Lasse Leskelä

Personal profile

Artistic and research interests

My research area is stochastics, the mathematical theory of random events, systems, and processes. The main focus is statistical network models and random graphs, together with random dynamical systems and stochastic processes acting on networks. The general objective is to derive fundamental mathematical laws for predicting the macroscopic behavior of large random systems and describing the accuracy of statistical learning algorithms for large network models. Such laws allow to make predictions using massive high-dimensional data sets for which classical methods of statistics and machine learning are often infeasible. Statistical network models under study include random intersection graphs, stochastic block models, and graphons. The archetypes of stochastic dynamics studied are branching processes, random walks, and first passage percolation. The formulas and methods have a wide range of applications ranging from information and social networks to biological and financial systems.

Education/Academic qualification

Doctor of Science (Technology)

Master of Science (Technology), Engineering Physics

External positions

Professor (pro tem), University of Jyväskylä

1 Oct 2011 → 31 Jul 2012

University Lecturer, University of Jyväskylä

1 Jan 2011 → 31 Dec 2014

Assistant professor, Eindhoven University of Technology

1 Jun 2007 → 31 Dec 2007

Postdoctoral researcher, Centrum voor Wiskunde en Informatica

1 Jun 2006 → 31 May 2007

Visiting Scholar, Columbia University

1 Mar 2006 → 31 May 2006

Keywords

Probability theory
Stochastic processes
Random graphs
Statistical network models
Queueing Theory
Applied probability
Stochastics
Stochastic modeling

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Research Output

18 Article
11 Conference contribution
7 Working paper
1 Chapter

Assortativity and bidegree distributions on Bernoulli random graph superpositions

Bloznelis, M., Karjalainen, J. & Leskelä, L., 2 Jun 2020, Algorithms and Models for the Web Graph: 17th International Workshop, WAW 2020, Warsaw, Poland, September 21–22, 2020, Proceedings. Kamiński, B., Prałat, P. & Szufel, P. (eds.). Springer LNCS, p. 68-81 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12091).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Towards analyzing large graphs with quantum annealing

Reittu, H., Kotovirta, V., Leskelä, L., Rummukainen, H. & Räty, T., 24 Feb 2020, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. IEEE, p. 2457-2464 9006174

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Analysis of large sparse graphs using regular decomposition of graph distance matrices

Reittu, H., Leskelä, L., Räty, T. & Fiorucci, M., 22 Jan 2019, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. Song, Y., Liu, B., Lee, K., Abe, N., Pu, C., Qiao, M., Ahmed, N., Kossmann, D., Saltz, J., Tang, J., He, J., Liu, H. & Hu, X. (eds.). IEEE, p. 3784-3792 9 p. 8622118

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access