[{"content":" Theory, statistics and algorithms for spatial stochastic processes\nComputational statistics and simulation\nStatistics in metric spaces and similarity spaces\n","date":null,"permalink":"https://dominicschuhmacher.name/","section":"Dominic Schuhmacher","summary":"","title":"Dominic Schuhmacher"},{"content":"","date":null,"permalink":"https://dominicschuhmacher.name/categories/","section":"Categories","summary":"","title":"Categories"},{"content":" 1994-2000 Study of Mathematics and Computer Science at the University of Zürich 2000-2005 Doctoral Studies at the University of Zürich 2005 Dr.sc.nat. in Mathematics, University of Zürich 2006-2008 Postdoc on SNF fellowship, University of Western Australia 2008-2013 Senior assistant at the Institute of Mathematical Statistics and Actuarial Science, University of Bern 2013 Habilitation (venia legendi) in Stochastics, University of Bern 2013- Full professor (W3) at the University of Göttingen 2015-2017\n2023-2025 Head of the Institute of Mathematical Stochastics ","date":null,"permalink":"https://dominicschuhmacher.name/cv/","section":"Dominic Schuhmacher","summary":"","title":"CV"},{"content":" Ich stelle Bescheinigungen über Grundkenntnisse in Stochastik für die Zulassung zur Aktuarausbildung aus, sofern diese an der Universität Göttingen erworben wurden. In der Regel muss dazu die Bachelorvorlesung Maß- und Wahrscheinlichkeitstheorie, sowie eine Vorlesung in (mathematischer) Statistik erfolgreich besucht worden sein. Siehe Anhang B der Lernziele im Grundwissen für die genaueren Inhalte.\nBitte senden Sie mir eine Kopie Ihres Bachelor- und/oder Masterzeugnisses zur Bestätigung und geben Sie an, in welchem Semester (z.B. WiSe 2021/22) die relevanten Vorlesungen besucht wurden. Sämtliche zugesandten Dokumente werden nach Abschluss des Vorgangs umgehend gelöscht.\n","date":null,"permalink":"https://dominicschuhmacher.name/dav/","section":"Dominic Schuhmacher","summary":"","title":"DAV-Korrespondent für Stochastik"},{"content":" The following is a very subjective collection of links centered around some professional and personal interests of mine.\nMaths, Stats and Programming Arktomys – Data Science Services\nIMS Göttingen\nMathematik an der Universität Göttingen\nR-project\nPosit\nHugging Face\nRcpp for everyone\nStan\nCGAL\nSpatstat\nOnlineGDB\nregexr\nDNSchecker\nGeneral Chat GPT\nClaude\nGemini\nThe Guardian\nQuanta Magazine\nHeise Online\nLEO English \u0026lt;--\u0026gt; German Dictionary\nUrban Dictionary\nLEO French \u0026lt;--\u0026gt; German Dictionary\nBob: dictionnaire d'argot\nDeepL Translator\nReiseauskunft DB\nGöttingen SUB Göttingen\nGöVB\nSwitzerland SRF\nopendata.swiss\nIdiotikon\nJapan Jisho - Online Dictionary\nJotoba - Online Dictionary\n言葉で遊ぼう - Japanese Wordle\n青空文庫 - Public Domain Texts\ne-Stat - 政府統計の総合窓口\n国立国語研究所 - National Institute for Japanase Language and Linguistics\nROIS-DS Center for Open Data in the Humanities\nNatively - graded Japanese books and more\nContemporary Japanese Literature Blog\nJapanese Film Database\nJust One Cookbook\nNHK World Japan\nNHK News Web Easy\nThe Japan Times\nSumikai\n","date":null,"permalink":"https://dominicschuhmacher.name/links/","section":"Dominic Schuhmacher","summary":"","title":"Links"},{"content":"Journal articles and preprints # Marina Struleva, Shayan Hundrieser, DS and Axel Munk (2026).\nSharp Convergence Rates of Empirical Unbalanced Optimal Transport for Spatio-Temporal Point Processes, Stochastic Process. Appl. 198, 104938. Marianne Abémgnigni Njifon, Tobias Weber, Viktor Bezborodov, Tyll Krueger and DS (2025).\nBlock Graph Neural Networks for tumor heterogeneity prediction, arXiv preprint. DS and Leoni Carla Wirth (2024). Stein's Method for Spatial Random Graphs, arXiv preprint. Raoul Müller, DS and Jorge Mateu (2024). ANOVA for metric spaces, with applications to spatial data,\nStatistical Science 39(2), 262-285. Marianne Abémgnigni Njifon and DS (2024). Graph convolutional networks for spatial interpolation of correlated data,\nSpatial Statistics 60, 100822. DS and Leoni Carla Wirth (2023). Assignment Based Metrics for Attributed Graphs, arXiv preprint. DS (2023). Distance maps between Japanese kanji characters based on hierarchical optimal transport, arXiv preprint. Raoul Müller, Anita Schöbel, DS (2023). Location problems with cutoff,\nAsia-Pacific Journal of Operational Research 40(3), 2250045. Johannes Wieditz, Yvo Pokern, DS and Stephan Huckemann (2022).\nCharacteristic and necessary minutiae in fingerprints, J R Stat Soc Series C 71, 27–50. Garyfallos Konstantinoudis, DS, Roland Ammann, Tamara Diesch, Claudia Kuehni and Ben Spycher (2020).\nBayesian spatial modelling of childhood cancer incidence in Switzerland using exact point data: a nationwide study during 1985–2015, International Journal of Health Geographics 19(15). Raoul M\u0026uuml;ller, DS and Jorge Mateu (2020). Metrics and barycenters for point pattern data,\nStatistics and Computing 30, 953-972. Valentin Hartmann and DS (2020).\nSemi-discrete optimal transport: a solution procedure for the unsquared Euclidean distance case,\nMathematical Methods of Operations Research 92, 133-163. Fabian Kück and DS (2020). Convergence rates for the degree distribution in a dynamic network model, Stochastic Models 36(1), 134-171. [Published version] Garyfallos Konstantinoudis, DS, H\u0026aring;vard Rue and Ben Spycher (2020).\nDiscrete versus continuous domain models for disease mapping, Spatial and Spatio-Temporal Epidemiology 32, 100319. [Published version] G\u0026uuml;nther Koliander, DS and Franz Hlawatsch (2018). Rate-distortion theory of finite point processes,\nIEEE Trans. Inf. Theory 64(8), 5832-5861. [Published version] Fabian Kück and DS (2018). On the age of a randomly picked individual in a linear birth and death process,\nJ. Appl. Probab. 55(1), 82-93. [Published version] J\u0026ouml;rn Schrieber, DS and Carsten Gottschlich (2017). DOTmark - a benchmark for discrete optimal transport,\nIEEE Access 5, 271-282. Nathan Ross and DS (2017). Wireless network signals with moderately correlated shadowing still appear Poisson,\nIEEE Trans. Inf. Theory 63(2), 1177-1198. [Published version] DS, Anja Sturm and Henryk Z\u0026auml;hle (2016).\nOn qualitative robustness of the Lotka-Nagaev estimator for the offspring mean of a supercritical Galton-Watson process,\nJ. Stat. Plan. Inference 169, 56--70. Carsten Gottschlich and DS (2014).\nThe shortlist method for fast computation of the earth mover's distance and finding optimal solutions to transportation problems,\nPLoS ONE 9(10), e110214. Lutz Dümbgen, Kaspar Rufibach and DS (2014).\nMaximum-likelihood estimation of a log-concave density based on censored data,\nElectron. J. Statist. 8, 1405-1437. Kaspar Stucki and DS (2014). Bounds for the probability generating functional of a Gibbs point process,\nAdv. Appl. Probab. 46(1), 21-34. DS and Kaspar Stucki (2014). Gibbs point process approximation: total variation bounds using Stein’s method,\nAnn. Probab. 42(5), 1911-1951 Lutz Dümbgen, Richard Samworth and DS (2011).\nApproximation by log-concave distributions, with applications to regression, Ann. Statist. 39(2), 702--730.\nExtended version: Technical report 75, IMSV, University of Bern (http://arxiv.org/pdf/1002.3448v3). DS, André Hüsler and Lutz Dümbgen (2011). Multivariate log-concave distributions as a nearly parametric model,\nStatistics \u0026amp; Risk Modeling 28(3), 277--295.\nOriginal article by kind permission of Oldenbourg Wissenschaftsverlag, Munich/Germany.\nExtended version: Technical report 74, IMSV, University of Bern (http://arxiv.org/abs/0907.0250). A. Baddeley, M. Berman, N. Fisher, A. Hardegen, R. Milne, DS, R. Shah, and R. Turner (2010).\nSpatial logistic regression and change-of-support in Poisson point processes,\nElectron. J. Statist. 4, 1151--1201. DS and Lutz Dümbgen (2010). Consistency of multivariate log-concave density estimators,\nStatist. Probab. Letters 80(5-6), 376--380. DS (2009). Stein's method and Poisson process approximation for a class of Wasserstein metrics,\nBernoulli 15(2), 550--568. DS (2009). Distance estimates for dependent thinnings of point processes with densities,\nElectron. J. Probab. 14, 1080--1116. DS and Aihua Xia (2008). A new metric between distributions of point processes,\nAdv. Appl. Probab. 40(3), 651--672. DS, Ba-Tuong Vo and Ba-Ngu Vo (2008). A consistent metric for performance evaluation of multi-object filters,\nIEEE Trans. Signal Processing 56(8, part 1), 3447--3457. DS (2005). Distance estimates for dependent superpositions of point processes,\nStochastic Process. Appl. 115, 1819--1837. [Reprint of final version] DS (2005). Distance estimates for Poisson process approximations of dependent thinnings,\nElectron. J. Probab. 10, 165--201. DS (2005). Upper Bounds for Spatial Point Process Approximations,\nAnn. Appl. Probab. 15, no. 1B, 615--651.\n\u0026nbsp; Book chapters, contributions to collections/proceedings # David Ginsbourger, Olivier Roustant, DS, Nicolas Durrande and Nicolas Lenz (2016).\nOn ANOVA decompositions of kernels and Gaussian random field paths,\nin Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014\nRonald Cools and Dirk Nuyens (editors), Springer Proceedings in Mathematics and Statistics 163, 315-330. DS (2014). Stein’s method for approximating complex distributions, with a view towards point processes,\nin: Stochastic Geometry, Spatial Statistics and Random Fields, Vol. II: Models and Algorithms,\nVolker Schmidt (editor), Springer Lecture Notes in Mathematics 2120, 1-30. Lutz Dümbgen, Richard Samworth and DS (2013). Stochastic search for semiparametric linear regression models,\nin: From Probability to Statistics and Back: High-Dimensional Models and Processes,\nA Festschrift in Honor of Jon Wellner, IMS collections vol. 9, 78--90. E-book # DS (2026), Spatial Stochastics: A Three-Semester Course. \u0026nbsp;\n","date":null,"permalink":"https://dominicschuhmacher.name/publications/","section":"Dominic Schuhmacher","summary":"","title":"Publications"},{"content":" Former members #Postdoc # Dr. Viktor Bezborodov PhD students # Dr. Marianne Abemgnigni Njifon\nGraph Neural Networks for Spatial Data Analysis: Theoretical Results and Applications (2025) Dr. Leoni Carla Wirth\nSpatial Random Graphs: Studying Dependence Structures in Spatial Data (2025)\njoint supervision with Prof. G. Reinert Dr. Raoul Müller\nBarycenters and ANOVA for Point Pattern Data (2022)\njoint supervision with Prof. A. Sch\u0026ouml;bel Dr. Johannes Wieditz\nCharacteristic and Necessary Minutiae in Fingerprints (2021)\njoint supervision with Prof. Stephan Huckemann Dr. Henning Höllwarth\nRegularized Rao–Blackwellization – An Extension of a Classical Technique with Applications to Gibbs Point Process Statistics (2020) Dr. Jörn Schrieber\nAlgorithms for Optimal Transport and Wasserstein Distances (2019)\njoint supervision with Prof. Anita Schöbel Dr. Garyfallos Konstantinoudis\nAnalysis of Clustering of Childhood Cancers (2019)\njoint supervision with PD Ben Spycher, PhD, Universität Bern Dr. Fabian Kück\nConvergence Rates in Dynamic Network Models (2017)\nDr. Kaspar Stucki\nInvariance Properties and Approximation Results for Point Processes (2013)\njoint supervision with Prof. Ilya Molchanov, Universität Bern ","date":null,"permalink":"https://dominicschuhmacher.name/research/","section":"Dominic Schuhmacher","summary":"","title":"Research Group"},{"content":"pppdist and related functions #for working with inter-point-pattern distances as a contribution to the R package\nSpatstat: Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests\nR package logconcens #Maximum Likelihood Estimation of a log-Concave Density Based on Censored Data\nlogconcens package on CRAN Presentation at the Swiss Statistics Meeting 2011 in Fribourg Dümbgen, Rufibach and S (2014) R Package transport #Computation of Optimal Transport Plans and Wasserstein Distances\ntransport package on GitHub transport package on CRAN R Package ttbary #Barycenter Methods for Spatial Point Patterns\nttbary package on CRAN Müller, S and Mateu (2020) R Package kanjistat #A Statistical Framework for the Analysis of Japanese Kanji Characters\nkanjistat website (GitHub) kanjistat package on CRAN S (2023) Interactive visualization of kanji distances R Package graphmetrics #Assignment Based Metrics for Attributed Graphs\ngraphmetrics package on GitHub S and Wirth (2023) ","date":null,"permalink":"https://dominicschuhmacher.name/software/","section":"Dominic Schuhmacher","summary":"","title":"Software"},{"content":"","date":null,"permalink":"https://dominicschuhmacher.name/tags/","section":"Tags","summary":"","title":"Tags"},{"content":" Lecture course on spatial stochastics #My three-semester lecture course covers mainly the probability theory of random objects in space. A relatively large part is the theory of point processes (random point patterns). Other objects include random fields (random functions), random closed sets, random graphs and random tessellations in $\\mathbb{R}^d$. In the accompanying seminars we treat complementary topics of stochastic simulation and statistics of objects in Euclidean space.\nThe lecture notes are now available as an open-access book.\nLecture notes on spatial statistics #These lecture notes from 2011 are for a one-semester course which first gives an introduction to point process theory and then focuses on statistical aspects of point process.\nBachelor\u0026rsquo;s and Master\u0026rsquo;s theses #Below is a list of theses I have supervised. The names of the students have been omitted due to European data protection laws except for published theses.\nMaster # Pit Neumann, Predicting opioid overdose locations using the INLA-SPDE method for fast Bayesian inference in point process models (2025) Iterative Boltzmann Inversion — A spatial statistics perspective (2023) Theory for Hamiltonian MCMC algorithms for Gibbs processes (2023) The Implementation of a Risk Analysis in the Reinsurance Sector using Copulas (2022); joint supervision with Prof. Michael Fröhlich Structural Inference for Temporal Knowledge Graphs: a Deep Learning Method and a Stochastic Theory Framework (2022) Pricing Approaches for the Insurance Division Aviation in Primary Insurance and Reinsurance (2022); joint supervision with Prof. Michael Fröhlich Spatial Modelling of Gaussian Markov Random Fields using INLA and SPDEs (2021) Uniqueness of Gibbs Measures: Sufficient Conditions (2021) Estimation of Photovoltaic-Generated power: Convolutional Neural Network vs Kriging (2021) Varianzanalyse in euklidischen und nichteuklidischen metrischen Räumen (2021) Wasserstein Learning for Generative Point Process Models (2020) Uniqueness of Gibbs measures via Disagreement Percolation (2020) Binned Estimation of the Pair Correlation Function and Iterative Boltzmann Inversion (2020) A Probabilistic Look at Mutual Information with Application to Point Process (2017) Selective Importance Sampling for Computing the Maximum Likelihood Estimator in Point Process Models (2017) Convergence Rates for Point Processes Thinned by Logit-Gaussian Random Fields (2016) Maximum-Likelihood-Schätzung von exponentiellen Familien von stochastischen Prozessen (2016) Maximum Likelihood Estimation for Spatial Point Processes using Monte Carlo Methods (2016) Statistical Inference of Linear Birth-And-Death Processes (2015) Konvergenzgeschwindigkeit für Markov-Chain Monte Carlo (2015) Tests auf Unabhängigkeit zwischen Punkten und Marken (2015) Thinning of Point Processes by [0,1]-Transformed Gaussian Random Fields (2014) Additivity and Ortho-Additivity in Gaussian Random Fields (2013); joint supervision with Prof. David Ginsbourger Bachelor # Fundamentals of Jackson Networks and Their Application (2026) An Application of Space-Time Point Processes: The ETAS-Model for Earthquake Prediction (2025) Konvergenzraten auf Grundlage der Minorisierungsbedingung für Markovketten auf abzählbarem Zustandsraum (2024) Continuum percolation theory (2024) Predictability of PRNGs using neural networks (2024) Exploring Spatio-Temporal Kriging: Theory and Application to Nitrogen Dioxide Data in the Greater Frankfurt Area (2023) Lennart Finke, Markov Models for Spaced Repetition Learning (2023); joint supervision with Prof. Anja Sturm Noisy Hamiltonian Monte Carlo with an application for point processes (2023) Maximum Likelihood Estimation for Hawkes Processes and Real Data Application (2022) Entrywise Relative Error Bounds for the Stationary Distribution of Perturbed Markov Chains on a Finite State Space. (2022) Second Order Moment Measures of Point Processes (2022) Obere Schranken bei der Bewertung von Stoploss-Verträgen in der Rückversicherung (2022); gemeinsame Betreuung mit Prof. Michael Fröhlich Statistical Analysis of Simulation Algorithms for Finite Random Fields \u0026mdash; with a Focus on the Swendsen-Wang Algorithm for the Ising Model (2022) Sequential Monte Carlo Methods and Their Applications in Stock Markets (2022) Comparison of Metropolis Chain and Glauber Dynamics for Proper q-Colorings on a Graph (2021) Das Tobit-Modell: Methodische Anwendungen und Vergleiche zu linearen Regressionen (2021) Markov-Ketten mit allgemeinem Zustandsraum (2021) A comparison between Metropolis–Hastings and Hamiltonian Monte Carlo (2020) Vorhersage im Besag-York-Mollié-Modell (2018) Spline-Regression (2018) Nichtparametrische Regression - Kernregression und lokale Polynome (2018) Theorie und Simulation von Gaußschen Markov-Zufallsfeldern (2018) Comparison of Logistic Regression and Maximum Pseudolikelihood for Spatial Point Processes (2017) Metropolis-Hastings Algorithms for Spatial Point Processes (2016) Valentin Hartmann, A Geometry-Based Approach for Solving the Transportation Problem with Euclidean Cost (2016) A Comprehensive Overview of Linear Birth-and-Death Processes with an Outlook to the Non-Linear Case (2015) Limit Behaviour of Discrete Models in Financial Mathematics (2015) Gaußsche Zufallsfelder: Differenzierbarkeit von Pfaden (2015) Shuffling Measures and the Total Variation Distance to a Perfectly Randomized Deck of Cards (2015) Numerical Computation of L2-Wasserstein Distance Between Images (2014) Erwartete Treffzeiten in Markovketten und deren Anwendung auf Glücksspiele mit Sicherungsoption (2013) ","date":null,"permalink":"https://dominicschuhmacher.name/teaching/","section":"Dominic Schuhmacher","summary":"","title":"Teaching"}]