods from the continuum perspective”. Its purpose is to advance a more full view of iterative methods, which connects the measure of convergence in finite dimensions to the underlying continuum problem and the discretization that induced the finite dimensional linear system in question. We seek to introduce these ideas to younger researchers in the early stages of their careers while bringing together current leaders in the field to have fruitful discussions and share their latest research. This workshop will combine introductory lectures on interrelated subtopics and hosting and encouraging interesting discussions. There will also be a poster session wherein younger researchers can present their work.

The workshop has five invited speakers confirmed: Victorita Dolean (University of Strathclyde/Nice), Maya Neytcheva (Uppsala University), Catherine Powell (University of Manchester), Zdenek Strakoš (Charles University Prague), and Walter Zulehner (Johannes Kepler University, Austria).

The Workshop will take place from the morning of June 3 to lunchtime of June 7, 2019 in the Hamilton Building of Trinity College Dublin in Ireland. More detailed information, including how to register, can be found at We encourage interested colleagues to register and book hotel reservations as soon as possible, as the demand for hotel rooms is exceedingly high during the summer months, particularly in June.

Important Deadlines:
March 20, 2019: Registration with talk/poster abstract submission (for early career people)
March 20, 2019: Registration with request for possible limited financial support (for early career people)
May 20, 2019: General registration deadline

2019 Gene Golub SIAM Summer School on High Performance Data Analytics

2019 Gene Golub SIAM Summer School on High Performance Data Analytics

About the program

The 10th Gene Golub SIAM Summer School will take place in France, at a conference center in Aussois, in the French Alps from June, 17 to June, 28, 2019 and will be held in conjunction with the SIAM Activity Group on Supercomputing. The intended audience is intermediate graduate students (students with a Master’s degree, Ph.D. students, or equivalent).

The focus of the school will be on large-scale data analytics, which lies at the intersection of data analytics algorithms and high performance computing. Students will be introduced to problems in data analytics arising from both the machine learning and the scientific computing communities. The school will include perspectives from industry, such as Hodge Star Scientific Computing, IBM, and NVIDIA, as well as from academic instructors, including

  • Animashree Anandkumar (Caltech and Nvidia)
  • Haesun Park (Georgia Institute of Technology)
  • Tammy Kolda (Sandia National Laboratories)
  • Jack Poulson (Hodge Star Scientific Computing)
  • Costas Bekas (IBM)

All courses will have a strong computing component. The school will be held in the spirit of Gene Golub, with lots of interactions between the lecturers and the participants. A poster blitz and a poster session will be organized for students who wish to present their own work. The lectures will have associated labs that will allow the students to get hands-on experience and have a closer interaction with the lecturers. The school is being organized by Laura Grigori (Inria and Sorbonne University), Matthew Knepley (University at Buffalo), Olaf Schenk (Università della Svizzera italiana), and Rich Vuduc (Georgia Institute of Technology).


We invite graduate students from disciplines related to the topic of the school (mathematical sciences, computing sciences, or a domain science with a computational science and engineering focus), to apply. Our mission is to increase diversity and we encourage students from under-represented groups to apply. Attendance will be restricted to about 40 well-qualified participants, who will be selected based on the submitted application documents. In order to apply, please send the following documents, all written in English and combined into a single PDF file to :

  • A cover letter describing your experience and motivation to take part in G2S3 2019 (2 pages max.)
  • A short CV (2 pages max.)
  • A transcript containing relevant classes you attended (only course titles and grades)

The applicants should provide specific forms of evidence in their materials of the following:

  • Prior relevant research in any of the topic areas of the summer school OR in related areas,
  • their interest in learning about multiple topic areas (e.g., a student from computational statistics has applications that require scaling to large-scale parallel computers),
  • description of collaborative or interdisciplinary projects or work,
  • a description of software and programming background.

Please use the following email subject: [G2S3 Application]: last name, first name.

In addition, one letter of recommendation from your advisor should be sent separately to using the email subject [G2S3 Reference]: last name, first name.

Applying for Financial Support

The generous sponsorship from SIAM makes it possible that all selected participants will have their lodging and meals covered by the school. In addition, we will (at least partially) reimburse reasonable travel costs upon application. If you require travel support, then please submit a brief statement indicating the expected amount with your application. A request of funding will not influence the decision on admission to the school.

Applications are being accepted now through February 8, 2019. More information is available on the G2S3 2019 website (

2019 Applied Machine Learning Summer School at Los Alamos National Laboratory

We are excited to announce the 2019 Los Alamos National Laboratory Applied Machine Learning/Applied Research in Earth Sciences (ARiES) Summer School. The theme topics for this year include

  • Scientific Machine Learning for Geoscience Applications;
  • Nonnegative Tensor Factorization for Machine Learning;
  • Machine Learning for Analyzing Scientific Images;
  • Active Learning Applied to Fluid Flow in Nanoscale Porous Media.

We are currently accepting applications. Successful applicants will receive a prestigious research fellowship, hands-on technical training experiences and professional development from internationally reputable geophysics, space, and computational scientists.

For more information about our summer school, project, and mentor information, please visit our summer school website at Applications must be submitted by January 3, 2019, for the first round of consideration. Late applications may also be considered.

Two Tenure-track Assistant Professor positions, ICES, UT Austin

The Institute for Computational Engineering and Sciences (ICES) at The University of Texas at Austin is an interdisciplinary research and education institute focused on transforming science, engineering, and medicine through computation. ICES invites applicants for two tenure-track assistant professor faculty positions.

The first position is at the interfaces of Data Science and Computational Science. Areas of interest include, but are not limited to, computational statistics, machine learning, graph theory, randomized linear algebra, and tensor methods. Candidates should have a strong connection to challenging applications in science, engineering and/or medicine. Candidates should be committed to establishing an interdisciplinary research program at the intersection of advanced mathematical, statistical, and computational techniques, high-performance computing, and target applications. The successful candidate will be appointed in an appropriate department at UT, depending on their research and teaching interests.

The second position is in the area of Computational Medicine. Areas of interest include, but are not limited to, computational oncology, computational cardiology, computational neurosciences, and imaging science. Candidates should be committed to establishing an interdisciplinary research program at the intersection of target medical problems and advanced mathematical, statistical, and computational techniques. The successful candidate will be appointed in an appropriate department at UT, depending on their research and teaching interests. This includes the possibility of an appointment at the new Dell Medical School.

Review of applications will begin January 15, 2019. Full details on both positions can be found at

Postdoctoral position, Numerical Linear Algebra, University of Edinburgh

Applications are invited for a 3-year postdoctoral position as part of an EPSRC project in the area of numerical linear algebra for PDE-constrained optimisation problems, with applications to data science. The successful candidate will join the research group of Dr John Pearson in the School of Mathematics, University of Edinburgh.

The project is funded by the EPSRC Grant “Modern Linear Algebra for PDE-Constrained Optimisation Models for Huge-Scale Data Analysis”, and by the University of Edinburgh. The successful candidate will contribute to the development of numerical methods and iterative solvers for huge-scale matrix systems arising from optimisation problems with PDE constraints, and will apply their techniques to cutting-edge problems from data science. Experience with PDE-constrained optimisation/inverse problems, and/or numerical methods for PDEs (including numerical linear algebra for solving matrix systems), is desirable. Expertise in a relevant programming language (e.g., Python, C++, MATLAB) is essential.

The position should be taken up in September 2019, or an alternative date by mutual agreement. Further particulars, as well as the application link, may be found at

Informal enquiries are encouraged, and may be made to John Pearson at The closing date for applications is 5pm on 22 January 2019.

Submissions for next SIAM-LA digest

The next SIAM-LA Digest is due to be sent out on Feb 04, 2019. Please send any postings for the next Digest to siam-la at Only SIAG/LA members may submit postings. To contact the list owner, send an email to siam-la-owner at