Hi!
Side quests
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!
Research
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 work in the Graph Mining team at Google Research in Zürich.
My ORCID iD is 0000000332465323. And of course, no computer scientist can hide from dblp and Google Scholar.

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 kDisks,
Master's thesis. selfarchived version
Publications

W. He, H. Fichtenberger and P. Peng
“A Differentially Private Clustering Algorithm for WellClustered Graphs,”
to appear in 12th International Conference on Learning Representations (ICLR),
2024. 
H. Fichtenberger, M. Henzinger and J. Upadhyay
“Constant Matters: Finegrained Complexity of Differentially Private Continual Observation.”
in 40th International Conference on Machine Learning (ICML),
2023. conference version arXiv version 
B. A. Mayer, A. Tsitsulin, H. Fichtenberger, J. Halcrow and B. Perozzi
“HUGE: Huge Unsupervised Graph Embeddings with TPUs”
in 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),
2023. conference version 
M. Bateni, H. Esfandiari, H. Fichtenberger, M. Henzinger, R. Jayaram, V. Mirrokni and A. Wiese
“Optimal Fully Dynamic kCenter Clustering for Adaptive and Oblivious Adversaries”
in 34th Symposium on Discrete Algorithms (SODA),
2023. conference version arXiv version 
H. Fichtenberger and P. Peng
“Approximately Counting Subgraphs in Data Streams,”
in 41st Symposium on Principles of Database Systems (PODS),
2022. conference version arXiv version 
H. Fichtenberger, M. Henzinger and L. Ost
“Differentially Private Algorithms for Graphs Under Continual Observation,”
in 29th Annual European Symposium on Algorithms (ESA),
2021. conference version arXiv version 
H. Fichtenberger and A. Rey,
“Testing Stability Properties in Graphical Hedonic Games,”
in Autonomous Agents and MultiAgent Systems (JAAMAS),
2021. journal version 
H. Fichtenberger, S. Lattanzi, A. NorouziFard and O. Svensson
“Consistent kClustering for General Metrics,”
in 32nd Symposium on Discrete Algorithms (SODA),
2021, pp. 2660–2678. 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 IndividualBased 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 BoundedDegree 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 TheoryBased 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 TwoSided 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 OneSided 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 ConstantSize Graphs That Preserve the Local Structure of HighGirth 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 selfarchived version 
H. Fichtenberger, M. Gillé, M. Schmidt, C. Schwiegelshohn and C. Sohler,
“BICO: BIRCH Meets Coresets for kMeans Clustering,”
in 21st Annual European Symposium on Algorithms (ESA),
2013, pp. 481–492. conference version selfarchived version slides source code  show all…
Projects
Some of my personal or workrelated projects are available on GitHub or their respective project websites.
Klausuransicht
Klausuransicht, exam view, is a web app (texts in German only) that can be used to project information such as a clock, remaining time and rules during exams, e.g., at university. Once loaded, it runs locally in the browser.

BICO: kmeans 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 kmeans clusterings, more precisely, to compute kmeans 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 kmedian in streams
From the perspective of edit distance, kmeans and kmedian objectives look almost the same. However, algorithmically, the two problems are tackled quite differently. PROBI is an algorithm for kmedian 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 opensource vector graphics editor. I use it to draw posters and slides. When creating slides with overlays (stepbystep 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 inkscapeexportlayers 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.