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: firstname.lastname@example.org or Head of Department Søren Højsgaard, phone: +45 9940 8801, e-mail: email@example.com
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.
Graz University of Technology offers two PhD positions in the Institute of Geometry. The research area is computational geometry and topology. Possible topics include topological data analysis, connections of discrete geometry and topology, design and analysis of algorithms in computational topology.
We offer a 4-year university assistant position (30 h/week) with a net salary of approximately 23,000 EUR per year. The starting date for both positions is Oct 1 2021.
We are looking for outstanding students with a master’s degree in Mathematics/Computer Science or a closely related field. Knowledge in at least one of the fields computational geometry or computational topology is a plus. Basic knowledge in algebraic topology, theoretical computer science, and programming skills, in particular in C++ or python, are also desirable.
The position includes teaching duties as teaching assistant, both for students of mathematics and others. The ability to teach in German is an advantage.
Applications should include: * a letter of application and motivation * a detailed curriculum vitae * a recent academic transcript * a copy of the master thesis (or a current draft) * a reference letter (for instance, by the Master thesis supervisor – can be sent separately) * email addresses of additional references (if applicable)
The Department of Mathematical Sciences at the University of Delaware invites applications for a postdoctoral researcher position beginning on or around July 1, 2021. The postdoctoral researcher will work with Dr. Chad Giusti to develop novel methods for understanding how brain networks encode and process information using techniques from topology, algebra, and geometry. The position carries a competitive salary and benefits, and offers interdisciplinary research and training opportunities in applied topology and neuroscience. Teaching opportunities are likely to be available but teaching is not required.
Applicants should have a PhD in mathematics or related discipline, with a strong background in algebraic or combinatorial topology. Programming experience or experience with data analysis are preferred. Background in neuroscience, biology, or applied topology are a plus, but are not required so long as the applicant is excited to learn. Some travel for conferences and collaborative research is expected. The position is renewable annually for three years.
The official ad is in the works and will be up on mathjobs shortly. Applications will be reviewed on a rolling basis and accepted until the position is filled. Applicants are encouraged to contact Chad Giusti (firstname.lastname@example.org) with questions.
Applied Facets of Geometry and Topology on April 22, 2021 —————————————
Time Zone: Berlin Time (Central European Summer Time, CEST)
* 04:00 pm to 04:40 pm, James A. Sethian (UC Berkeley / LBNL) * 04:50 pm to 05:30 pm, Lisbeth Fajstrup (Aalborg U) * 06:00 pm to 06:40 pm, Jacek Brodzki (U Southampton)
Thematic Day 2
Cellular Materials on May 6, 2021 ——————
Time Zone: Berlin Time (Central European Summer Time, CEST)
* 04:00 pm to 04:40 pm, Francisco Garcia-Moreno (Helmholtz-Zentrum Berlin / TU Berlin) * 04:50 pm to 05:30 pm, Emanuel (Menachem) Lazar (Bar-Ilan U) * 06:00 pm to 06:40 pm, Michael Klatt (Saarland U) * 06:50 pm to 07:30 pm, John M. Sullivan (TU Berlin)
Registered participants will receive the links for the semester by e-mail.
Upcoming Thematic Days: ———————-
Thematic Day 3, May 20, 2021, Stochastic Geometry and Materials Thematic Day 4, May 26, 2021, Topological Data Analysis Thematic Day 5, June 10, 2021, Algebraic Geometry and Framework Materials Thematic Day 6, June 24, 2021, t.b.a.
Myfanwy Evans (U Potsdam) Kathryn Hess Bellwald (EPFL) Frank Lutz (TU Berlin) Dmitriy Morozov (LBNL) Ileana Streinu (Smith College)
***** Call for papers – CGTA Special Issue on Algorithmic Aspects of Computational and Applied Topology *****
Computational Geometry is a forum for research in theoretical and applied aspects of computational geometry. The journal publishes fundamental research in all areas of the subject, as well as disseminating information on the applications, techniques, and use of computational geometry. Computational Geometry publishes articles on the design and analysis of geometric algorithms. All aspects of computational geometry are covered, including the numerical, graph theoretical and combinatorial aspects. Also welcomed are computational geometry solutions to fundamental problems arising in computer graphics, pattern recognition, robotics, image processing, CAD-CAM, VLSI design and geographical information systems.
This special issue is dedicated to original contributions in the general area of computational and applied topology. This includes, but is not limited to, progress on theory, algorithms and complexity analysis, implementation, and experimental study of problems in areas such as persistent homology, topological data analysis, low dimensional topology, homotopy theory, and topological combinatorics.
This special issue will be edited by Erin Wolf Chambers, Brittany Terese Fasy, and Clément Maria.
All submissions will be thoroughly evaluated in a single-blind peer-review process by at least two independent reviewers. The guest editors reserve the right to reject without review any submissions deemed to be outside the scope of the special issue. Authors are welcome to contact the special issue editors with questions about scope before preparing a submission.
Manuscripts submitted for this special issue should represent original, previously unpublished work or broader surveys which are original. At the time of submission, and for the entire review period, the paper (or essentially the same paper) should neither be under review by any conference with published proceedings nor by a scientific journal.
Multiple, redundant or concurrent publication: An author should not in general publish manuscripts describing essentially the same research in more than one journal or primary publication. Elsevier does not view the following uses of a work as prior publication: publication in the form of an abstract; publication as an academic thesis; publication as an electronic preprint.