Part of Prof. Dr. Heiko Röglin's group

Heading the subgroup on clustering for big data

Summer 2019: Projektgruppe Entwurf und Implementierung von Algorithmen

Summer 2019: Lab Efficient Algorithms for Selected Problems: Design, Analysis and Implementation

Winter 2018/2019: Seminar Foundations of Data Science

Winter 2018/2019: Cluster Analysis

Summer 2018: Seminar Advanced Algorithms

Summer 2018: Randomized Algorithms & Probabilistic Analysis

Winter 2017/2018: Seminar Advanced Algorithms

Summer 2017: Randomized Algorithms & Probabilistic Analysis

Summer 2016: Randomized Algorithms & Probabilistic Analysis

Winter 2019/2020: Seminar Advanced Algorithms

Hierarchical Clustering

Lena Carta: Fairness in min-sum-radii clustering, ongoing.

Lukas Drexler: Exponential-time algorithms for lower-bounded facility location, ongoing.

Anna Arutyunova: Variants of lower-bounded facility location, ongoing.

Julian Wargalla: Coresets for constrained clustering problems, ongoing.

Jan Höckendorff: Spherical k-means++ in data streams, ongoing.

Mehrdad Soltani: A Practical Implementation of a Kernel k-means++ Streaming Algorithm, Master thesis, 2019.

Yannick Vogt: Qualität primaler und dualer Lösungen für das Steinerwaldproblem (in German), Bachelor thesis, 2018.

Felix Schröter: Verschiedene lokale Suchalgorithmen für das Steinerwaldproblem (in German), Bachelor thesis, 2018.

Benedikt Pago: Analysis of divisive clustering algorithms, Master thesis, 2018.

Julius von Kohout: Empirical evaluation of k-means++, Master thesis, 2018.

Simon Dieck: Experimentelle Analyse von Algorithmen für das Steinerwald-Problem (in German), Bachelor thesis, 2018.

Matthias Tellen: Experimentelle Analyse von Varianten von Wards Methode (in German), Bachelor thesis, 2018.

Lukas Pradel: Kernel-k-means Methoden zur spektralen Clusteranalyse von Graphen (in German), Master thesis, 2015.

Jan Stallmann: Benchmarkinstanzen für das k-means Problem (in German), Bachelor thesis, 2014.

Hendrik Fichtenberger: Experimentelle Analyse von Kernmengenberechnungen für probabilistisches Clustering (in German), Bachelor thesis, 2012.

Qingchui Zhu: Datenstromalgorithmen für Regression (in German), Diploma thesis, 2012.

Geometric Data analysis, including k-means clustering in data streams

Combinatorial Optimization: Variants and Constraints

Approximation Algorithms

A. Großwendt, H. Röglin, M. Schmidt: Analysis of Ward's method. To appear at SODA 2019.

C. Rösner, M.S.: Privacy preserving clustering with constraints. ICALP 2018: 96:1-96:14. Also: CoRR, 2018, arXiv:1802.0249. Talk

M. Groß, A. Gupta, A. Kumar, J. Matuschke, D. R. Schmidt, M.S., J. Verschae: A Local-Search Algorithm for Steiner Forest. ITCS 2018, volume 94, 31:1–31:17, 2018. arXiv:1707.02753. Talk Poster

A. Gupta, G. Guruganesh, M.S.: Approximation algorithms for aversion k-clustering via local k-median. ICALP 2016: 66:1-66:13. Paper at DROPS Talk

J. Blömer, C. Lammersen, M.S., C. Sohler: Theoretical Analysis of the k-Means Algorithm - A Survey, CoRR, 2016, arXiv:1602.08254.

E. Lee, M.S., J. Wright: Improved and Simplified Inapproximability for k-means, CoRR, 2015, arXiv:1509.00916. Talk

J.-P. W. Kappmeier, D. R. Schmidt, M.S.: Solving k-means on High-Dimensional Big Data. SEA 2015: 259-270, arXiv:1502.04265.

H. Fichtenberger, M. Gillé, M.S., C. Schwiegelshohn, C. Sohler: BICO: BIRCH Meets Coresets for k-Means Clustering. ESA 2013: 481-492. Talk

D. Feldman, M.S., C. Sohler: Turning big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering. SODA 2013: 1434-1453. Talk (Focus on Coresets) Talk (Focus on Dimensionality Reduction)

M. Groß, J.-P. Kappmeier, D. R. Schmidt, M.S.: Approximating Earliest Arrival Flows in Arbitrary Networks. ESA 2012: 551-562. Best Student Paper Award. Talk

C. Lammersen, M.S., C. Sohler: Probabilistic k-Median Clustering in Data Streams. WAOA 2012: 70-81. Talk

F. Hellweg, M. S., C. Sohler. Testing Euclidean Spanners. ESA 2010: 60-71. Talk

M. S., M. Skutella. Earliest Arrival Flows with Multiple Sinks. ISCO 2010.

D. Dressler, M. Groß, J.-P. Kappmeier, T. Kelter, J. Kulbatzki, D. Plümpe (now Schmidt), G. Schlechter, M. S., M. Skutella, S. Temme. On the use of network flow techniques for assigning evacuees to exits. International Conference on Evacuation Modeling, ICEM 2009.

E. Lee, M.S., J. Wright: Improved and Simplified Inapproximability for k-means, Information Processing Letters, 2017, 120: 40-43.

J. Blömer, C. Lammersen, M.S., C. Sohler: Theoretical Analysis of the k-Means Algorithm - A Survey. Algorithm Engineering 2016: 81-116.

C. Lammersen, M.S., C. Sohler: Probabilistic k-Median Clustering in Data Streams, Theory of Computing Systems, volume 56, part 1, January 2015, pages 251-290.

M. S., M. Skutella. Earliest arrival flows in networks with multiple sinks, Discrete Applied Mathematics, volume 164, part 1, February 2014, pages 320–327.