| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
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| Initial Amendment Date: | November 3, 2016 |
| Latest Amendment Date: | November 3, 2016 |
| Award Number: | 1642637 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Joanna Kania-Bartoszynska
jkaniaba@nsf.gov (703)292-4881 DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | January 1, 2017 |
| End Date: | December 31, 2017 (Estimated) |
| Total Intended Award Amount: | $34,300.00 |
| Total Awarded Amount to Date: | $34,300.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1600 GRAND AVE SAINT PAUL MN US 55105-1899 (651)696-6062 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1600 Grand Avenue Saint Paul MN US 55105-1801 |
| 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 |
| 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 conference, "Topological Data Analysis: Theory and Applications," will take place at Macalaster College (St. Paul, MN) from June 12-16, 2017. Topological Data Analysis (TDA) is a relatively recent and quickly-developing area of research at the intersection of mathematics, statistics, and computer science. The goal of the conference is to encourage research in TDA by faculty and students, especially at primarily undergraduate institutions, who might not have prior experience with TDA. As such, this conference will attract faculty, graduate students, and advanced undergraduates from colleges and universities across the upper Midwestern states. The conference is designed to equip attendees with not only the theoretical framework of TDA, but also practical computational tools, providing points of entry so that faculty and students from diverse settings can begin research in topological data analysis and incorporate this work into their teaching. The conference will spur new research collaborations between institutions and across disciplines. The resulting monograph, prepared by principal lecturer Dr. Vin de Silva, Associate Professor of Mathematics at Pomona College, should be of interest to mathematicians, scientists and students.
In recent years, Topological Data Analysis (TDA) has attracted widespread interest from mathematicians and scientists looking for new tools to analyze ever increasing amounts of complex data arising from neuroscience, digital imaging, genetics, biological aggregations, sensor networks, cancer research, and other areas. The intellectual appeal of TDA arises from its combination of advanced mathematics, cutting-edge algorithms, and practical applications. Yet, despite its mathematical sophistication, TDA methodology is surprisingly intuitive and lends itself well to research with students, even at the undergraduate level. The principal lecturer at this conference will be Dr. Vin de Silva who has been a key contributor to the development of TDA. Lecture topics will include winding numbers, simplicial homology and cohomology, the persistence algorithm, stability theorems, zigzag persistence, category theory and generalized persistence, and Reeb cosheaves. In addition to lectures, the conference will feature lab sessions that will offer participants hands-on experience analyzing real data using state-of-the-art TDA software, as well as a poster session highlighting TDA research involving students. This conference will focus on mathematics and computation rather than statistics, and it will emphasize research in the setting of primarily undergraduate institutions.
Information about the conference is available at http://pages.stolaf.edu/tda-conference/
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 primary outcome of this project was a five-day workshop on Topological Data Analysis (TDA) held at Macalester College in St. Paul, MN on June 12-16, 2017. TDA is a young and quickly-developing area of research at the intersection of mathematics, statistics, and computer science. In recent years, TDA has attracted widespread interest, not only from mathematicians, but also from scientists looking for new tools to analyze ever-increasing amounts of complex data in areas including neuroscience, digital imaging, genetics, biological aggregations, sensor networks, and cancer research. The conference was structured so that it was accessible to faculty from primarily undergraduate institutions in the upper midwest, along with graduate students and advanced undergraduate students. The principal speaker for the workshop was Vin de Silva, Associate Professor of Mathematics at Pomona College and one of the world's foremost authorities on TDA. Professor de Silva gave ten lectures ranging from basic introductory material up through current topics at the leading edge of research in TDA. The topics covered were:
- Winding Numbers
- Cycles and Cocycles, Sensor Network Coverage
- Persistent Homology
- Topological Data Analysis
- Persistence Diagrams
- Zigzag Persistence
- The Stability Theorem
- Topological Dimensionality Reduction
- Persistence and Category Theory
- Reeb Graphs
In addition to the lectures, there were hands-on computing laboratories demonstrating the various computational tools used in TDA and a poster session allowing attendees to present work they have done in this area. The workshop was attended by a total of 51 participants, including 34 faculty members, 10 graduate students, and seven undergraduate students. Approximately 75% of the attendees had no prior experiencewith TDA.
Last Modified: 01/19/2018
Modified by: Matthew Richey
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