Konstantin Kutzkov


London School of Economics and Political Science (LSE)

Department of Statistics

Room: COL 7.13

Emails: kutzkov_at_gmail.com and k.k.kutzkov_at_lse.ac.uk

Welcome to my homepage. I am a postdoctoral research fellow in data science at the Department of Statistics at LSE working with Milan VojnovicMy research interests are in design and analysis of algorithms for massive data mining problems. Before starting my PhD, I had also worked on exact algorithms for hard combinatorial problems.

I am originally from Sofia, Bulgaria. I studied Computer Science and Mathematics at LMU München and I obtained my PhD in Computer Science from IT University of Copenhagen under the supervision of Rasmus PaghMy PhD thesis is entitled New Sampling and Sketching Techniques for Data Stream MiningDuring my PhD studies I interned at Yahoo Labs in Barcelona where I was hosted by Francesco Bonchi. After my PhD, I was a research scientist at NEC Laboratories Europe where I was part of the Network Data Analytics group. You can visit my Linkedin profile for more details on my education and professional experience.

Below is a list with my publications:

Journal publications
Laurent BulteauVincent FroeseKonstantin KutzkovRasmus Pagh
Triangle counting in dynamic graph streams
Algorithmica, to appear
(Extends SWAT 2014 paper with the same title)

Alexander Golovnev, Konstantin Kutzkov
New exact algorithms for the 2-constraint satisfaction problem
Theoretical Computer Science 526, 2014

Konstantin Kutzkov 
An exact algorithm exponential time algorithm for counting bipartite cliques
Information Processing Letters 112(13), 2012

Konstantin Kutzkov 
New upper bound for the #3-SAT problem
Information Processing Letters 105(1), 2007

Conference proceedings

Tian GuoKonstantin Kutzkov, Mohamed Ahmed, Jean-Paul Calbimonte, Karl Aberer 
Efficient Distributed Decision Trees for Robust Regression
Machine Learning and Knowledge Discovery in Databases - European Conference (ECML/PKDD), 2016

Mathias NiepertMohamed Ahmed, Konstantin Kutzkov 
Learning Convolutional Networks for Graphs
33rd International Conference on Machine Learning (ICML), 2016

Konstantin KutzkovMohamed Ahmed, Sofia Nikitaki 
Weighted similarity estimation in data streams
24th ACM International Conference on Information and Knowledge Management (CIKM), 2015

Konstantin Kutzkov, Rasmus Pagh 
Triangle counting in dynamic graph streams 
14th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT), 2014

Konstantin Kutzkov, Rasmus Pagh 
Consistent subset sampling 
14th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT), 2014

David García-Soriano, Konstantin Kutzkov 
Triangle counting in streamed graphs via small vertex covers 
14th SIAM International Conference on Data Mining (SDM), 2014

Konstantin Kutzkov, Albert Bifet, Francesco Bonchi, Aristides Gionis 
STRIP: Stream Learning of Influence Probabilities 
19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013

Konstantin Kutzkov 
Deterministic algorithms for skewed matrix products
30th International Symposium on Theoretical Aspects of Computer Science (STACS), 2013

Konstantin Kutzkov, Rasmus Pagh
On the Streaming Complexity of Computing Local Clustering Coefficients
6th ACM conference on Web Search and Data Mining (WSDM), 2013

Andrea Campagna, Konstantin Kutzkov, Rasmus Pagh 
On Parallelizing Matrix Multiplication by the Column-Row Method
15th Meeting on Algorithm Engineering and Experiments (ALENEX), 2013

Konstantin Kutzkov 
Improved Counter Based Algorithms for Frequent Pairs Mining in Transactional Data Streams
Machine Learning and Knowledge Discovery in Databases - European Conference (ECML/PKDD), 2012

Alexander S. Kulikov, Konstantin Kutzkov 
New Bounds for MAX-SAT by Clause Learning 
Second International Symposium on Computer Science in Russia (CSR), 2007

Submitted papers

Konstantin Kutzkov, Mathias Niepert, Mohamed Ahmed
Scalable Regression Tree Learning in Data Streams

Note that the above papers are provided only for personal use for research or educational purposes. The copyrights belong to the respective publishers.