Eindhoven SPOR Seminar

This biweekly seminar invites excellent speakers from around the world to speak at the SPOR cluster at the department of Mathematics and Computer Science of Eindhoven University of Technology.  The talks concern all realms of stochastics, algorithmics, and discrete mathematics (e.g., probability theory, statistics, operations research, and combinatorial optimization). Emphasis is placed on the context, the motivation, and the main ideas behind the presented results. The audience ranges from senior researchers to first year Ph.D. students. 

The seminar is organized by Aida Abiad Monge, Elisa Perrone, Jaron Sanders, Neeladri Maitra, Noela Müller, and Alexander Van Werde.

Upcoming and recent talks (2024)

Upcoming talks

Recent talks

June 11: Konstantin Avrachenkov (INRIA Sophia Antipolis).
Higher-order spectral clustering for geometric graphs.

In many networks nodes have features from geometric spaces. However, in the case of clustering geometric graphs—the task of identifying groups of tightly connected nodes in a graph—can be rather difficult. The widely used method of spectral clustering is mostly inefficient for these graphs. It is based on the eigenvector of the adjacency matrix associated with the second eigenvalue. For geometric graphs such a vector provides information about the geometric structure but does not allow to recover the cluster structure.

We present an effective generalization of this method, which we call higher-order spectral clustering. It resembles in concept the classical spectral clustering method but uses for partitioning the eigenvector associated with a higher-order eigenvalue. As it will be shown, this algorithm gives the almost exact recovery of clusters for a wide class of geometric graphs sampled from Soft Geometric Block Model. A small adjustment of the algorithm provides the exact recovery. We also show that our method is effective in numerical experiments for real world networks.

This is a joint work with Andrei Bobu (Criteo) and Maximilien Dreveton (EPFL).

Link to the paper: https://link.springer.com/content/pdf/10.1007/s00041-021-09825-2.pdf

May 28: Gábor Pete (Alfréd Rényi Institute of Mathematics & Budapest University of Technology and Economics)
Preferential attachment trees built from random walks.

I will talk about two separate projects where random walks are building a random tree, yielding preferential attachment behaviour from completely local mechanisms.

First, the Tree Builder Random Walk is a randomly growing tree, built by a walker as she is walking around the tree. At each time n, she adds a leaf to her current vertex with probability $n^{-\gamma}$, $\gamma \in (2/3, 1]$, then moves to a uniform random neighbor on the possibly modified tree. We show that the tree process at its growth times, after a random finite number of steps, can be coupled to be identical to the Barabási-Albert preferential attachment tree model. This coupling implies that many properties known for the BA-model, such as diameter and degree distribution, can be directly transferred to our model. Joint work with János Engländer, Giulio Iacobelli, and Rodrigo Ribeiro, https://arxiv.org/abs/2311.18734.

Second, we introduce a network-of-networks model for physical networks: we randomly grow subgraphs of an ambient graph (say, a box of $\Z^d$) until they hit each other, building a tree from how these spatially extended nodes touch each other. We compute non-rigorously the degree distribution exponent of this tree in large generality, and offer a novel way, the Physical Laplacian, to measure the effects of physicality on the network structure. We also provide a rigorous analysis when the nodes are loop-erased random walks in dimension $d=2$ or $d\ge 5$, using a connection with the Uniform Spanning Tree. Joint work with Ádám Timár, Sigurdur Örn Stefánsson, Ivan Bonamassa, and Márton Pósfai, https://arxiv.org/abs/2306.01583, to appear in Nature Communications.

May 7: Krystal Guo (Korteweg-De Vries Institute for Mathematics, University of Amsterdam and QuSoft)
Algebraic graph theory and quantum walks

The interplay between the properties of graphs and the eigenvalues of their adjacency matrices is well-studied. Important graph invariants, such as diameter and chromatic number, can be understood using these eigenvalue techniques. In this talk, we bring these classical techniques in algebraic graph theory to the study of quantum walks.

A system of interacting quantum qubits can be modelled by a quantum process on an underlying graph and is, in some sense, a quantum analogue of random walk. This gives rise to a rich connection between graph theory, linear algebra and quantum computing. In this talk, I will give an overview of applications of algebraic graph theory in quantum walks, as well as various recent results.

In a recent paper with V. Schmeits, we show that the evolution of a general discrete-time quantum walk that consists of two reflections satisfies a Chebyshev recurrence, under a projection. We apply this to a model of discrete-time quantum walk proposed by H. Zhan [J. Algebraic Combin. 53(4):1187-1213, 2020], whose transition matrix is given by two reflections, using the face and vertex incidence relations of a graph embedded in an orientable surface. For the vertex-face walk and more general 2-reflection quantum walks, we prove theorems about perfect state transfer and periodicity and give infinite families of examples where these occur.

April 23: Phyllis Wan (Erasmus University Rotterdam)
Graphical lasso for extremes

Gaussian graphical lasso is a powerful tool for modelling sparse dependence structure for non-extreme data. For extreme data, Gaussian graphical model is not suitable. Instead, Huesler-Reiss graphical model was recently proposed as an alternative. The adaptation of graphical lasso in this scenario is not straightforward due to the different structure in parameter matrix. We propose a graphical lasso for Huesler-Reiss graphical model through a reparametrization. The estimator is solved via a penalized likelihood and enjoys convenient properties of the traditional graphical lasso: concentration equalities, fast computation and the ability to scale up to large dimensions.

March 19: Samuel Fiorini (Université Libre de Bruxelles)
Integer programs with nearly totally unimodular matrices: the cographic case

It is a notorious open question whether integer programs (IPs) of the form max {p^T x : Mx <= h, x in Z^n}, where all square submatrices of M have their determinants bounded by a constant Delta in absolute value, can be solved in polynomial time.

We consider the case in which we further require that by removing a constant number of rows and columns from M, one obtains a submatrix A that is totally unimodular (TU). In virtue of Seymour’s decomposition of TU matrices, there are two base cases to consider, namely the case where A is a network matrix and that where A is the transpose of a network matrix. We show that the IP can be solved in strongly polynomial time in the second base case, that is, when A is the transpose of a network matrix. We achieve our result in two main steps, the first related to the theory of IPs and the second related to graph minor theory.

First, we derive a strong proximity result for the case where M becomes TU after removing k rows only, where k is a constant. In particular, we show that given an optimal solution of the linear programming relaxation, an optimal solution to the IP can be obtained by finding a constant number of augmentations by circuit vectors.

Second, we focus on the case where A is the transpose of a network matrix. We reformulate the IP as a maximum constrained potential problem on a graph G. In this context, circuit vectors have a natural combinatorial interpretation: they correspond to “doubly connected sets”. As part of the input, every vertex has an associated vector of k integer weights. These define a natural set of *roots*, namely, the vertices whose weight vector is nonzero. We observe that if G is 2-connected, then it has no rooted K_{2,t}-minor for t = \Omega(k \Delta). We leverage this to obtain a tree-decomposition of G into highly structured graphs for which we can solve the problem locally. This allows us to solve the global problem via dynamic programming.

This is joint work with Manuel Aprile, Gwenaël Joret, Stefan Kober, Michał Seweryn, Stefan Weltge and Yelena Yuditsky.

March 5: Wouter Duivesteijn and Sibylle Hess (TU/e)
Clusters, Subgroups, Preferences

We will discuss some of our recent work advancing two data mining subfields that can be seen as cousins of each other.

Clustering strives to partition a dataset into coherent groups. This becomes challenging when the natural shape of the clusters in the data space is nonconvex, and when there is a considerable amount of noise in the data. We introduce SpectACl (Spectral Averagely-dense Clustering), a new clustering method introducing a small but important change of the popular Spectral Clustering method, that is motivated by filling a gap in the theory of kernel-based clustering methods. SpectACl finds clusters having a large average density, where the appropriate density for each cluster is automatically determined through the spectrum of the weighted adjacency matrix. This opens up a comparison to the density-based DBSCAN clustering method, eliminating the parameter sensitivity issues from which DBSCAN suffers. As in the Spectral Clustering pipeline, the final step of SpectACl is an embedding postprocessing step using k-means. However, unlike Spectral Clustering, we demonstrate the fundamental soundness of applying k-means to the embedding step in SpectACL: from SpectACl’s objective function we derive an upper bound by means of the eigenvector decomposition; we derive that the optimization of our upper bound is equal to k-means on the eigenvectors.

Exceptional Model Mining (EMM) strives to find subgroups in a dataset that behave somehow exceptionally. We partition the columns of the dataset into two sets. The one set is used for defining candidate subgroups (by making conjunctions of conditions on single attributes); a challenge lies in efficiently traversing the search space of candidate subgroups. The other set is used for evaluating exceptionality of subgroup behavior: we define a kind of interaction between these target columns, and deem subgroups to be exceptional if parameters of this interaction have exceptional values. We will illustrate this concept with three types of interaction: standard linear regression and its implications for the price of rice in China, nonlinear mixed models and their application on potato growth, and preferences (where the target columns become a ranking of discrete objects) and their application on sushi and elections. The last kind of interaction turns EMM into Exceptional Preferences Mining. It can be made more intelligent by incorporating clustering-like behavior into the function that determines exceptionality.

February 20: Rajat Subhra Hazra (Leiden University)
Evolution of discordances in voter dynamics on random regular graphs.

We consider the classical continuous-time interacting particle system for 2-opinion dynamics known as the voter process on a random d-regular graph.It is well known that this particle system is dual to a system of coalescing random walkers and that in this geometrical setting, as the graph size increases, the time to reach consensus grows quadratically with the number of nodes n.

We study the time-evolution of the density of the discordant edges (i.e. edges with different opinions at their end vertices) until the consensus is reached. This observable can be thought as the dynamic “perimeter” associated to the density of opinions of one type in the given network (the latter to be seen as the “volume”). While the volume of one opinion evolves as a martingale until the consensus time, the density of discordant edges (i.e. the perimeter) undergoes a very different metastable evolution which we make precise.

If time permits we will discuss extensions to the directed configuration models.

February 6: Gabriel Coutinho (Universidade Federal de Minas Gerais)
Semidefinite Programming and Gauge Duality

Antiblocking duality is a key piece in the theory of polyhedral combinatorics. This concept can be applied to convex corners which are not polyhedra, and can be interpreted in a general setting of norm or gauge duality. In this talk I will carefully explain what all of this means, and I will show an application to derive an alternative formulation for the celebrated Lovász theta number of a graph. I will also comment on other recent applications, and possible avenues for further investigation.

January 23: Ivo Adan (TU/e)
A rate balance principle

In this talk we will introduce a rate balance principle for general (not necessarily Markovian) stochastic processes. The usefulness of this principle will be demonstrated by deriving well-known as well as new results for queues with state-dependent arrival and services processes.

January 16: Introduction event for new members:
  • Alexandra Lassotta
  • Nelly Litvak
  • Frank Röttger

Past talks (2015-2023)


December 12: Jannis Kurtz (University of Amsterdam)
Approximating integer two-stage robust optimization problems with decision rules and neural networks.

In two-stage robust optimization (2RO) we tackle optimization problems with an inherent two-stage structure involving uncertainty in the problem parameters. This type of problems has a wide range of applications especially in the field of operations research. However, when the decision variables are integer, these problems are computationally extremely challenging and there is still a lack of efficient approximation algorithms.

In the first part of this talk we consider the so called k-adaptability approach, where a set of k second-stage solutions is calculated already in the first-stage which leads to approximate solutions of the problem. While this approach attained increasing popularity, nothing is known about its approximation guarantees for 2RO. We derive the first known approximation guarantees depending on the number of solutions k, which lead to several interesting insights on the complexity of the problem.

In the second part we consider a deep learning approach to solve 2RO, where a neural network is trained on small-dimensional instances to predict the second-stage optimal values. This neural network is then incorporated into a column-and-constraint generation algorithm (a classical exact method to solve 2RO), leading to significant improvements in computation time and often even solution quality.

December 1: Special event with three speakers!
  • Benny Van Houdt (U Antwerpen)
    Asymptotic and Stochastic Improvements of First-Come First-Served Scheduling
  • Christine Fricker (INRIA)
    Fluid limit for a M/G/infini queue with advance booking, booking cancelation and reneging

    The motivation is the study of car-sharing systems, with two kinds of cars, free-floating cars parked everywhere in the service area and station-based cars. These systems become popular as a green urbain mode of transportation, designed to avoid users owning a car. For station-based cars which can be book in advance, we study a simple model. It is a variant of the infinite server queue with Poisson arrival process and general service time distribution with advance reservation, possibility of cancellation before service and reneging during service. The convergence to a fluid limit extends the classical M/G/infini queue case. When the capacity is finite, there are two regimes for this loss system. In saturation, the capacity is not always reached. It is a joint work with Florian Verdier.

  • Andres Ferragut (Universidad ORT Uruguay)
    On the universal optimality of timer based caching policies for decreasing hazard rates

    Consider a caching system receiving requests from a given catalog. The cache objective is to maximize its hit rate, i.e. the number of items that are served directly from the cache. Two main alternatives have been studied: replacement policies (such as LRU) and timer-based policies (TTL). In previous work, we have explored the role of the hazard rate function of the inter-request times in the optimal TTL policy. Recently, Towsley et al. also identified that the optimal non-anticipative replacement policy involves the hazard rate function.
    In this talk we will discuss how these two concepts relate, in the case of decreasing hazard rates. In particular we show that, in large scale systems, the optimal non-anticipative policy converges to a fixed threshold policy, and that this threshold is exactly the dual value of the convex optimization problem that defines the optimal TTL policy. Therefore, we can approximate the optimal hit rate by the TTL policy fluid limit, allowing us to compute a universal performance bound. Time permitting, we will also analyze in particular the role of heavy tails (Zipf like) in popularities and discuss implementation issues.

November 21: Irène Gijbels (KU Leuven)
Copula-based divergence measures between random vectors and application in hierarchical variable clustering

The interest in this talk is on quantifying dependence between a finite number of random vectors. We focus on copula-based dependence quantification between multiple groups of random variables of possibly different sizes. This is done via a family of Phi-divergences (which includes for example mutual information). A theoretical framework is provided, and the divergence measures are illustrated by means of examples. Special attention goes to elliptical copulas, with Gaussian and Student’s t copulas as exemplary cases. Parametric and semi-parametric estimation procedures for the divergence measures are briefly discussed. These divergence measures are then used for measuring the similarity between variable clusters in an agglomerative hierarchical procedure. An example involving cluster analysis of continuous random variables is presented.

This talk is based on joint work with Steven De Keyser.

October 31: Cassio de Campos (TU/e)
Credal Models for Uncertainty Treatment

There is a current trend on reevaluating artificial intelligence (AI), its advancements and their implications to society. Uncertainty treatment plays a major role in this discussion. This talk will hopefully convince you that we can make AI more reliable and trustworthy by a sound treatment of uncertainty. Uncertainty is often modelled by probabilities, while it has been argued that some broadening of probability theory is required for a more convincing treatment, as one may not always be able to provide a reliable probability for every situation. Credal models generalize probability theory to allow for partial probability specifications and are arguably a good direction to follow when information is scarce, vague, and/or conflicting. We will present and discuss credal approaches from simple examples to sophisticated credal machine learning models and even their reach into both adversarial and causal inferences. The talk argues that we must continue to push AI forward by investing in Cautious AI.

October 10: Richard Gill (Leiden University)
Statistical issues in the investigation of a suspected serial killer nurse

Investigating a cluster of deaths on a hospital ward is a difficult task for medical investigators, police, and courts. Patients do die in hospitals (the three most common causes of deaths in a hospital are, in order: cancer, heart disease, medical errors). Often such cases come to the attention of police investigators for two reasons: gossip about a particular nurse is circulating just as a couple of unexpected and disturbing events occur. Hospital investigators see a pattern and call in the police.

I will discuss two such cases with which I have been intensively involved. The first one is the case of the Dutch nurse Lucia de Berk. Arrested in 2001, convicted by a succession of courts up to the supreme court by 2005, after which a long fight started to get her a re-trial. She was completely exonerated in 2010. The second case is that of the English nurse Lucy Letby. Arrested in 2018, 2019 and 2020 for murders taking place in 2015 and 2016. Her trial started in 2022 and concluded with a “full life” sentence a couple of months ago.

There are many similarities between the two cases, but also a couple of disturbing differences. One difference being that Lucy Letby’s lawyers seem to have made no attempt whatsoever to defend her. Another difference is that statistics was used against Lucia de Berk but not, apparently, against Lucy Letby. But appearances are not always what they seem.

Report published by Royal Statistical Society on statistical issues in these cases

News feature in “Science” about myself and my work

September 26: Ralph Neininger (Goethe University Frankfurt)
Recursive distributional equations in applications

Recursive distributional equations (RDE) appear in various areas of probability such as the probabilistic analysis of algorithms, probabilistic number theory, stochastic geometry or, more recently, in distributional reinforcement learning (DRL). In this talk more applied aspects of RDE are discussed in the context of their applications. Some of the new results discussed on DRL are joint work with Julian Gerstenberg and Denis Spiegel

September 12: Carla Groenland (Delft University of Technology)
Counting graphic sequences via integrated random walks

Via a new probabilistic result, we provide (1+o(1))-asymptotics for the number of integer sequences n-1>= d_1 >= … >= d_n >= 0 that form the degree sequence of an n-vertex graph (improving both the upper and lower bound by a multiplicative n^{1/4}-factor). In particular, we determine the asymptotic probability that the integral of a (lazy) simple symmetric random walk bridge remains non-negative. This talk will explain how this problem arose, what the connection is with the problem about random walks (including what all the words in this abstract mean) and then provide a short sketch of the proof. This is based on joint work with Paul Balister, Serte Donderwinkel, Tom Johnston and Alex Scott.

June 6: Lars Rohwedder (Maastricht University)
Recent advances in the Santa Claus problem

Over the past 20 years, the Santa Claus problem has been the source of many beautiful algorithmic ideas and to date remains an active and fruitful research area. The problem takes its metaphorical name from the narrative of Santa Claus distributing his gifts to children in a way that the least happy child is as happy as possible. I will give an overview over the landscape of techniques used to approach the problem, previous results as well as open questions. Then I will focus on recent joint work with Bamas [STOC’23], where we make progress towards sublogarithmic approximations.

May 17: Varun Gupta (University of Chicago)
Greedy Algorithm for Multiway Matching with Bounded Regret

We consider a finite horizon online resource allocation/matching problem where the goal of the decision maker is to combine resources (from a finite set of resource types) into feasible configurations. Each configuration is specified by the number of resources consumed of each type and a reward. The resources are further subdivided into three types – offline, online-queueable (which arrive online and can be stored in a buffer), and online-nonqueueable (which arrive online and must be matched on arrival or lost). We prove the efficacy of a simple greedy algorithm when the corresponding static planning linear program (SPP) exhibits a non-degeneracy condition called the general position gap (GPG). In particular we prove that, (i) our greedy algorithm gets bounded any-time regret when no configuration contains both an online-queueable and an online-nonqueueable resource, and (ii) O(log t) expected any-time regret otherwise (we also prove a matching lower bound). By considering the three types of resources, our matching framework encompasses several well-studied problems such as dynamic multi-sided matching, network revenue management, online stochastic packing, and multiclass queueing systems.

May 9: Cécile Mailler (University of Bath)
Scaling limit of critical random trees in random environment

It has been well-known since Aldous’ seminal work in the 90s that a critical Galton-Watson tree conditioned to survive until generation n converges, as n goes to infinity, to a continuous random tree (CRT). This is the equivalent, in the world of random trees, of the fact that a random walk with finite-variance i.i.d. increments converges to the Brownian motion. In this is joint work with Guillaume Conchon-Kerjan and Daniel Kious, we prove that a Galton-Watson tree “in random environment” (GWRE) also converges to the CRT. GWREs are GW trees in which the offspring distribution of an individual depends on its generation, and the sequence of offspring distributions (indexed by the generation) is sampled in an i.i.d. way. I will review known results on GWREs before stating our main result and giving ideas of the proof.

April 25: Afrouz Jabal, Marek Skarupski (TU/e)
Research Introduction

During this seminar, two postdocs within our cluster will introduce themselves and their research.

April 4: Oliver Tse (TU/e)
Optimization with Interacting Particles

We discuss recent developments in the use of interacting particles for solving high-dimensional optimization problems, and provide insights into analytical guarantees for convergence in the particle mean-field limit.

March 21: M. Rosário Oliveira (Universidade de Lisboa)
Interval Principal Component Analysis: What if our data are intervals?

Symbolic Data Analysis gives a new way of thinking in Data Science by extending the standard variables to take into consideration new kinds of data, called “symbolic”, as they cannot be reduced to numbers without losing much information. Interval data are a prime example of symbolic data. There have been a series of proposed adaptations of the Principal Component Analysis method for interval-valued symbolic data, all of which have the downside of having intermediate steps that deal with conventional data. In this talk, we use algebraic structures on the intervals, to define symbolic principal components as linear combinations of the intervals that maximise the symbolic variance. This framework provides the mathematical tools needed to use the symbolic principal components to transform the original data in a way that is mathematically coherent with the remainder of the framework and defines the principal components as solutions to maximisation problems, like what is done in conventional Principal Component Analysis. As in the conventional case, our formulation of interval-based principal component analysis can be seen as a dimensionality reduction method, since we explicitly project the original symbolic observations on the reduced space spanned by the first principal components. We conclude by exploring real-world data from the telecommunications sector, in an attempt to detect Internet redirection attacks in real time. Joint work with Rodrigo Serrão Girão and Lina Oliveira

February 28: Johan Segers (ISBA)
Modelling multivariate extreme value distributions via Markov trees

Multivariate extreme value distributions are a common choice for modelling multivariate extremes. In high dimensions, however, the construction of flexible and parsimonious models is challenging. We propose to combine bivariate extreme value distributions into a Markov random field with respect to a tree. Although in general not an extreme value distribution itself, this Markov tree is attracted by a multivariate extreme value distribution. The latter serves as a tree-based approximation to an unknown extreme value distribution with the given bivariate distributions as margins. Given data, we learn an appropriate tree structure by Prim’s algorithm with estimated pairwise upper tail dependence coefficients or Kendall’s tau values as edge weights. The distributions of pairs of connected variables can be fitted in various ways. The resulting tree-structured extreme value distribution allows for inference on rare event probabilities, as illustrated on river discharge data from the upper Danube basin. Based on joint work with Shuang Hu and Zuoxiang Peng.

February 7: Benoît Corsini, Ralihe Villagran, Yaron Yeger (TU/e)
Research introduction

During this seminar, three postdocs within our cluster will introduce themselves and their research.


Dec 6 Luca Avena (LEI)
Nov 22 Peter Grunwald (CWI/LEI)
Nov 1 Sjoerd Dirksen (UU)
Oct 11 Debankur Mukherjee (GT)
Sep 27 Pieter Kleer (TiSEM)
Sep 13 Rik Versendaal (UU)
Jun 14 Alex Mey, Martin Frohn (TU/e)
May 31 Dirk Fahland (TU/e)
May 17 Rik Versendaal (UU)
Apr 26 Karl Rohe (UW Madison)
Feb 22 Adam Zsolt Wagner (Tel Aviv University)


Dec 21 Botond Szabo (Bocconi University)
Nov 9 Matthias Mnich (TUHH)
Oct 19 Noella Müller (TU Eindhoven)
Sep 14 Matteo D’Achille  (Université Paris-Est Créteil)
Jun 29 Tim van Erven (UvA
Jun 15 Alessandra Cipriani    (TUDelft)
Jun 1 Ahmad Abdi
May 18 Shaoji Tang
May 4 Matthieu Jonckheere
Apr 13 Oxana Zaal
Apr 13 Christopher Hojny   (TU Eindhoven)
Apr 6 Alessandra Cipriani (TU Delft)
Mar 23 Christian Brownlees   (UPF)
Mar 9 Tim Oosterwijk   (UM)
Feb 23 Miklos Racz   (Princeton University)
Feb 9 Christian Hirsch  (University of Groningen)
Jan  26 Piotr Zwiernik   (UPF)


Dec 8 Guillem Perarnau    (UPC)
Nov 24 Alessandro Zocca    (VU)
Nov 10 Jaap Storm (TUe)
Nov 10 Suman Chakraborty (TUe)
Oct 27 Bart Smeulders    (TUe)
Oct 27 Kathrin Möllenhoff
Oct 13 Wouter de Vries (KPN)
Sep 29 Laura Sanità
Sep 15 Zsolt Bartha
Sep 15 Elisa Perrone

March 2020: the STO-seminar programme has been discontinued due to the Covid-19 pandemic.

Feb 26 Rob van der Mei (CWI – VU)
Feb 4 Jim Portegies (TU Eindhoven)
Jan 16 Seva Shneer (Heriot-Watt University)


Dec 16 Gianmarco Bet (University of Florence)
Weighted Dyck paths for non stationary queues
Dec 3 Kay Bogerd (TU Eindhoven)
Detecting small communities in inhomogeneous random graphs
Nov 28 Johannes Schmidt-Hieber (UTwente)
Towards a statistical foundation of deep learning
Nov 12 Tim Hulsof (TU Eindhoven)
Oct 24 Joost Jorritsma (TU Eindhoven)
Typical weighted distance in preferential attachment models – Interpolating small and mini worlds
Oct 8 Albert Senén Cerdà ; Pim van der Hoorn; Martin Zubeldia (TU Eindhoven)
New colleagues introducing themselves and their research
Sep 18 Corli van Zyl (North-West University, Potchefstroom, South Africa)
Signed sequential rank cumulative sum charts
Sep 12 Richard Post (Eindhoven University of Technology)
Complex nanomaterials: a stochastic view
Feb 8 Siva Athreya (Indian Statistical Institute)
Respondent Driven Sampling and Random Graph Convergence
Jan 24 Sam Thomas (Cambridge)
Cutoff for Random Walk on Dynamical Erdos-Renyi
Jan 17 Jeannette Janssen (Dalhousie University)
Recognizing graphs formed by a spatial random process


Dec 12 Ziv Scully (Carnegie Mellon University)
SOAP: One Clean Analysis of All Age-Based Scheduling Policies
Dec 11 Stefan Klootwijk (University of Twente)
On Random Shortest Path Metrics
Oct 5 Andreas Kyprianou (University of Bath)
Some strange results in fragmentation-coalescence models
Oct 5 Joris Mulder (Tilburg University)
The Matrix-F Prior for Estimating and Testing Covariance Matrices
Aug 27 Nicolas Broutin (Sorbonne Université)
Fragmentations and tree-like fractals: a functional fixed-point approach
Aug 27 David Goldberg (Cornell University)
Beating the curse of dimensionality in options pricing and optimal stopping
Aug 27 Lutz Warnke (Georgia Institute of Technology)
A dynamic view on the probabilistic method: random graph processes
Aug 10 Yoshiaki Inoue (Osaka University, Dept. of Information and Communications Technology)
Sample-Path Analysis of the Age of Information (AoI) and Its Applications to FCFS Single-Server Queues
June 8 Santiago Duran (CNRS, LAAS & Universite de Toulouse)
Asymptotic Optimal Control of Markov-Modulated Restless Bandits
June 5 Ambedkar Dukkipati (Indian Institute of Science)
Spectral graph algorithms for community detection in networks: Statistical Analysis and Consistency
May 24 Adelle Coster (UNSW Sydney)
Mathematical Modelling of Insulin Regulation in Glucose Transport
May 23 Botond Szabo (Universiteit Leiden)
Bayesian nonparametric approach to log-concave density estimation
May 15 Chang-Han Rhee (CWI)
On Heavy-Tailed Rare-Event Analysis
May 8 Yutaka Sakuma (National Defense Academy)
An arrival distribution for the equilibrium expected waiting time in a discrete-time single-server queue with acceptance period and Poisson population of customers
Apr 25 Zhuozhao Zhan (Eindhoven University of Technology)
Optimal Unidirectional Switch Design
Apr 24 Frits Spieksma (Eindhoven University of Technology)
Robust Balanced Optimization Problems
Apr 4 Eyal Castiel (Toulouse Mathematical Institute (IMT) and ISAE)
Queuing Networks and separation of time scales using Log-Sobolev inequality
Mar 28 Liudmila Prokhorenkova (Yandex)
Community detection through likelihood optimization: in search of a sound model
Mar 21 Céline Comte (Nokia Bell Labs France and Telecom ParisTech)
Performance of Balanced Fairness in Resource Pools: A Recursive Approach
Feb 1 Benny van Houdt (Universiteit Antwerpen)
On the Response Time Distribution of a Class of Limited Processor Sharing Queues
Feb 1 Nicolas Gast (INRIA & University of Grenoble Alpes)
A Refined Mean Field Approximation


Dec 6 Nelly Litvak (University of Twente, TU/e)
Who needs mathematics: talking maths to a general public
Oct 11 Hermann Þórisson (University of Iceland)
Palm Versions and Extra Heads
June 21 Jan Nagel (TU/e)
The speed of biased random walk among random conductances
Jun 1 Ron Kenett (University of Turin)
On the Performance of Sequential Procedures for Detecting a Change, and Information Quality (InfoQ)
May 18 Galit Yom-Tov (Technion – Israel Institute of Technology)
An Invitation Control Policy for Proactive Service Systems: Balancing Efficiency, Value and Service Level
Feb 15 Paulo Serra (TU/e)
Dimension Estimation using Random Connection Models


Nov 30 Rob van den Berg (VU Amsterdam)
Near-critical and frozen percolation
Oct 18 Rob J Hyndman (Monash University, Australia)
Forecasting large collections of related time series
Sept 28 Tim Hulshof (TU/e)
Higher order corrections for anisotropic bootstrap percolation
Sept 13 Gábor Lugosi (Universitat Pompeu Fabra)
How to estimate the mean of a random variable?
Sept 13 Robert Nowak (University of Wisconsin – Madison)
How to estimate the mean of a random variable?
June 21 Alessandro Di Bucchianico (TU/e)
Developments in Monitoring Dynamic Data
June 15 Alexandre Mauroy (Luxembourg Centre for Systems Biomedicine)
An operator-theoretic approach to network identification
May 11 Nikhil Bansal (TU/e)
On the Komlos Conjecture, or, how to Control your Brownian Motion
Feb 23 Edwin van den Heuvel (TU/e)
Randomized Controlled Trials: The Stepped Wedge Design
Feb 10 Christian Borgs (Microsoft Research New England)
Graphon processes as a new model for large, sparse graphs
Jan 26 Miklós Telek Technical University of Budapest
Markovian point, terminating and branching processes
Jan 20 Joachim Arts TU/e, IE&IS, OPAC group
Repairable Stocking and Expediting in a Fluctuating Demand Environment: Optimal Policy and Heuristics


Dec 16 Sándor Kolumbán (Technical University Eindhoven)
Data perturbation methods for hypothesis testing in nonlinear estimation problems
Nov 24 Jan Ramon (INRIA-Lille and KU Leuven)
Learning evolutionary models from a snapshot
Oct 14 Luca Avena (Leiden University)
Random walks in Markovian environments: a perturbative approach
June 10 Jan-Pieter Dorsman (Leiden University)
Analysis of fibre-loop optical buffers with a void-avoiding schedule
May 26 Phil Whiting (Macquarie University)
Compute and Forward for Wireless Networks – Scheduling and Capacity in Heterogenous Networks
May 6 Botond Szabó (CWI and Budapest University of Technology and Economics)
Adaptive horseshoe estimation
Apr 29 Balázs Ráth (Budapest University of Technology and Economics)
Multiplicative coalescent with linear deletion: a rigid representation
Apr 22 Elena Pulvirenti (Leiden)
Metastability for the Widom-Rowlinson model
Mar 18
Rui Castro (TU/e)
On adaptive sensing for inference of structured sparse signals
Mar 11 Carlo Lancia (University of Rome TorVergata)
Parallel TASEP on a ring: blockage problem and non-analyticity of the current
Mar 4 Daniel Valesin (Groningen)
Percolation on the stationary distribution of the voter model on Z^d
Feb 25 Stella Kapodistria (TU/e)
Scheduling preventive maintenance on a wind turbine based on quantitative data
Feb 3 Martin Roth (KNMI and TU/e)
Analysis of trends in extreme rainfall: A regional approach
Feb 3 Murtuza Ali Abidini (TU/e)
Performance Analysis in Optical Networks
Jan 27 Laurens Smit (Leiden University)
Explicit Solutions, Properties and Applications of DES and RES Markov processes
Jan 10 Tobias Muller (Utrecht University)
A hyperbolic model for complex networks
Jan 6 Robert Fitzner (Stockholm Universiteit)
Using Super-Resolution Microscopy to Probe Exchange Pathways (a joint venture of chemistry and mathematics)



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