The Centre is running a virtual seminar during the pandemic on Wednesdays, 12:15 am via Zoom. The Meeting-ID is 390 769 7241. Please contact Pawel Dlotko to obtain the passcode.

If you would like to give a talk as part of our seminar series please contact Pawel Dlotko.

If you are interested in research talks by members of the center, see below.

Upcoming seminars

  • 23/11/2022
    Rafał Topolnicki (Dioscuri TDA/ Wrocław): Topology-Driven Goodness-of-Fit Tests in Arbitrary Dimensions
    Abstract:In the talk a novel application of topology to the statistical hypotesis testing is presented. The method we developed adopts the Euler characteristic curve (ECC) to one- and two-sample goodness of fit tests. The presented tests, which we call TopoTest, work for samples in arbitrary dimension, having often higher power than the state of the art statistical tests. It is demonstrated that the type I error of TopoTests can be controlled and their type II error vanishes exponentially with increasing sample size. Results of extensive numerical simulations of TopoTest are shown to demonstrate its power. Joint work with Paweł Dłotko, Niklas Hellmer, Łukasz Stettner.
  • Past seminars

    • 09/11/2022
      Anna-Laura Sattelberger (MPI MIS/ KTH Stockholm): An Algebraic Invariant of Multiparameter Persistence Modules
      Abstract:In the one-parameter case, persistence modules naturally are graded modules over the univariate polynomial ring and hence perfectly understood from an algebraic point of view. By a classical structure theorem, one associates the so-called “barcode” to the module, from which one reads topological features of the data. Generalizing persistent homology to a multi-filtration and its homology allows for the extraction of finer information from data, but its algebraic properties are more subtle. In this talk, I discuss the shift-dimension, a stable invariant of multiparameter persistence modules which is obtained as the hierarchical stabilization of a zeroth total multigraded Betti number of the module. This talk is based on work with Wojciech Chachólski and René Corbet (arXiv preprint)
    • 26/10/2022
      Benjamin Br�ck (ETHZ): tba
    • 19/10/2022 3:15 pm
      Radmila Sazdanovic
    • 12/10/2022
      Aldi Hoxha: Simulation and measurements of a rotary inductive position sensor
      Abstract:PCB-based inductive position sensors (IPS) are a valid alternative in the measurement of the position in all those applications where the sensor has to work in harsh environmental conditions. The main components of the sensor are the stator, which contains two receivers and a transmitter, and the conductive target, which moves at a predefined air gap from the stator. Having established within the region where the moving conductor relies a known induced magnetic field, eddy currents are generated inside it, as the Faraday-Newmann law predicts. The receivers are able to encode this motion in electrical signals, thus providing the position of the target. The aim of the presentation is to show the working principle of the sensor, and how to solve numerically the eddy currents problem on the target in order predict the signals on the receivers.
    • 05/10/2022 3:15 pm
      Alexander D Smith: Data analysis via Topology and Geometry with Applications in Chemical Engineering
      Abstract:This talk is focused on the development and application of topological and geometrical methods for the analysis of chemical engineering data. Topology and geometry allow us to view data as a shape and provide us tools to quantify this shape. Abstracting data as a shape captures intrinsic characteristics of the data that are independent of the environment and methods used to obtain the data. These representations (e.g., graphs, manifolds, and point clouds) also provide means for integrating domain knowledge into data analysis that can strengthen connections between theory and experiment. We present a thorough review of applied topology and geometry methods in chemical engineering through applications on real datasets such as: molecular dynamics simulations, dynamical systems, process systems, and soft matter systems.
    • 21/09/2022
      Rob Scharein: Introducing KnotPlot (IMPAN R. 106)
      Abstract:The speaker will introduce his software KnotPlot, a tool for mathematicians, biologists, physicists and others to explore computational knot theory. A brief review of knot theory will be made and example applications will be shown. Also knots as artistic and cultural entities will be discussed.
    • 01/06/2022
      Matt Burfitt (Aberdeen): Topological approaches to the analysis of Fast Field-Cycling MRI images
      Abstract: Fast Field-Cycling MRI (FFC MRI) has the potential to recover new biomarkers form a range of diseases by scanning a number of low magnetic field strengths simultaneously. The images produced by an FFC scanner can be interpreted in the form of a sequence times series of 2-dimensional grey scale images with each times series corresponding to each of the magnetic field strengths. I will investigate the applications of topology and machine learning to brain stroke images obtained by the FFC MRI scanner. A main obstacle to achieving good results lies in multiplicative brightness errors occurring in the data. A simple solution might be to consider pixelwise image feature vectors initially only up to multiplication by a constant. This can be thought of as splitting the data point cloud within a product by first embedding into standard n-simplices. We observe that this point cloud embedding can provide good information on tissue types which when modelled against the other component of the data point cloud can be used to highlight stroke damaged tissue. A drawback of the first method is that it discarded the pixel spatial locations information of the image. However, this can be captured by persistent homology in a parameter choice free process. A direct comparison between pixel intensity histograms and the Betti curves reveals that homology captures and emphasises tissue signals. Moreover, addition geometric and topological information about the images is captured with persistent homology, in particular by considering changes between images at differing fields and times. From here the ultimately aim is to extract persistent homology features useful for machine leaning and develop new visual diagnostics for the FFC data.
    • 06/06/2022, 10-12 (!), tbc
      Yonatan Gutman: Optimal Embedding of Topological Dynamical Systems
      Abstract:Given a metrizable space $X$ of dimension $d$ it is a classical fact that the minimal $n$ which guarantees that $X$ may be embedded topologically in $[0,1]^n$, is $n=2d+1$.An analogous problem in the category of dynamical systems, is under what conditions one can guarantee that a topological dynamical system $(X,T)$ embeds in $([0,1]^n)^\mathbb{Z}$ under the shift action. This problem witnessed a rapid development in the last 20 years starting with Gromov's introduction of the invariant of mean dimension (1999) and the breakthrough results of Lindenstrauss and Weiss (2000). We will present our joint result with Tusukamoto concerning optimal embedding of minimal systems (2020) whose proof offers a surprising connection to signal analysis.
    • 25/05/2022
      Bernd Sturmfels (MPI MiS, Leipzig): Geometry of Dependency Equilibria
      Abstract:An n-person game is specified by n tensors of the same format. Its equilibria are points in that tensor space. Dependency equilibria satisfy linear constraints on conditional probabilities. These cut out the Spohn variety, named after the philosopher who introduced the concept. Nash equilibria are tensors of rank one. We discuss the real algebraic geometry of the Spohn variety and its payoff map, with emphasis on connections to oriented matroids and algebraic statistics.
    • 11/05/2022
      Justyna Signerska-Rynkowska: Embedding theorems and various notions of entropy in time-series analysis
      Abstract: I will start with presenting Takens� embedding theorem and its various generalizations. Next, I will discuss different notions of entropy commonly used in time-series analysis such as permutation entropy, sample entropy, approximation entropy, and directional entropy. We will see how these ideas can be applied for determinism testing and reconstruction of the dynamics of the underlying dynamical system.
    • 04/05/2022, 4:15 pm (!)
      Michał Lipiński: Morse pre-decomposition of an invariant set
      Abstract: An invariant set is a central object in the study of dynamical systems. We can reveal the internal structure of the set by decomposing it into gradient and recurrent parts. In particular, the recurrent parts (stationary points, periodic orbits, etc.) are sealed together with gradient-like trajectories. As a result, we get a Morse decomposition, a collection of isolated invariant sets (Morse sets) partially ordered by connecting trajectories. In this talk, I will present some results concerning the extension of the idea of Morse decomposition. The refined construction will allow studying the internal structure of recurrent parts of the decomposition. It is achieved by replacing the partial order with a preorder.
    • 27/04/2022
      Federica Buccino: Micro-cracks and lacunae in human bones: a debated mechanobiological link
      Abstract: The increase in fragility fractures and the related dramatic impact on the healthcare and financial system, raise a big red flag, especially in the approach to the clinical treatment. With the rise in life expectancy, the prevalence of chronic conditions, such as osteoporosis, is also set to escalate. All these contributing factors highlight the need and urgency to address the fragility fracture crisis with a critical eye on the intricate multi-scale arrangement of bone structure. Indeed, despite the evident health- and economic-related interests about bone fracture, its comprehension is still limited to macro-(50 cm-10 mm) and meso-scale (10 mm-500 �m) level, where its occurrence is however catastrophic. It is still unclear what befalls at the micro-scale (500 �m -1 �m) from a mechanobiological point of view and what is the role of microscopic bone porosities, named lacunae, during the damage processes. This is mainly due to the complexity of the parameters to be considered when dealing with bone fractures, such as bone�s structural and morphological features and their cross-relationships with damage initiation and propagation. More in depth, there exists a dualism behind the role of lacunae: they seem to play an antithetical mechanical contribution, having an effect on both strength and toughness. For these reasons, recent studies address the issue from a multi-scale perspective, elevating the micro-scale phenomena as the key for detecting early damage occurrence. However, several limitations arise specifically for defining a quantitative framework to assess the contribution of lacunar micro-pores to fracture initiation and propagation. Moreover, the need for high resolution imaging imposes time-demanding post-processing phases. To overcome these issues, we exploit synchrotron scans in combination with micro-mechanical tests, to offer a fracture mechanics-based approach for quantifying the critical stress intensification in healthy and osteoporotic trabecular human bones. This is paired with a morphological and densitometric framework for capturing lacunar network differences in presence of pathological alterations. To address the current time-consuming and computationally expensive manual/semi-automatic segmenting steps, we implement convolutional neural network to detect the initiation and propagation of micro-scale damages. The results highlight the intimate cross talks between toughening and weakening phenomena at micro-scale as a fundamental aspect for fracture prevention.
    • 20/04/2022
      Bartosz Naskręcki: Crystallographic tesselations and growth polynomials
      Abstract: We consider the Euclidean space R^N with a CW-tesselation which is transformed regularly by a crystallographic group. Such a group is naturally associated with a flat orbifold, obtained from the N-dimensional torus under the action of a certain finite group. I will explain the relation of this orbifold with the polynomials that count the number of cells in our tesselation treated as an expanding CW-complex. The natural choice of the vertex of the tesselation leads to growth functions which are polynomials of degree N for every fixed cell dimension. The leading terms of these growth polynomials form a vector which up to constant characterizes the flat tesselation orbifold. I will also explain the role of the lower order terms in these polynomials based on some calculations with fundamental domains of the crystallographic group. In practice, this leads to a simple concrete algorithm which allows one to recognize the crystallographic group from a partial data about tesselation.
    • 06/04/2022
      Daniel Wilczak: The method of covering relations in (in)finite dimension.
      Abstract: The method of covering relations [1,2] proved to be a very efficient topological tool in qualitative analysis of finite dimensional dynamical systems. It has been used in validation of the existence of various types of dynamics, including chaotic dynamics, connecting orbits, even some specific global bifurcations. In this talk I will show that the method can be adopted to a class of infinite dimensional problems. The main motivation for this research was the rigorous numerical analysis of dissipative PDEs. We have shown the existence of chaos [3] and infinite number of connecting orbits [4] between periodic solutions in one-dimensional Kuramoto-Shivashinsky PDE. Presented results are based on two articles [3,4] written with Piotr Zgliczyński.
      [1] P. Zgliczyński, Computer assisted proof of chaos in the Rossler equations and the Henon map, Nonlinearity {\bf 10} (1997), 243-252.
      [2] P. Zgliczyński, M. Gidea, Covering relations for multidimensional dynamical systems, J. Diff. Eq., 202/1(2004), 33--58.
      [3] D. Wilczak, P. Zgliczyński, A geometric method for infinite-dimensional chaos: symbolic dynamics for the Kuramoto-Sivashinsky PDE on the line, J. Diff. Eq., Vol. 269 No. 10 (2020), 8509-8548.
      [4] D. Wilczak, P. Zgliczyński, A rigorous C1-algorithm for integration of dissipative PDEs based on automatic differentiation and the Taylor method, in preparation.
    • 28/03/2022, 9 am Purdue = 3 pm Warsaw
      Tamal Dey (Purdue): Computing Generalized Rank Invariant for 2-parameter Persistence Modules via Zigzag Persistence
      Abstract: The notion of generalized rank invariant in the context of multiparameter persistence has become an important ingredient for defining interesting homological structures such as generalized persistence diagrams. Naturally, computing these rank invariants efficiently is a prelude to computing any of these derived structures efficiently. We show that the generalized rank over a finite interval I of a Z^2-indexed persistence module M is equal to the generalized rank of the zigzag module that is induced on a certain path in I tracing mostly its boundary. Hence, we can compute the generalized rank over I by computing the barcode of the zigzag module obtained by restricting the bifiltration inducing M to that path. If I has t points, this computation takes O(t^\omega) time where \omega\in[2,2.373) is the exponent of matrix multiplication. Among others, we apply this result to obtain an improved algorithm for the following problem. Given a bifiltration inducing a module M, determine whether M is interval decomposable and, if so, compute all intervals supporting its summands.
      Joint work with: Woojin Kim and Facundo Memoli
    • 18/03/2022, 10 am
      Witold Rudnicki: All-relevant feature selection in Machine Learning: methods and applications.
      Abstract: In the talk I will present short overview of general approaches to feature selection and explain the need for the all-relevant feature selection. I will give short motivation for the need of the feature selection methods that take into account synergistic interactions. Then I will describe two algorithms that I have proposed, Boruta and MDFS. Finally I will show some examples of real life applications and discuss caveats and limitations of these methods.
    • 14/03/2022, 8:30-10:30
      Joint workshop with Lodz University
    • 02/03/2022
      Davide Gurnari: ToMATo Clustering
      Abstract: Topological Mode Analysis Tool is a clustering algorithm that exploits topological persistence to guide the merging of candidate clusters. After a brief overview of the most famous clustering schemes, I will present the original paper by Chazal et al. and provide additional examples using current off-the-shelf implementations.
    • 23/02/2022
      Anastasios Stefanou (Uni Bremen): Multiparameter persistence meets Gr�bner bases.
      Abstract: Ordinary persistent homology extends from one parameter to multiparameter filtrations. The resulting algebraic methodology is called multiparameter persistent homology. Although multiparameter persistence modules of finite type admit a unique decomposition into indecomposables, the indecomposable modules do not always have the structure of a generalized interval module. Thus, to study these multigraded module structures one has to use alternative algebraic tools to present them. One such tool is called minimal free resolutions. A known result in homological algebra is that any $N^n$-graded module admits a unique minimal free resolution (up to a non-canonical notion of equivalence). Although these resolutions are not unique for the module in the standard sense, there are ways on which one can utilize the theory of Gr�bner bases to explicitly construct a minimal free resolution for the persistent module of a given multifiltration. The output minimal free resolution depends on (i) choosing a monomial order in the polynomial ring R:=K[x1,\ldots,x_n] (e.g. the lexicographic order) and (ii) on other things, e.g. choosing an ordering on the simplices of the input multi-filtered complex. In this talk I will give an introduction on Gr�bner bases over R^l, and discuss how Gr�bner bases can be useful in multiparameter persistence.
    • 16/02/2022
      Kunal Dutta (University of Warsaw): On Dimensionality Reduction for Persistent Homology
      Abstract: Given a point cloud in a high-dimensional space, we are interested in computing the persistent homology of the Cech filtration while avoiding the curse of dimensionality, and investigate the effectiveness of dimensionality reduction for this problem. This line of work was initiated by Sheehy [SoCG 2014] who showed that any map preserving pairwise distances between points up to a $(1\pm \varepsilon)$ factor, also preserves the Cech filtration up to a $(1 \pm 4\varepsilon)$ factor. We'll follow the work of Sheehy [2014] and Lotz [Proc. Roy. Soc., 2019], before arriving at the result of [Arya, Boissonnat, D., Lotz, J. App. Comp. Topol., 2021], which answers an open question of Sheehy regarding dimensionality reduction for persistent homology computed using the \emph{$k$-distance}, or \emph{distance to a measure}. We show that any linear transformation that preserves pairwise distances up to a $(1 \pm \varepsilon)$ multiplicative factor, must preserve the persistent homology of the Čech filtration, computed using the $k$-distance, up to a factor of $(1 - \varepsilon)^{-1}$. Our results also hold for the Vietoris-Rips and Delaunay filtrations for the $k$-distance, as well as the Čech filtration for the approximate $k$-distance of Buchet et al. [Comput. Geom., 2016]. We'll also look at several extensions of this result, for point sets (i) lying in a region of bounded Gaussian width or (ii) on a low-dimensional manifold, obtaining the target dimension bounds of Lotz [Proc. Roy. Soc., 2019] and Clarkson [Proc. SoCG, 2008 ] respectively. Joint work with S. Arya, J.-D. Boissonnat, and M. Lotz.
    • 19/01/2022
      Wojciech Chachólski (KTH Stockholm): Why should one care about metrics on (multi) persistent modules?
      Abstract: What do we use metrics on persistent modules for? Is it only to asure stability of some constructions? In my talk I will describe why I care about such metrics, show how to construct a rich space of them and illustrate how to use them for analysis.
    • 05/01/2022 and 14(!)/01/2022
      Justyna Signerska-Rynkowska: Entropy and entropy-like quantities in time series analysis
      Abstract: In the first part of the talk I will present the concepts of topological and metric entropy in dynamical systems theory and relations between them. Having introduced the mathematical background, we will explore these notions and their various counterparts applied to time series analysis, namely approximate entropy, sample entropy, permutation entropy and transfer entropy used, among others, in heart-rate variability analysis, EEG research, cluster analysis or testing determinism.
    • 08/12/2021 and 15/12/2022
      Michał Lipiński: An introduction to the selected topics on analysis of a time series.
      Abstract: During this working seminar I will present some aspects, classic ideas and approaches in analysis of a time series. The talk is based on Nonlinear Time Series Analysis handbook by H.Kantz and T.Schreiber (2004).
    • 01/12/2021
      Workshop on TDA and Medical Data in Gdansk, no seminar
    • 24/11/2021
      Niklas Hellmer: Bottleneck Profiles and Prokhorov Metrics for Persistence Modules
      Abstract: In TDA, Bottleneck and interleaving distances and their variants are common tools to compare persistence diagrams or modules. However, they only capture the single, most extreme difference. We introduce bottleneck profiles, a way to study all the differences at varying scales. Algebraically speaking, this construction can be thought of measuring how far two persistence modules are from being interleaved. In addition, one easily obtains a rich family of (extended pseudo)metrics for persistence modules, mimicking the classical Prokhorov metric from probability theory. As an application, we describe proxies for the Wasserstein distance, which allow for fast computations.
    • 18/11/2021, 4 pm (note the unusual time and date)
      Simon Rudkin (Swansea): Hidden in Plain Sight: The Real Messages of Economic Data
      Abstract: Recognising the powerful messages that may be drawn from the shape of data, we ask how inference on well-studied datasets in economics and finance may be enriched through topological data analysis. Examples considered include the application of ball mapper algorithms in credit scoring, regional growth trajectories, analysis of stock returns and to the political economy of Brexit. In each case we demonstrate how anomaly phenomenon are better understood when viewed through the multidimensional lens; policy and practice then being more effectively directed as a consequence. Discussing the ways in which developments of theory may support research agendas across economics and finance, we signpost where the tools of TDA themselves can be developed to embolden the synergies between disciplines. Consequentially, we signpost an exciting research agenda that further reveals the information that an inability to meaningfully capture the multidimensional nature of data had veiled for so long.
    • 03/11/2021, 2:30 pm
      Wolfram Bosbach: TDA in medical engineering
      Abstract:Medical research can greatly benefit from advanced mathematical methods. There are patterns of higher order in human anatomy which are not intuitively recognisable by the human eye. Trabecular bone, neuronal networks in the brain, or lung bronchi exhibit interconnectivities which allow analyses about their physiological or pathological state. Numerical finite elements can extend pattern analyses and give insights into mechanical tissue behaviour. Radiology is the medical field which can provide 3-dimensional imaging of anatomical body parts. Funded by the DFG, we are now working towards applying topological data analysis (TDA) to existing and new classification problems.
      Funding: Deutsche Forschungsgemeinschaft (DFG) grant no-BO-4961/11
    • 03/11/2021
      Davide Gurnari: Distributed algorithms for Euler Characteristic Curves
      Abstract: The Euler characteristic of a simplicial complex is the alternate sum of its Betti number, or equivalently the alternating sum of the number of simplices of following dimensions. For a filtered complex the Euler Characteristic Curve is a function that assigns an Euler number to each filtration level. ECCs are closely related to persistence diagrams via the fundamental theorem of persistent homology and stability with respect to the 1-Wasserstein distance can be proven under certain conditions. The first part of this talk we will introduce the concept of ECC and prove its stability with respect to the 1-Wasserstein distance. In the second part we will present new techniques to compute the ECC of filtered Vietoris-Rips complexes. By following a distributed approach, the contributions to the ECC can be computed locally without having to explicitly build up the whole complex. This allows us to significatively reduce both time and memory requirements, giving us the opportunity to tackle much larger datasets compared to, for instance, persistent homology. An implementation of such algorithms is available as a scikit-learn-compatible Python package. Finally, we will discuss how such ideas and algorithms can be adapted to work in the multiparameter persistence setting.
    • 27/10/2021
      Michał Lipiński: Morse-Conley-Forman theory for generalized combinatorial multivector fields on finite topological spaces
      Abstract: In the talk, I will present a generalization of the theory of multivector fields which can be seen as a combinatorial counterpart of vector fields induced by continuous dynamical systems. The theory has its roots in the theory of combinatorial vector fields by Robin Forman and was later extended by Marian Mrozek to multivector fields for Lefschetz complexes. Our generalization involves three fundamental changes, which will be explained during the talk. Consequently, the theory becomes more flexible and handy in applications. Moreover, we can introduce a new interpretation of a multivector as a dynamical "black box." With the new settings, we define combinatorial counterparts of multiple objects from the classical theory of dynamical systems, among others: isolated invariant set, index pair, Conley index, limit set, attractor, or Morse decomposition. We also show that the desirable properties as additivity of a Conley index and Morse inequalities hold. Furthermore, we use persistent homology to study the robustness of the structure of Morse sets. In particular, we construct a zigzag persistence module for Morse decomposition for multivector fields. Finally, we will present some numerical experiments based on the presented theory.
    • 20/10/2021
      Jan F. Senge: Topological descriptors for surface roughness of shot peened surfaces
      Abstract: Surface topography and roughness evaluation plays an important role in many problems such as friction, contact deformation or coat adhesion. Impact surface treatments can be used to improve the properties of the treated surface in the cases mentioned. Different roughness parameters like the arithmetic average height deviation, root mean square height or skewness are used to describe the topography of a surface and constitute the most used features to describe changes occurred during impact treatment. Depending on the surface treatment process considered, standard roughness parameters may struggle to exhibit differences between surface samples of the same material at different stages of the process. As an extension the work done to improve existing methods and find new descriptors, we consider methods from computational topology, namely persistent homology to capture meaningful geometric features of the surface and use that to construct new surface roughness parameters better able to distinguish surface samples. Furthermore, invariants such as the persistence diagram have been studied extensively in the last years and provide a rich structure which can be exploited using a plethora of different machine learning techniques. In the talk, I give an introduction into existing standards for surface roughness measurement and compare how well persistence-based parameters perform for classification and clustering tasks in the case of the shot peening surface treatment process opposed to conventional roughness parameters mentioned in these standards.
    • 13/10/2021, 5 pm
      Anastasios Stefanou: Persistent Homology of Phylogenetic Networks
      Abstract: We devise a hierarchical representation of phylogenetic networks over a set of taxa X, called the cliquegram, which generalizes the dendrogram representation of phylogenetic tree ultrametrics. We show that cliquegrams form a lattice. Furthermore, we show that the cliquegram lattice model is a faithful model for solving the Phylogenetic Network Reconstruction Problem (PNRP): Given a set of trees there is a unique minimal network that contains them. Moreover, we develop a zigzag persistent homology theory for cliquegrams relative to a choice of a taxon x in X, called taxa-decorated persistent homology. Generalizations of these ideas (i.e. the cluttergram) from the setting of networks to the setting of filtrations are also developed. The algorithms implementing those constructions are discussed and their implementations are provided in Python.
    • 02/12/2020
      Artem Dudko: On computability and computational complexity of Julia sets.
      Abstract: In this talk I will give an introduction to computability and computational complexity of sets with the focus on Julia sets of polynomials. For a polynomial p(z) its Julia set is the set of points on a complex plane near which the iterations of p(z) behave chaotically. Roughly speaking, a set is called computable if there is an algorithm which can produce an approximation of this set (by a finite set of pixels) with any given precision. Computational complexity measures how hard it is to produce such approximations. In the talk I will show that already in the class of Julia sets of quadratic polynomials there are various interesting computational phenomena, including:
      - large classes of functions having Julia sets of low (polynomial) computational complexity;
      - functions with non-computable Julia sets;
      - large classes of functions having Julia sets of arbitrarily high computational complexity.
    • 25/11/2020
      John Harvey (Swansea University): Estimation of dimension
      Abstract:I will survey various established and new techniques to estimate the dimension of a submanifold of Euclidean space from a sampled point cloud, how we might investigate underlying assumptions of the estimators, and how they relate to testing for manifoldness
    • 18/11/2020
      Pawel Dlotko (Dioscuri TDA, Warsaw): Mild introduction to TDA
      Abstract: In this talk I will provide a general introduction to methods of TDA - concentrated mostly on persistent homology and mapper algorithm. I will present the basic theory, highlight the main application. But, I will mostly encourage questions and discussion.
    • 11/11/2020
      No seminar due to public holiday!
    • 04/11/2020
      Niklas Hellmer (Dioscuri TDA, Warsaw): Nonembeddability of Persistence Diagrams with p>2 Wasserstein Metric.
      Abstract: To apply kernel methods on persistence diagrams, they are mapped into a Hilbert space. The question is how much information we lose by doing so. I present recent work by Alexander Wagner that shows that persistence diagrams with p-Wasserstein metric do not admit a coarse embedding into a Hilbert space for p>2.

    Talks of center members in other seminars

    • 26/02/2021
      Niklas Hellmer: "Discrete Prokhorov Metric for Persistence Diagrams" at APATG.