NEWS & HIGHLIGHTS
Also, visit our Calendar of Events for upcoming DELTA Webinars, workshops and conferences.
Elected AAAS Fellow, Organizing symposium on Graph Theory Underpinning New Domains of Physical Chemistry symposium at National Meeting of the American Chemical Society.
Upcoming invited presentation at “Computational Topology and Application” at the Tsinghua Sanya International Mathematics Forum (TSIMF) in Sanya, China, December 7-11, 2020.
Telluride Science Lecture Series, Energy Landscapes symposium, “Descriptors of Energy Landscapes Using Topological Analysis” (virtual due to Covid-19), August 11, 2020.
Telluride Virtual Workshop, Multi-Scale Quantum Mechanical Analysis of Condensed Phase Systems symposium, “Finding Complexity in Complex Solutions”, July 29, 2020.
Telluride Science Lecture Series, Multi-Scale Quantum Mechanical Analysis of Condensed Phase Systems symposium, “Structural and Dynamic Topology of Complex Solutions” (virtual due to Covid-19), July 21, 2020.
SIAM Conference on Mathematics of Data Science (virtual due to Covid-19), “Approaches to Understand Topology in Chemistry”, June 27, 2020.
Invited talk at SIAM Conference on Mathematics of Data Science 2020 in Cincinnati, Ohio, May 5-7, 2020.
Speaking at CU Boulder, Topology Day, April 21, 2020.
Invited talk on the topology of complex fluids at Physics of Liquids symposium at the spring meeting of the American Physical Society in Denver, Colorado, March 2-6, 2020.
Program Co-Chair for the 73rd Southeast Regional Meeting of the American Chemical Society (SERMACS) to be held November 10th - 13th, 2021 - Birmingham, AL. (https://sermacs2021.weebly.com/)
Clay Black (student, Annie Gorden's Group) – successfully defended his thesis 3/30/2020. “Copper(II) 2-Quinoxalinol Salen Type Ligands as Catalysts for C-H Oxidation Reactions.” Clay will be leaving to take a position in Houston, Texas.
Presenting our work in an invited presentation to be included in the symposium “Advancing Frontiers in Heteromultimetallic Chemistry” at the ACS National Meeting, Aug. 16 – 20, 2020 in San Francisco, CA (https://www.acs.org/content/acs/en/meetings/national-meeting/fall-2020-national-meeting-expo.html).
TEDx talk on “Discovering hidden structure in big data” (using TDA!) published/posted at https://www.ted.com/talks/bala_krishnamoorthy_discovering_hidden_structure_in_big_data (talk originally given in June 2019).
Presenting our work on geometric measure theory approaches to characterize soft matter surfaces at the ACS National Meeting, March 22, 2020 in Philadelphia, PA (https://www.acs.org/content/acs/en/meetings/national-meeting.html).
Presented the Geometry and Topology Seminar at Oregon State University (via Zoom) on May 19, 2020 titled "Jaccard Filtration and Stable Paths in the Mapper".
Published "How do I ... develop an online research seminar?" Notices of the American Mathematical Society, Volume 67, Number 8, September 2020.
CSU Press release, "From flocks of birds to schools of fish, data can take many shapes", August 11, 2020.
CSU graduate student Joshua Mirth defended his PhD thesis, "Vietoris-Rips Metric Thickenings and Wasserstein Spaces", on May 15, 2020. He will continue working with DELTA over the summer, before starting a Postdoctoral Research Position in Fall 2020 at Michigan State University.
Spoke on Applied topology: From global to local at the Mathematics of Data and Decisions at Davis (MADDD) Seminar, UC Davis, June 2, 2020, 4pm Pacific Time.
Through the use of examples, I will explain one way in which applied topology has evolved since the birth of persistent homology in the early 2000s. The first applications of topology to data emphasized the global shape of a dataset, such as the three-circle model for 3 x 3 pixel patches from natural images, or the configuration space of the cyclo-octane molecule, which is a sphere with a Klein bottle attached via two circles of singularity. More recently, persistent homology is being used to measure the local geometry of data. How do you vectorize geometry for use in machine learning problems? Persistent homology, and its vectorization techniques including persistence landscapes and persistence images, provide popular techniques for incorporating geometry in machine learning. I will survey applications arising from machine learning tasks in agent-based modeling, shape recognition, archaeology, materials science, and biology.
Spoke on Descriptors of Energy Landscapes using Topological Analysis (DELTA) at the Mathematics of Data Science VIrtual Lecture Series, Tufts University, June 4, 2020.
Many of the properties of a chemical system are described by its energy landscape, a real-valued function defined on a high-dimensional domain. I will explain how topology, and in particular persistent homology, can be used in order to describe some of the pertinent features of an energy landscape. Whereas a merge tree encodes how connected components of an energy landscape evolve as the energy level increases, persistent homology can also quantify the shape of these connected components. As a motivating example, we completely describe the sublevelset persistent homology of the n-alkanes. In a recently funded NSF Harnessing the Data Revolution project, the DELTA team is learning how to identify and leverage changing topological features of energy landscapes across a range of chemical conditions in order to predict reactivity.
All talks at: https://www.math.colostate.edu/~adams/talks/ (only some are relevant to DELTA).