The NIH Big Data to Knowledge (BD2K, http://bd2k.nih.gov/) initiative announces the release of three related RFAs for new training programs and revisions to existing training programs in biomedical Big Data Science:
- Predoctoral Training in Biomedical Big Data Science (T32) (NOT-HG14-004)
- Revisions to Add Biomedical Big Data Training to Active Institutional Training Grants (T32) (NOT-HG14-005)
- Revisions to Add Biomedical Big Data Training to Active NLM Institutional Training Grants in Biomedical Informatics (T15) (NOT-HG14-006)
The first deadline for these applications is July 28, 2014, with an optional letter of intent due June 28, 2014.
Please view full details by going to the links above.
From the NSF:
The National Science Foundation (NSF) Division of Mathematical Sciences (DMS) aims to enhance the synergistic relationships between the mathematical sciences and other NSF-supported disciplines through the Mathematical Sciences Innovation Incubator (MSII) activity. The MSII activity encourages and supports new research collaborations among mathematical scientists and other scientists and engineers working in NSF-supported research areas of high national priority by:
* facilitating DMS co-review and co-funding of multi-disciplinary research collaborations involving mathematical scientists;
* providing leverage for investments of non-DMS NSF programs in projects that include mathematical scientists; and
* providing a uniform mechanism through which collaborative research teams involving mathematical scientists can request DMS co-review.
To view more details, please visit the MSII page. To apply and to view this complete letter, go to:
News & announcements for the SIAM membership community
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Dear SIAM members,
Did you know that SIAM offers discounted member rates for members of other mathematical societies? SIAM has a reciprocity agreement with 12 societies: view the entire list. If you would like to change your membership to a “reciprocal” category, contact SIAM Customer Service at firstname.lastname@example.org.
Boris Kramer of the Virginia Tech SIAM chapter gives us a recap of the Past President’s visit to VT:
The SIAM Student Chapter at Virginia Tech was excited to welcome Dr. Nick Trefethen, former SIAM president, to our campus in Blacksburg, VA. Dr. Trefethen was a guest of the College of Science’s Academy of Integrated Science. He gave the initial lecture in the distinguished lecture series associated with the Computational Modeling and Data Analytics initiative. Dr Trefethen also kindly agreed to give a special talk for the Virginia Tech SIAM Student Chapter, which was attended by both graduate and undergraduate students. Dr. Trefethen talked about Chebfun, an extensive Matlab computing toolbox based on Chebyshev approximations of continuous functions. During the presentation, we were able to interactively try out various features of the toolbox on our laptops, which was very insightful and fun. Overall, this event successfully broadened our chapter’s visibility on campus and was a nice add-on to our regular biweekly speaker series.
What is good enough to aid health economics decision making?
Philadelphia, PA—A computer model is a representation of the functional relationship between one set of parameters, which forms the model input, and a corresponding set of target parameters, which forms the model output. A true model for a particular problem can rarely be defined with certainty. The most we can do to mitigate error is to quantify the uncertainty in the model.
In a recent paper published in the SIAM/ASA Journal on Uncertainty Quantification, authors Mark Strong and Jeremy Oakley offer a method to incorporate judgments into a model about structural uncertainty that results from building an “incorrect” model.
“Given that ‘all models are wrong,’ it is important that we develop methods for quantifying our uncertainty in model structure such that we can know when our model is ‘good enough’,” author Mark Strong says. “Better models mean better decisions.” Read the rest of this entry »
From Lewis-Burke Associates LLC:
President Obama proposes significant national investments in agencies and programs critical to the applied mathematics and computational science research communities:
- National Science Foundation – $7.255 billion in FY 2015 (1.2 percent above the FY 2014 enacted funding level); DMS would see a decrease of 0.5 percent from the FY 2014 level.
- Department of Energy’s Office of Science – $5.11 billion in FY 2015 (0.9 percent above the FY 2014 level), with Advanced Scientific Computing Research increasing 13.2 percent over the FY 2014 level;
- Department of Defense Basic Research – $2.02 billion in FY 2015 (6.9 percent down from the FY 2014 level), with DARPA Defense Research Sciences decreasing by 0.9 percent from the FY 2014 request level.
- National Institutes of Health – $30.4 billion in FY 2015 (0.7 percent over the FY 2014 level).
Please see a full report of Programs of Interest to the Applied Mathematics and Computational Science Communities in the President’s FY 2015 Budget Request.
From the NSF:
The NSF Research Traineeship (NRT) program is a new NSF graduate education initiative. It is designed to encourage the development of bold, new, potentially transformative, and scalable models for STEM graduate training that ensure that graduate students develop the skills, knowledge, and competencies needed to pursue a range of STEM careers.
The NRT program initially has one priority research theme – Data-Enabled Science and Engineering (DESE); in addition, proposals are encouraged on any other crosscutting, interdisciplinary theme. In either case, proposals should identify the alignment of project research themes with national research priorities and the need for innovative approaches to train graduate students in those areas. NRT projects should develop evidence-based, sustainable approaches and practices that substantially improve STEM graduate education for NRT trainees and for STEM graduate students broadly at an institution. NRT emphasizes the development of competencies for both research and research-related careers.
View more details on the NSF site here.
SIAM is pleased to announce the 2014 Class of SIAM Fellows. These distinguished members were nominated for their exemplary research as well as outstanding service to the community. Through their contributions, SIAM Fellows help advance the fields of applied mathematics and computational science.
SIAM would like to congratulate these 32 members of the community listed below in alphabetical order:
Mark Ainsworth, Brown University
John S. Baras, University of Maryland, College Park
Lorenz T. Biegler, Carnegie Mellon University
Åke Björck, Linköping University, Emeritus
Each year, the Society for Industrial and Applied Mathematics (SIAM) designates as Fellows of the society members who have made outstanding contributions to fields served by SIAM.
This year, SIAM is happy to recognize 32 members of the community for this honor. Fellows are nominated by peers for their distinguished contributions to the fields of applied mathematics and computational science and related disciplines.