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Machine Learning Section

 

Department of Computer Science (DIKU)

University of Copenhagen

 

Universitetsparken 1

2100 København N

Denmark

 

Room: 1-1-N110

 

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Fabian Gieseke

I am an Assistant Professor (tenure-track) for Large-Scale Data Science at the Department of Computer Science of the University of Copenhagen. I have received my Diploma degrees in Mathematics and Computer Science from the Westfälische Wilhelms-Universität Münster (Germany) and my PhD in Computer Science from the Carl von Ossietzky Universität Oldenburg (Germany). I am also responsible for the Industrial Data Analysis Service (IDAS) that aims at fostering collaborations between the Department of Computer Science and Danish companies.

               
 

My research interests lie in the area of large-scale data science, i.e., I work on efficiently analyzing huge amounts of data given limited compute resources. I work together with physicists to improve the classification accuracy of systems for detecting, e.g., new stars or distant galaxies by incorporating huge amounts of image data into the training phase of appropriate machine learning models. Other ongoing projects conducted together with researchers from the field of remote sensing aim at efficiently analyzing satellite images via, e.g., deep convolutional neural networks or specialized change detection algorithms. Nowadays, such tasks often involve the analysis of huge amounts of data in the tera- or even petabyte range and I work on overcoming such challenges by developing efficient schemes that are adapted to the specific needs of the tasks at hand. In particular, I make use of techniques and tools from the fields of high-performance computing (e.g., GPGPU programming) and distributed computing (e.g., Apache Spark) to reduce the practical runtime of the involved methods. In many cases, the original algorithmic building blocks are not suited for modern (massively-parallel) hardware architectures. The adaptation of known and the development of new methods that can effectively deal with huge amounts of data are part of my main research activities.

 

For an overview of past and current research projects, see here.

 

Selected Publications

  1. Fabian Gieseke and Christian Igel. Training Big Random Forests with Little Resources. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). 2018, Accepted.  draft 

  2. Fabian Gieseke, Steven Bloemen, Cas Bogaard, Tom Heskes, Jonas Kindler, Richard A Scalzo, Valerio A R M Ribeiro, Jan Roestel, Paul J Groot, Fang Yuan, Anais Möller, and Brad E Tucker. Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys. Monthly Notices of the Royal Astronomical Society (MNRAS), 2017.   

  3. Fabian Gieseke, Cosmin Oancea, and Christian Igel. bufferkdtree: A Python library for massive nearest neighbor queries on multi-many-core devices. Knowledge-Based Systems 120:1–3, 2017.   

  4. Fabian Gieseke, Tapio Pahikkala, and Tom Heskes. Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering. In Proceedings of the 14th International Symposium on Intelligent Data Analysis. Advances in Intelligent Data Analysis XIV 9385. 2015, 95–107.   

  5. Fabian Gieseke, Justin Heinermann, Cosmin Oancea, and Christian Igel. Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs. In Proceedings of the 31st International Conference on Machine Learning (ICML) 32(1). 2014, 172-180.   

  6. Fabian Gieseke, Tapio Pahikkala, and Christian Igel. Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification. In Proceedings of the 5th Asian Conference on Machine Learning (ACML). 2013, 62-71.   

  7. Tapio Pahikkala, Antti Airola, Fabian Gieseke, and Oliver Kramer. Unsupervised Multi-Class Regularized Least-Squares Classification. In Proceedings of the 12th IEEE International Conference on Data Mining (ICDM). 2012, 585-594.   

  8. Fabian Gieseke, Gabriel Moruz, and Jan Vahrenhold. Resilient K-d Trees: K-Means in Space Revisited. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM). 2010, 815-820.   

  9. Fabian Gieseke, Joachim Gudmundsson, and Jan Vahrenhold. Pruning Spanners and Constructing Well-Separated Pair Decompositions in the Presence of Memory Hierarchies. Journal of Discrete Algorithms (JDA) 8(3):259-272, 2010.   

  10. Fabian Gieseke, Tapio Pahikkala, and Oliver Kramer. Fast Evolutionary Maximum Margin Clustering. In Proceedings of the 26th International Conference on Machine Learning (ICML). 2009, 361-368.   

 

A complete list of my publications can be found here.

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