
Mathematics of Data Analysis  Oberseminar
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 13.03.2018: The seminar in the summer semester will again take place on Monday afternoon from 14:00 to 15:30, in room 03.10.011.
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Google calendar of the Oberseminar
Monday, 30.07.2018 
14:0015:30 
Speaker: 
Marita Thomas (WIAS Berlin) 
Title: 

Room: 
03.10.011 
Monday, 11.06.2018 
14:0015:30 
Speaker: 
Dominik Nagel (University Osnabrück) 
Title: 

Room: 
03.10.011 
Thursday, 26.04.2018 
10:1511:45 
Speaker: 
Bosu Choi ((Michigan State University) 
Title: 
The development of sublineartime compressive sensing methods for signals which are sparse in Tensorized Bases of Bounded Orthonormal Functions (TBBOFs) will be discussed. These new methods are obtained from CoSaMP by replacing its usual support identification procedure with a new faster one inspired by fast Sparse Fourier Transform (SFT) techniques. The resulting sublinearized CoSaMP method allows for the rapid approximation of TBBOFsparse functions of many variables. Both numerics and theoretical recovery guarantees will be presented. 
Room: 
02.08.011 
Monday, 23.04.2018 
14:0015:30 
Speaker: 
Bernd Sturmfels (MPI Leipzig) 
Title: 
This lecture discusses the role of algebraic geometry in data science. We report on recent work with Paul Breiding, Sara Kalisnik and Madeline Weinstein. We seek to determine a real algebraic variety from a fixed finite subset of points. Existing methods are studied and new methods are developed. Our focus lies on topological and algebraic features, such as dimension and defining polynomials. All algorithms are tested on a range of datasets and made available in a Julia package. 
Room: 
03.10.011 
Monday, 16.04.2018 
14:0015:30 
Speaker: 
Ayush Bhandari (MIT) 
Title: 
The ability to resolve overlapping echoes or pulses is an indefensible art that finds applications across several areas of science and engineering. This problem boils down to recovery of a sparse signal given its lowpass projections. In the recent years, a number of solutions have been proposed to solve this problem and variations on the theme include convex programing approaches, atomic norm minimization and high resolution frequency estimation.
That said, in many practical cases of interest, the pulse may be distorted due to physical properties of propagation and transmission. Such cases can not be handled well by existing signal models. Inspired by problems in spectroscopy, radar, photoacoustic imaging and ultrawide band arrays, on which we base our case studies, in this work we take a step towards recovering continuoustime spikes from timevarying pulses. To this end, we repurpose existing superresolution approach and extend its utility to the case of distorted pulses by developing the idea of steerable pulses. Application of our algorithm on the abovementioned case studies results in substantial improvement in peaksignaltonoise ratio, thus promising interesting future directions. Finally, we show how this approach can be used to infer geometry of red blood cells from photoacoustic tomography which was previously not possible in literature. 
Room: 
03.10.011 
Tuesday, 27.02.2018 
11:3013:00 
Speaker: 
Maik Kahnt (DESY) 
Title: 
Xray ptychography is the newest and most promising microscopy technique at synchrotron sources. The resolution is neither limited by fabrication errors of the optics, the size of the sample nor the beam size on the sample. This talk focuses on the ptychographic phase retrieval algorithm and the challenges when it is applied to real experimental data. 
Room: 
02.08.020 
Monday, 22.01.2018 
14:0015:30 
Speaker: 
Manuel Stein ^{} (Vrije Universiteit Brussel) 
Title: 
The talk focuses on signal processing with measurements obtained by an array of sensors which feature a lowresolution digitization process. Employing a large number of antennas in conjunction with lowcomplexity analogtodigital conversion is motivated via the technical requirements of exemplary future wireless systems. We outline the challenges associated with statistical processing and analysis under such a system architecture. Reducing the intractable probabilistic models arising under hardlimiting within the exponential family, we then conservatively approximate established information measures connected to signal processing performance. Finally, the presented methods are exploited to study the favorable design of highperformance wireless sensor systems with low hardware complexity. 
Room: 
02.08.020 
Monday, 15.01.2018 
14:0015:30 
Speaker: 
Johannes Maly, Sara KrauseSolberg (TUM) 
Title: 
TBA 
Room: 
02.08.020 
Thursday, 11.01.2018 
11:0012:00 
Speaker: 
YoungPil Choi ^{} (Inha University, South Korea) 
Title: 
In this talk, we discuss an analytical framework for investigating the efficiency of a consensusbased model for tackling global optimization problems. We study the optimization algorithm in the meanfield sense showing the convergence to the global minimizer for a large class of functions. 
Room: 
00.10.011 
Thursday, 11.01.2018 
10:0011:00 
Speaker: 
Philippe Sünnen 
Title: 
In this talk I will sketch the proof of a uniqueness theorem on a class of neural nets. The important requirements of the proof are that the neural nets satisfy the so called generic conditions and that the activation function of the output function is given by the hyperbolic tangent. Under these requirements it can be shown that two neural nets with the same output function are isomorphic, which means that the depths of the nets are equal and that the weights and the thresholds are equal up to permutations and changes in sign. The basic idea is to continue the output function to an open subset of the complex plane and read off the information to reconstruct the net from the set of singularities. 
Room: 
00.10.011 
Monday, 08.01.2018 
14:0015:30 
Speaker: 
Florian Boßmann ^{} (Universität Passau) 
Title: 
Structural reconstruction using quadratic sparsity penalisation 
Room: 
02.08.020 
Monday, 18.12.2017 
14:0015:30 
Speaker: 
Andreas Langer ^{} (Universität Stuttgart) 
Title: 
In image reconstruction one often minimizes a nonsmooth functional consisting of one or two data fidelity terms, a regularization term, and parameters, which balance the aforementioned terms. The proper choice of the parameters is delicate. In fact, badly chosen weights either may not only remove noise but also details in images, or retain noise in homogeneous regions. Hence a good reconstruction may be obtained by choosing the parameters such that a good compromise of the aforementioned effects are made. We revisit the disrcepancy principle and demonstrate how it can be used for finding parameters in functionals consisting of one and two data terms. However, since images consist of multiple objects of different scales, it is expected that spatially varying weights would give better reconstructions than a scalar parameter. In this vein we adapte our proposed algorithm for computing distributed weights. We study the convergence behaviour of the proposed algorithms and present several numerical experiments for image reconstruction. 
Room: 
02.08.020 
Monday, 11.12.2017 
14:0015:30 
Speaker: 
Jeff Hogan ^{} (The University of Newcastle, Australia) 
Title: 
A family of realvalued, regular, compactly supported, orthonormal, multiresolution wavelets on the line was produced by Ingrid Daubechies in 1988. Several of the techniques used by Daubechies, including spectral factorization, are unavailable in higher dimensions. In work with David Franklin (Newcastle) and Matthew Tam (Goettingen) the application of techniques such as iterated projections, the DouglasRachford algorithm, and PALM (Proximal Alternating Linearized Minimization) to the construction of (nontensorial) multidimensional wavelets has been investigated. I'll report on the progress of this project and discuss several extensions we hope to address in future work. 
Room: 
02.08.020 
Monday, 27.11.2017 
14:0015:30 
Speaker: 
Prof. W. R. Madych ^{} (University of Connecticut, USA) 
Title: 
Spline summability of cardinal sine series and the Bernstein class B_{\pi} 
Room: 
02.08.020 
Friday, 18.08.2017 
11:0012:00 
Speaker: 
Andreas Bluhm, Daniel Stilck Franca (TUM) 
Title: 
We show how to sketch semidefinite programs (SDPs) using positive maps in order to reduce their dimension. More precisely, we use JohnsonLindenstrauss transforms to produce a smaller SDP whose solution preserves feasibility or approximates the value of the original problem with high probability. These techniques allow to improve both complexity and storage space requirements. They apply to problems in which the Schatten 1norm of the matrices specifying the SDP and of a solution to the problem is constant in the problem size. Furthermore, we provide some nogo results which clarify the limitations of positive, linear sketches in this setting. Finally, we discuss numerical examples to benchmark our methods. 
Room: 
02.08.011 
Tuesday, 18.07.2017 
14:3016:00 
Speaker: 
Prof. Uwe Kähler ^{} (Universidad de Aveiro, Portugal) 
Title: 
In the last two decades one can observe an increased interest in the analysis of discrete structures. One one hand the fact that increased computational power is nowadays available to everybody and that computers can essentially work only with discrete values sparked an increased interest in working with discrete structures. This is true even for persons who are originally unrelated to the field. An outstanding example can be seen in the change of the philosophy of the Finite Element Method. From the classical point of view of being essentially a method for discretization of partial differential equations via a variational formulation the modern approach lifts the problem and, therefore, the finite element modelation directly on to the mesh, resulting in the socalled Finite Element Exterior Calculus. This means that one requires discrete structures which are equivalent to the usual continuous structures. On the other hand, the increased computational power also means that problems in physics which are traditionally modeled by means of continuous analysis are more and more directly studied on the discrete level, the principal example being the Ising model from statistical physics as opposed to the continuous Heisenberg model which has been studied by S. Smirnov and his collaborators using discrete complex analysis. Unfortunately, a higher dimensional analogue of discrete function theories is only in its infancy. In this talk we will present two principal approaches: the classic one based on finite differences as well as a more general version called script geometry. Furthermore, we will present the basic ingredients of a function theory, such as Fischer decomposition and power series as well as discuss potentialtheoretical arguments like discrete Cauchy kernels, discrete Hilbert/Riesztransforms and Hardy spaces. Among possible applications we are going to discuss discrete Riemann boundary value problems and their importance for image processing. 
Room: 
02.08.020 
Thursday, 13.07.2017 
10:3012:00 
Speaker: 
Prof. HansGeorg Feichtinger ^{} (Universität Wien) 
Title: 
The talk describes a surprisingly rich family of function spaces which can be defined on general LCA (locally compact Abelian groups, such as G = R^d). The start point is the Banach Gelfand triple (SO,L2,SO’), consisting of the Segal algebra SO(G) as a space of test functions and the dual space as the minimal resp. maximal space in this family. One of the most attractive (and surprising) facts about this setting, which requires only the use of Banach spaces and their dual spaces, is the existence of a kernel theorem, which extends the classical association of L2kernels with the family of HilbertSchmidt operators. As time permits a number of questions arising from classical analysis and timefrequency analysis resp. Gabor analysis are mentioned. 
Room: 
02.08.020 
Tuesday, 11.07.2017 
14:3015:30 
Speaker: 
Kiryung Lee (GeorgiaTech) 
Title: 
The restricted isometry property (RIP) has been an integral tool in the analysis of various inverse problems with sparsity models. We propose generalized notions of sparsity and provide a unified framework on the RIP for structured measurements, in particular when combined with isotropic group actions. Our results extend the RIP for partial Fourier measurements by Rudelson and Vershynin to a much broader context and provide upper bounds on the number of group structured measurements for the RIP on generalized sparsity models. We illustrate the main results with an infinite dimensional example, where the sparsity represented by a smoothness condition approximates the total variation. We also discuss fast dimensionality reduction on generalized sparsity models. In generalizing models, the sparsity parameter becomes no longer subadditive. Therefore, the RIP does not preserve distances among sparse vectors. We show a weaker version with additive distortion, which is similar to analogous property in 1bit compressed sensing. This is a joint work with Marius Junge. 
Room: 
02.08.020 
Thursday, 29.06.2017 
11:0012:00 
Speaker: 
Prof. Ahmed I. Zayed ^{} ( DePaul University, Chicago) 
Title: 
In this talk we present the solution of the energy concentration problem for the Fourier transform that was proposed by D. Slepian, H.Landau, and H. Pollak of Bell Labs in the 1960s, and then investigate the solution of a similar problem for the special affine Fourier transformation. 
Room: 
02.08.020 
Tuesday, 21.06.2017 
10:3011:30 
Speaker: 
Timo Klock ^{} (Simula Research Laboratory) 
Title: 
Inverse problems of unmixing type arise in many reallife applications such as audio processing or medical image analysis. In such problems additive noise directly affects a sparse signal before being measured through a sampling matrix. Consequently, the noise in the measurement is amplified through the sampling process and the socalled noise folding phenomenon occurs. This amplification worsens the results on support identification by means of "classical'' sparse recovery techniques based on the l1penalised Lasso functional. Several recent works suggest to apply a multipenalty framework for a correct modeling and separation of the original signal in such problems. Admittedly, the parameter choice in such multipenalty functionals becomes more involved compared other singlepenalty methods. In this talk, we use multipenalty regularization for the unmixing problem. It has been shown that the resulting functional can be seen as a parameterised Lasso such that we can solve the multipenalty minimisation with known techniques for the Lasso. In this spirit, we provide an extension of the Lassopath algorithm for an efficient calculation of large parts of the multipenalty solution space without performing extensive gridsearches over regularisation parameters. Finally, by using a naive support selection heuristic based on signaltonoise ratios, we identify a unique support that is compared to results from conventional sparse recovery techniques. Such experiments confirm improved results of the multipenalty functional compared to singlepenalty counterparts. 
Room: 
TBA 
Tuesday, 06.06.2017 
14:3016:00 
Speaker: 
Prof. Stephan Günnemann (TUM) 
Title: 
Efficient and Robust Learning with Graphs 
Room: 
02.08.020 
Tuesday, 30.05.2017 
14:3016:00 
Speaker: 
Prof. Felix Krahmer (TUM) 
Title: 
In this talk, we will discuss random models with varying degrees of imposed structure for different applications in signal processing and data analysis. First, we will study a matrix factorization problem as motivated by applications in bioinformatics. To establish uniqueness under a random model, we develop new tools in probabilistic combinatorics. Motivated by applications in wireless communication, we consider the problem of simultaneous demixing and deconvolution for randomly embedded signals. We improve upon recent results by Ling and Strohmer, establishing for the first time nearoptimal parameter dependence. Lastly, we show nearoptimal recovery guarantees for analogtodigital conversion in combination with compressed sensing for structured random measurement systems. These are joint works with the speaker’s PhD students David James, Dominik Stöger, and JoeMei Feng as well as with Matthias Hein (Universität des Saarlandes), Peter Jung (TU Berlin), and Rayan Saab (UC San Diego). 
Room: 
02.06.011 
Thursday, 18.05.2017 
09:0010:00 
Speaker: 
Matthias Beckmann ^{} (Universität Hamburg) 
Title: 
This talk concerns the approximation of bivariate functions by using the well established filtered back projection (FBP) formula from computerized tomography, which allows us to reconstruct a bivariate function from given Radon data. Our aim is to analyse the inherent FBP approximation error which is incurred by the application of a lowpass filter. To this end, we present error estimates in Sobolev spaces of fractional order. The obtained error bounds depend on the bandwidth of the utilized filter, on the flatness of the filter’s window function at the origin, on the smoothness of the target function, and on the order of the considered Sobolev norm. Finally, we prove convergence for the approximate FBP reconstruction in the treated Sobolev norms along with asymptotic convergence rates, as the filter’s bandwidth goes to infinity. The theoretical results are supported by numerical experiments. This talk is based on joint work with Armin Iske. 
Room: 
02.08.011 
Tuesday, 16.05.2017 
14:3016:00 
Speaker: 
Benedikt Diederichs (Universität Hamburg) 
Title: 
Prony's problem  estimating the frequencies of an exponential sum  and its higher dimensional analogs have attracted a lot of attention in recent years. A somewhat neglected question is whether this problem is wellposed. In this talk, some results in this direction will be presented. The most important techniques we need are e cient estimates of certain exponential sums. Inci dentally, they can be used to improve classic estimates of the condition numbers of matrices arising when one interpolates with a positive definite kernel. If time permits, we will discuss this connection. This talk is based on joint work with Armin Iske. 
Room: 
02.08.020 
Tuesday, 09.05.2017 
14:3016:00 
Speaker: 
Markus Stachl (TUM) 
Title: 
The flow of motorized vehicles through urban road networks is known to be one of the main reasons for high pollution in metropolitan areas. So far little scientific research has been spent on the effects of coordinated traffic lights on emissions. In our approach to simulate traffic flow through a network of roads we resort to a wellposed macroscopic conservation law coupled with a onedimensional pollution model. Model Predictive Control (MPC) is used as a responsive optimization technique to manage the movement of cars close to junctions, mirroring the use of traffic signals. On basis of an exemplaric road network in Munich we show that by optimizing traffic dynamics in this manner a decrease in COemissions by 510% can be achieved. 
Room: 
02.08.020 
Tuesday, 02.05.2017 
14:3016:00 
Speaker: 
Frank Filbir ^{} (Helmholtz Zentrum München) 
Title: 
We develop constructive algorithms to represent functions defined on a metric measure space within a prescribed accuracy. The constructions can be based on either spectral information or scattered samples of the target function. Our algorithmic scheme is asymptotically optimal in the sense of nonlinear nwidths and asymptotically optimal up to a logarithmic factor with respect to the metric entropy. The talk is based on joint work with Martin Ehler, University of Vienna. 
Room: 
02.08.020 
Tuesday, 04.04.2017 
10:3011:30 
Speaker: 
Thomas Fink ^{} (Universität Passau) 
Title: 
In various fields of image analysis, determining the precise geometry of occurent edges, e.g. the contour of an object, is a crucial task. Especially the curvature of an edge is of great practical relevance. In this talk, we introduce an extension of the continuous shearlet transform which additionally utilizes shears of higher order. This extension, called the Taylorlet transform, allows for a detection of the position and orientation, as well as the curvature and other higher order geometric information of edges. Furthermore, we introduce novel vanishing moment conditions of the form \( \int_{\mathbb{R}} g \left( \pm t^k \right) t^m dt \). We will show that Taylorlets fulfilling such conditions enable a more robust detection of the geometric edge features. 
Room: 
02.10.011 
Tuesday, 28.03.2017 
10:3011:30 
Speaker: 
Dr. Ilaria Lucardesi ^{} (École des Mines de Nancy) 
Title: 
The two most studied elliptic PDEs are probably the torsion problem, also known as StVenant problem, and the Dirichlet eigenvalue problem. For these classical problems, many estimates and qualitative properties have been obtained, see for example works by P\'olya, Szeg\"o, Schiffer, Payne, Hersch, Bandle, and many others. In this seminar I present some recent results about upper and lower bounds of two shape functionals involving the maximum of the torsion function: I consider the ratio $T(Omega)\lambda_1(Omega) Omega$ and the product $M(Omega)\lambda_1(Omega)$, where $Omega$ is bounded open set with finite Lebesgue measure $Omega$, $T(\Omega)$ denotes the torsion, and $\lambda_1(\Omega)$ the first Dirichlet eigenvalue. Particular attention is devoted to the subclass of convex sets. 
Room: 
02.12.020 
Wednesday, 01.03.2017 
11:0012:00 
Speaker: 
Patrick van Meurs ^{} (Kanazawa University) 
Title: 
The starting point is a 2D model for the dynamics of n dislocations, which are modelled as point particles with a positive or negative ’charge’. In the celebrated engineering paper by Groma and Balogh in 1999, the limit passage n → ∞ of these dislocation dynamics is performed in a statistical mechanics framework, which relies on a phenomenological closure assumption. In my talk, I present how to pass rigorously to the limit n → ∞ by using the theory of Wasserstein gradient flows and using advanced functional analysis on the weak form of the evolution equation. Interestingly, our conclusion for the limiting dynamics of the dislocation density differs from the conclusion in the paper by Groma and Balogh. 
Room: 
02.08.011 
Tuesday, 07.02.2017 
16:0017:00 
Speaker: 
Kiryung Lee ^{} (Georgia Tech) 
Title: 
Multichannel blind deconvolution resolves unknown input signal from multiple channel outputs with unknown impulse responses. It is often easier to estimate only the unknown channel impulse responses when they are modeled with only few parameters. This is the case with channel estimation problems in wireless communications and underwater acoustics. In particular with FIR models, various reconstruction methods based on statistics of the input signal and/or the commutativity of the convolution operator have been proposed with algebraic performance guarantees in 1990s. However, these guarantees are restricted to the case where the observations are noisefree or the input has infinite length. In fact, with finitely many noisy observations, the empirical performance of these classical methods deteriorates dramatically as the length of observation decreases. This weakness restricts their utility in estimating timevarying channels. Motivated from the observation that parametric models with few parameters can be embedded into lowdimensional subspaces, we propose modifications on the classical methods leveraging these subspace models. Our proposed method provides significant improvement over the original method in terms of providing stable estimates from finitely many noisy measurements. Furthermore, using recently developed tools in applied probability, we derive rigorous performance guarantees under certain random subspace models. We verify that the numerical results are aligned with our theory through Monte Carlo simulations. Furthermore, we also verify the empirical success in a realistic setup of channel delay estimation. Finally, we extend the methods and theory to the sparsity case and present corresponding theoretic and numerical results. This is the joint work with Felix Krahmer and Justin Romberg. 
Room: 
02.12.020 
Tuesday, 31.01.2017 
16:0017:00 
Speaker: 
Stefano Almi (TUM) 
Title: 
The aim of this talk is to present a variational model of the quasistatic evolution of hydraulic crack in the general framework of rateindependent processes. 
Room: 
02.06.020 
Tuesday, 24.01.2017 
16:0017:30 
Speaker: 
Marco Morandotti ^{} (TUM) 
Title: 
In this talk I will present the theory of structured deformations. Starting from the original formulation by Del Piero and Owen, I will move to the variational formulation proposed by Choksi and Fonseca, to conclude with some recent results regarding relaxation of nonconvex energies. I will also present some examples and explicit formulas as an application to specific surface energies. 
Room: 
02.06.020 
Tuesday, 17.01.2017 
10:3012:00 
Speaker: 
Giacomo Albi ^{} (TUM) 
Title: 
In this talk I will review some basic results in the modeling and control of multiagent system. The prototype problem will account a large system of interacting agents, whose dynamics are influenced by a policy maker acting in order to enforce a desired behavior. Different examples will be shown: from opinionformation processes to crowdsafety management. From the mathematical viewpoint, this situation can be described by means of a meanfield optimal control problem, governing the dynamics of the probability distribution of the agent population. In order to deal numerically with the highdimensionality and the nonlinearities of this type of problems, I will introduce a novel approximating hierarchy of suboptimal controls based on a stochasticBoltzmann approach, whose computation requires a moderate computational effort compared with standard direct approaches. I will compare the behavior of the control hierarchy with respect to the solution of the optimal control problem. Several numerical examples will show the effectiveness of the proposed strategies. 
Room: 
02.10.011 
Tuesday, 10.01.2017 
10:3012:00 
Speaker: 
Prof. Dr. Sergei Pereverzyev ^{} (RICAM Linz) 
Title: 
We discuss the parameter choice in learning algorithms generated by general regularization scheme. In contrast to classical deterministic regularization, the performance of regularized learning algorithms is influenced not only by the smoothness of a target function, but also by the capacity of a regularization space. In supervised learning both the smoothness and the capacity are intrinsically unknown. Therefore, we are interested in a posteriori regularization parameter choice rules and propose a new form of the balancing principle. We provide the analysis of the proposed rule and demonstrate its advantages in simulations. Joint research with Peter Mathe (WIASBerlin) and Shuai Lu (Fudan University, Shanghai). 
Room: 
02.10.011 
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