Invited Speakers: Hélène Barcelo (Arizona State) Saugata Basu (Purdue) Ulrich Bauer (Technical University of Munich) Andrew Blumberg (Columbia) Peter Bubenik (Florida) Gunnar Carlsson (Stanford) Herbert Edelsbrunner (ISTA) Alexander Grigor’yan (Bielefeld) Facundo Memoli (Ohio State) Elizabeth Munch (Michigan State) Nina Otter (UCLA) Leonid Polterovich (Tel Aviv) Eric Sedgewick (De Paul) Vin de Silva (Pomona College) Katharine Turner (Australian National University)
In addition there will be contributed talks. A call for submission of abstracts for these talks and posters will follow.
Scientific Committee: Jacek Brodzki (University of Southampton) Frédéric Chazal (INRIA) Kathryn Hess (EPFL Lausanne) Brittany Fasy (Montana State University) Robert Ghrist (University of Pennsylvania) Matt Kahle (Ohio State University) Claudia Landi (Università di Modena e Reggio Emilia) Primoz Skraba (Queen Mary, University of London) Schmuel Weinberger (University of Chicago)
We are pleased to report that Conference Proceedings of ATMCS10 will be published in conjunction with the Journal of Applied and Computation Topology (APCT). All those contributing to the conference will be invited to submit research and survey papers.
The conference is supported by the Centre for Topological Data Analysis. Limited financial help will be available.
We look forward to welcoming you next year in Oxford!
Heather Harrington, Ulrike Tillmann and Vidit Nanda
Call for papers Smart Tools and Applications in Graphics 26-29 October 2021
STAG aims to offer a forum for discussing novel ideas and results in Computer Graphics and Visual Computing. Papers addressing both theoretical and application-oriented aspects of research are welcome. STAG also encourages dialogue and cross-fertilization between different fields, including Computer Graphics, Computer Vision, Artificial Intelligence, and Topological Data Analysis.
Computational Geometry, Computational Topology, Topological Data Analysis for Visual Computing are included in the list of topics of STAG 2021.
We’re pleased to announce the VII Mexican Workshop in Geometric and Topological Data Analysis, which will take place online September 22nd-29th, 2021. Registration is free but required, and we are accepting proposals for contributed talks and posters until July 12th. The mini-courses for this year’s workshop are:
Computational Homology, Dynamics, and Data Tomasz Kaczynski, Université de Sherbrooke
What we offer: We offer a postdoc position within the Department of Mathematical Sciences as part of the project “Deciphering Nanoporosity of Amorphous Materials using Topological Data Analysis”. The selected candidate will be conducting research supervised by Lisbeth Fajstrup and Christophe A.N. Biscio and in collaboration with the Department of Chemistry and Bioscience to find new topological descriptors of porosity. The selected candidate will visit Lawrence Berkeley National Laboratory, USA for 4 months to collaborate with Staff Scientist Dmitriy Morozov.
What we request: The selected candidate will hold a PhD in Mathematics or a related field and will have promising research results within Topological Data Analysis, Computational Topology or related subjects. The project to which the postdoc will contribute involves developing the theory of non-monotone and multidimensional persistence to describe variations of the free volume and in particular tunnels in amorphous materials and implementation of such insight as a tool. This will require a strong background in Topological Data Analysis either theoretical or computational.
You may obtain further professional information from Associate Professor Christophe Biscio, phone: +45 9940 8925, e-mail: email@example.com or Head of Department Søren Højsgaard, phone: +45 9940 8801, e-mail: firstname.lastname@example.org
Appointment as Postdoc presupposes scientific qualifications at PhD–level or similar scientific qualifications. The research potential of each applicant will be emphasized in the overall assessment. Appointment as a Postdoc cannot exceed a period of four years in total at Aalborg University. https://www.stillinger.aau.dk/vis-stilling/?vacancy=1153647
Organizer(s): Hengrui Luo, Lawrence Berkeley National Laboratory
Chair(s): Chul Moon, Southern Methodist University
3:35 PM EDT 317318 Characterizing heterogeous information in persistent homology with applications to molecular structure modeling
Speaker. Zixuan Cang, University of California, Irvine
Abstract. Persistent homology is a powerful tool for characterizing the topology of a dataset at various geometric scales. However, in addition to geometric information, there can be a wide variety of nongeometric information, for example, there are element types and atomic charges in addition to the atomic coordinates in molecular structures. To characterize such datasets, we propose an enriched persistence barcode approach that retains the non-geometric information in the traditional persistence barcode. The enriched barcode is constructed by finding the smoothest representative cocycles determined by combinatorial Laplacian for each persistence pair. We show that when combined with machine learning methods, this enriched barcode approach achieves state-of-the-art performance in an important real-world problem, the prediction of protein-ligand binding affinity based on molecular structures.
3:55 PM EDT 317284 Gromov-Wasserstein learning in a Riemannian framework
Speaker. Samir Chowdhury, Stanford University
Abstract. Geometric and topological data analysis methods are increasingly being used to derive insights from data arising in the empirical sciences. We start with a use case where such techniques are applied to human neuroimaging data to obtain graphs which can then yield insights connecting neurobiology to human task performance. Reproducing such insights across populations requires statistical learning techniques such as averaging and PCA across graphs without known node correspondences. We formulate this problem using the Gromov-Wasserstein (GW) distance and present a recently-developed Riemannian framework for GW-averaging and tangent PCA. Beyond graph adjacency matrices, this framework permits consuming derived network representations such as distance or kernel matrices, and such choices lead to additional structure on the GW problem that can be exploited for theoretical and computational advantages. We show how replacing the adjacency matrix representation with a spectral representation leads to theoretical guarantees allowing efficient use of the Riemannian framework as well as state of the art accuracy and runtime in graph learning tasks such as matching and partitioning.
4:15 PM EDT 317312 Density estimation and modeling on symmetric spaces
Speaker. Didong Li, Princeton University
Abstract. In many applications, data and/or parameters are supported on non-Euclidean spaces. It is important to take into account the geometric structure of manifolds in statistical analysis to avoid misleading results. In this talk, we consider a very broad class of manifolds: non-compact Riemannian symmetric spaces. For this class, we provide statistical models on the tangent space, push these models forward onto the manifold, and easily calculate induced distributions by Jacobians. To illustrate the statistical utility of this theoretical result, we provide a general method to construct distributions on symmetric spaces, including the log-Gaussian distribution as an analogue of the multivariate Gaussian distribution in Euclidean space. With these new kernels on symmetric spaces, any existing density estimation approach designed for Euclidean spaces can be applied, and pushed forward to the manifold with an easy-to-calculate adjustment. We provide theorems showing that the induced density estimators on the manifold inherit the statistical optimality properties of the parent Euclidean density estimator; this holds for both frequentist and Bayesian nonparametric methods.
4:35 PM EDT 317251 Convergence of persistence diagram in the subcritical regime
Speaker. Takashi Owada, Purdue University
4:55 PM EDT 317225 Combining geometric and topological information for boundary estimation
Speaker. Justin Strait, University of Georgia
Abstract. We propose a method which jointly incorporates geometric and topological information to estimate object boundaries in images, through use of a topological clustering-based method to assist initialization of the Bayesian active contour model. Active contour methods combine pixel clustering, boundary smoothness, and prior shape information to estimate object boundaries. These methods are known to be extremely sensitive to algorithm initialization, relying on the user to provide a reasonable initial boundary. This task is difficult for images featuring objects with complex topological structures, such as holes or multiple connected components. Our proposed method provides an interpretable, smart initialization in these settings, freeing up the user from potential pitfalls. We provide a detailed simulation study, and then demonstrate our method on artificial image datasets from computer vision, as well as real-world applications to skin lesion and neural cellular images, for which multiple topological features can be identified.
5:00 PM EDT Discussion and Floor-time
This event is a subsequent event from last year’s https://appliedtopology.org/tda-at-jsm/
Specifically this is a 3 year position (researcher of type B) that becomes permanent if the candidate obtains the national habilitation. Candidates with Ph.D. degrees from outside Italy must have it officially recognized by the italian public administration, or at least start the process applying for it.
Algebraic topologists, including Topological Data Analysis experts, are encouraged to apply. The application must be sent by registered mail with acknowledgement of receipt and follow a series of strict rules indicated in the application form, or by electronic certified mail (PEC) *only* if you are italian or resident in Italy.
We would like to draw your attention to the “Second Graduate Student Conference: Geometry and Topology meet Data Analysis and Machine Learning (GTDAML2021)” to be held online July 30 – August 1, 2021. This is the second edition of the conference that was first held at The Ohio State University in 2019. The goal of the conference is to bring together graduate students to share their work, interests, and presence in the flourishing research landscape connecting applications of Geometry and Topology to Data Analysis and Machine Learning. We aim to enhance discussion and collaboration via poster sessions, short presentations, and discussion panels. The program will include a special lecture by Professor Deanna Needell from the Department of Mathematics at UCLA. In addition, we will have a discussion panel on industry and research. Some of our confirmed panelists include: Professor Lorin Crawford (Microsoft Research and Brown University), Professor Marco Cuturi (Google Brain and CREST – ENSAE, Institut Polytechnique de Paris), and Jesse Zhang (PhD Stanford, Co-Founder at Beacons). Students may apply to give a 20 minute talk (through Zoom) or a poster presentation (through gather.town). Talks and posters do not have to be about the participants’ own research, and expository talks are also very welcome. Students are encouraged to apply to give a talk, but if a talk cannot be scheduled due to time limitations, students are invited to present a poster instead. We are expecting to schedule around 20 talks in total. Registration details can be found at https://gtdaml.wixsite.com/2021. The deadline for applying to give a talk is June 7.
Researchers working at the interface of TDA and the life sciences are warmly encouraged to submit an article to the special issue of Entropy on applications of topological data analysis in the life sciences, guest edited by Pablo Camara and Kathryn Hess. The submission deadline is 30 November 2021.