Melanie Schmidt is a German computer scientist whose research involves algorithms for cluster analysis, including approximation algorithms, coresets, algorithmic fairness, and inapproximability. ^[1] She holds the chair for Algorithms and Data Structures in the Computer Science Department at Heinrich Heine University Düsseldorf. ^[2]

Education and career

Schmidt earned a diploma in computer science in 2009 through study at both the Technical University of Dortmund and the University of Verona in Italy. ^[1] She continued at the Technical University of Dortmund for doctoral study in computer science, and completed her doctorate ( Dr. rer. nat.) in 2014 with the dissertation Coresets and streaming algorithms for the ${\displaystyle k}$-means problem and related clustering objectives, jointly supervised by Christian Sohler, Johannes Blömer [ de], and Gernot Fink. ^{[1] [3] [4]}

After postdoctoral research at Carnegie Mellon University in the US and at the University of Bonn, she took a position at the University of Cologne in 2019 as junior professor of machine learning. She moved to her present position in Düsseldorf in 2021. ^[1]

Selected publications

• Lee, Euiwoong; Schmidt, Melanie; Wright, John (2017), "Improved and simplified inapproximability for ${\displaystyle k}$-means", Information Processing Letters, 120: 40–43, arXiv: 1509.00916, doi: 10.1016/J.IPL.2016.11.009
• Rösner, Clemens; Schmidt, Melanie (2018), "Privacy preserving clustering with constraints", in Chatzigiannakis, Ioannis; Kaklamanis, Christos; Marx, Dániel; Sannella, Donald (eds.), 45th International Colloquium on Automata, Languages, and Programming, ICALP 2018, July 9–13, 2018, Prague, Czech Republic, LIPIcs, vol. 107, Schloss Dagstuhl – Leibniz-Zentrum für Informatik, pp. 96:1–96:14, doi: 10.4230/LIPICS.ICALP.2018.96, S2CID  3343460
• Schmidt, Melanie; Schwiegelshohn, Chris; Sohler, Christian (2019), "Fair coresets and streaming algorithms for fair ${\displaystyle k}$-means", in Bampis, Evripidis; Megow, Nicole (eds.), Approximation and Online Algorithms – 17th International Workshop, WAOA 2019, Munich, Germany, September 12–13, 2019, Revised Selected Papers, Lecture Notes in Computer Science, vol. 11926, Springer, pp. 232–251, doi: 10.1007/978-3-030-39479-0_16, S2CID  57189168
• Feldman, Dan; Schmidt, Melanie; Sohler, Christian (2020), "Turning big data into tiny data: Constant-size coresets for ${\displaystyle k}$-means, PCA, and projective clustering", SIAM Journal on Computing, 49 (3): 601–657, arXiv: 1807.04518, doi: 10.1137/18M1209854; previously announced at the 2013 ACM–SIAM Symposium on Discrete Algorithms (SODA)

References

External links

• Home page
• Melanie Schmidt publications indexed by Google Scholar