| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
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| Initial Amendment Date: | July 25, 2019 |
| Latest Amendment Date: | September 7, 2021 |
| Award Number: | 1921366 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Swatee Naik
snaik@nsf.gov (703)292-4876 DMS Division Of Mathematical Sciences MPS Direct For Mathematical & Physical Scien |
| Start Date: | September 1, 2019 |
| End Date: | August 31, 2022 (Estimated) |
| Total Intended Award Amount: | $25,500.00 |
| Total Awarded Amount to Date: | $25,500.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
2500 BROADWAY LUBBOCK TX US 79409 (806)742-3884 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
2625 Memorial Circle Lubbock TX US 79409-1035 |
| Primary Place of Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
INFRASTRUCTURE PROGRAM, STATISTICS, MATHEMATICAL BIOLOGY |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.049 |
ABSTRACT
The workshop "Scientific Computing meets Machine Learning and Life Sciences" will be held on the campus of Texas Tech University in Lubbock, TX, from October 7 through October 9, 2019. This workshop will bring together leading experts and early career researchers from mathematics, statistics, computer science, machine learning, data sciences, and life sciences to report on cutting-edge and state-of-the-art computational algorithms in scientific computing and to identify computational and statistical challenges and open problems in machine learning and the life sciences. In addition, the workshop will provide a forum for an international and diverse group of researchers to foster communication, to facilitate new collaborative interactions, and to initiate joint research projects that will address the open and emerging issues and the computational and statistical challenges posed in machine learning and the life sciences. The three-day workshop will consist of presentations, posters, and group discussions that will stimulate an intensive exchange of ideas and foster fruitful interactions. This award supports the attendance of both researchers and graduate students, with priority given to graduate students, postdoctoral scholars, early career investigators, members of under-represented groups, and researchers who do not have other federal support.
Scientific computing is an increasingly important tool in many areas of science and engineering, such as biomedical imaging, genomics, proteomics, phylogeny, computer vision, and precision medicine, allowing biological data and systems to be explored that are not amenable to theoretical or experimental investigations. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. The advent of the big data era pushed machine learning to the forefront and has spurred broad interests in machine learning in recent years. The field of life sciences has advanced through a synergistic interplay between deep understanding of biology and mathematical techniques, especially from computational mathematics, probability, and statistics. Still, biologists are overwhelmed by the amount of data being generated and the new methods required for data-management. Quantitative theories are needed to help interpret and to contextualize observations. A variety of new challenges in scientific computing for machine learning have emerged in recent years that are related to the life sciences, such as developing predictive models for disorder detection, drug repurposing, toxicity prediction, electronic health record analysis, language translation, etc. These issues and many other open problems will be discussed among the diverse group of scientists participating in the workshop. More information is available at http://www.math.ttu.edu/scmlls2019/.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The workshop Scientific Computing meets Machine Learning and the Life Sciences (SCMLLS) was held at Texas Tech University on October 7-9, 2019. The workshop brought together leading experts and early career researchers from mathematics, statistics, computer science, machine learning, data sciences, and life sciences to report on cutting-edge and state-of-the art computational algorithms in scientific computing to identify computational and statistical challenges and open problems in machine learning and the life sciences. In addition, the workshop provided a forum for an international and diverse group of researchers to foster communication, to facilitate new collaborative interactions and to initiate joint research projects to address open and emerging issues on computational and statistical challenges posed in machine learning and the life sciences. The workshop featured 4 plenary talks, 12 invited talks, and 36 graduate student/postdoc posters, one panel discussion led by three distinguished panelists, two group discussions divided into three topics related to the theme of this workshop: scientific computing, machine learning, and life sciences. In addition, a tutorial was held for students during an evening session. This workshop included attendance of 18 past-PhD researchers who presented talks and 20 graduate students who presented posters. A Virtual Follow-up Meeting of the successful SCMLLS was held on March 5, 2022. All participants from 2019 were invited to attend the Virtual meeting as well as an open invitation to many others. Six invited speakers showcased some new and exciting advances in computational, statstical, and mathematical methods in machine learning in the life sciences.
Last Modified: 08/12/2022
Modified by: Linda J Allen
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