This is the personal website of Hendrik Fichtenberger. It's the place where I collect various bits and pieces that I wrote, developed or like. If you'd like to get in touch, feel free to drop me an email!
I'm a computer scientist, and my interests lie in designing and analyzing algorithms for large data. My research originates in theoretical computer science, and I've worked on sublinear, dynamic, clustering and graph algorithms. However, I also enjoy to implement neat algorithms, and some of our papers have actually been implemented by my coauthors and me. I obtained my Ph.D. at TU Dortmund in the research group of my doctoral advisor Christian Sohler. Afterwards, I was as a postdoc in Monika Henzinger's group at the University of Vienna. Currently, I'm working at Google Zürich.
Property Testing of Graphs and the Role of Neighborhood Distributions,
Ph.D. thesis. official version slides
Explicit Upper Bounds on the Minimum Size of Planar Graphs That Satisfy a Given Distribution of k-Disks,
Master's thesis. self-archived version
H. Fichtenberger, M. Henzinger and W. Ost
“Differentially Private Algorithms for Graphs Under Continual Observation,”
in 29th Annual European Symposium on Algorithms (ESA),
2021. conference version arXiv version
H. Fichtenberger, A. Rey,
“Testing Stability Properties in Graphical Hedonic Games,”
in Autonomous Agents and Multi-Agent Systems (JAAMAS),
2021. journal version
H. Fichtenberger, S. Lattanzi, A. Norouzi-Fard and O. Svensson
“Consistent k-Clustering for General Metrics,”
in 32nd Symposium on Discrete Algorithms (SODA),
2021. conference version arXiv version slides
A. Czumaj, H. Fichtenberger, P. Peng and C. Sohler,
“Testable Properties in General Graphs and Random Order Streaming,”
in 24th International Conference on Randomization and Computation (RANDOM),
2020, pp. 16:1–16:20. conference version arXiv version slides
H. Fichtenberger, M. Gao and P. Peng,
“Sampling Arbitrary Subgraphs Exactly Uniformly in Sublinear Time,”
in 47th International Colloquium on Automata, Languages and Programming (ICALP),
2020, pp. 45:1–45:13. conference version arXiv version slides
H. Fichtenberger, A. Krivošija and A. Rey,
“Testing Individual-Based Stability Properties in Graphical Hedonic Games,”
in 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS),
2019, pp. 882–890. conference version arXiv version journal version
H. Fichtenberger, P. Peng and C. Sohler,
“Every Testable (Infinite) Property of Bounded-Degree Graphs Contains an Infinite Hyperfinite Subproperty,”
in 30th Symposium on Discrete Algorithms (SODA),
2019, pp. 714–726. conference version conference slides extended slides
H. Fichtenberger and D. Rohde,
“A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice,”
in 32nd Conference on Neural Information Processing Systems (NeurIPS, formerly NIPS),
2018, pp. 6743–6754. conference version arXiv version source code
H. Fichtenberger and Y. Vasudev,
“A Two-Sided Error Distributed Property Tester For Conductance,”
in 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS),
2018, pp. 19:1–19:15. conference version slides
H. Fichtenberger, R. Levi, Y. Vasudev, and M. Wötzel,
“A Sublinear Tester for Outerplanarity (and Other Forbidden Minors) With One-Sided Error,”
in 45th International Colloquium on Automata, Languages, and Programming (ICALP),
2018, pp. 52:1–52:14. conference version arXiv version slides poster
H. Fichtenberger, P. Peng, and C. Sohler,
“On Constant-Size Graphs That Preserve the Local Structure of High-Girth Graphs,”
in 19th International Workshop on Randomization and Computation (RANDOM),
2015, pp. 786–799. conference version slides
D. Siedhoff, H. Fichtenberger, P. Libuschewski, F. Weichert, C. Sohler, and H. Müller,
“Signal/Background Classification of Time Series for Biological Virus Detection,”
in 36th Annual German Pattern Recognition Symposium (GCPR),
2014, pp. 388–398. conference version self-archived version
H. Fichtenberger, M. Gillé, M. Schmidt, C. Schwiegelshohn, and C. Sohler,
“BICO: BIRCH Meets Coresets for k-Means Clustering,”
in 21st Annual European Symposium on Algorithms (ESA),
2013, pp. 481–492. conference version self-archived version slides source code
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ProjectsSome of my personal or work-related projects are available on GitHub or their respective project websites.
BICO: k-means coresets and clusterings in streams
Clustering is a method to group objects that are similar with respect to some property (e.g., color). BICO is a streaming algorithm to compute k-means clusterings, more precisely, to compute k-means coresets. This implementation is the experimental part's core of our paper on this topic. It is suited for production use and provides better solutions in less time than many other algorithms. You can download the C++ sources here. There also exists a Java implementation for MOA and an adaption of the C++ implementation for the R package stream.
CluE: a clustering library
There exist many clustering algorithms for various objectives. CluE is a C++ library that implements several clustering algorithms. It was funded by DFG.
- PROBI: probabilistic k-median in streams
From the perspective of edit distance, k-means and k-median objectives look almost the same. However, algorithmically, the two problems are tackled quite differently. PROBI is an algorithm for k-median for regular and probabilistic inputs. This is a proof of concept implementation. You can download the C++ sources here.
- FIBS: job scheduler using files to communicate
Sometimes when you perform the same experiment ten or a hundred times, you realize that a single machine won't do anymore. However, you're still at an early stage and you don't want to migrate to a computing cluster right now (maybe you should, but…). There might be a bunch of computers around that don't need any reservation or scripting of workload managers, but there's nothing that connects them but a shared folder somewhere in the local network or in the cloud.
FIBS is a Python script that reads a simple job file, distributes jobs to available workers, takes care of running the jobs and collects the results – all by just using the shared folder. It's probably not what you want to use at a large scale, but it's simple and easy to set up.
- Inkscape Export Overlays: export slides with overlays
Inkscape is a very cool, free and open-source vector graphics editor. I use it to draw posters and slides. When creating slides with overlays (step-by-step animations), there are typically a lot of layers that need to be activated or deactivated in order to export specific overlays. This extension, which is forked from the inkscape-export-layers extension by Jesús Espino and Xavier Julian, adds the ability to mark layers as active for a range of overlays and export all overlays automatically at once to, e.g., PDF or PDF+LaTeX.
- PROBI: probabilistic k-median in streams