Lasse Leskelä

Current teaching

• MS-E1624 High-Dimensional Statistics
  • Aalto University, spring 2025.

Study resources

• Suomi–English online dictionary of statistics
  • Suomen Tilastoseura (suggest a new entry)

Lecture notes

• Probability theory
  • Aalto University 2024
• Random graphs and network statistics
  • Aalto University 2019
• Stochastic processes
  • Aalto University 2020
• Stokastiikka ja tilastollinen ajattelu
  • Aalto University 2017–2024
• Stokastiset prosessit
  • Aalto University 2018
• Stokastiikan perusteet
  • University of Jyväskylä 2013

Past courses

• MS-E1600 Probability Theory
  • Aalto University, autumn 2024.
• MS-E1600 Probability Theory
  • Aalto University, spring 2024.
• MS-E1603 Random Graphs and Network Statistics
  • Aalto University, autumn 2023.
• MS-EV0016 Spectral Methods in Statistical Network Inference
  • Aalto University, spring 2023.
• MS-E1600 Probability Theory
  • Aalto University, spring 2023.
• MS-E1603 Random Graphs and Network Statistics
  • Aalto University, autumn 2022.
• MS-E1603 Random Graphs and Network Statistics
  • Aalto University, autumn 2021.
• MS-E1600 Probability Theory
  • Aalto University, spring 2021.
• MS-C2111 Stochastic Processes
  • Aalto University, autumn 2020.
• MS-E1603 Random Graphs and Network Statistics
  • Aalto University, autumn 2020.
• MS-E1600 Probability Theory
  • Aalto University, spring 2020.
• MS-C2111 Stochastic Processes
  • Aalto University, autumn 2019.
• MS-E1603 Random Graphs and Network Statistics
  • Aalto University, autumn 2019.
• MS-A0504 Todennäköisyyslaskennan ja tilastotieteen peruskurssi
  • Aalto University, spring 2019.
• MS-C2111 Stochastic Processes
  • Aalto University, autumn 2018.
• MS-E1603 Random Graphs and Network Statistics
  • Aalto University, autumn 2018.
• MS-E1995 Random graphs and network statistics
  • Aalto University, spring 2018.
• MS-A0501, MS-A0502 Todennäköisyyslaskennan ja tilastotieteen peruskurssi
  • Aalto University, fall 2017.
• MS-E1992 Asymptotic statistics
  • Aalto University, fall 2016 – spring 2017.
• MS-A0501, MS-A0502, MS-A0503 Todennäköisyyslaskennan ja tilastotieteen peruskurssi
  • Aalto University, fall 2016 – spring 2017.
• MS-E1602 Large random systems
  • Aalto University, spring 2016.
• MS-A0503 Todennäköisyyslaskennan ja tilastotieteen peruskurssi
  • Aalto University, spring 2016.
• MS-C2111 Stochastic processes
  • Aalto University, fall 2015.
• Mat-1.3601 Introduction to stochastics
  • Aalto University, spring 2015.
• MS-A0503 Todennäköisyyslaskennan ja tilastotieteen peruskurssi
  • Aalto University, spring 2015.
• MS-C2111 Stochastic processes
  • Aalto University, fall 2014.
• Mat-2.4125 Stochastic population dynamics
  • Aalto University, fall 2014.
• MATS260 Probability theory 1
  • University of Jyväskylä, spring 2014.
• MATA280 Foundations of stochastics
  • University of Jyväskylä, fall 2013.
• MATS453 Reading seminar on Markov processes
  • University of Jyväskylä, spring 2013.
• MATA123 Computational linear algebra and geometry
  • University of Jyväskylä, spring 2013.
• MATA261 Introduction to stochastics
  • University of Jyväskylä, spring 2013.
• MATA271 Stochastic models
  • University of Jyväskylä, fall 2012.
• MATS255 Markov processes
  • University of Jyväskylä, fall 2012.
• MATS262 Stochastics 1
  • University of Jyväskylä, spring 2012.
• MATA271 Stochastic models
  • University of Jyväskylä, fall 2011.
• MATS255 Markov processes
  • University of Jyväskylä, fall 2011.
• MATS352 Stochastic differential equations 1
  • University of Jyväskylä, spring 2011.
• MATS353 Stochastic differential equations 2
  • University of Jyväskylä, spring 2011.
• Mat-1.3610 Extremes of random phenomena
  • Helsinki University of Technology, fall 2009.
• 2WB05 Simulation
  • Eindhoven University of Technology, fall 2007.
• 2S610 Probability theory and stochastic processes
  • Eindhoven University of Technology, fall 2007.
• 2DD18 Statistics and stochastic operations research
  • Eindhoven University of Technology, fall 2007.
• Mat-1.3607 Stochastic optimization
  • Helsinki University of Technology, fall 2005.
• Mat-1.3601 Introduction to stochastics
  • Helsinki University of Technology, spring 2005.
• Mat-1.500 Martingale theory
  • Helsinki University of Technology, fall 2002.

DSc projects supervised

• Structure and estimation of network models with overlapping communities
  • J Karjalainen, Aalto University, 2021.
• On pathwise stochastic integration of processes with unbounded power variation
  • Z Chen, Aalto University, 2016.

MSc projects supervised

• Spectral clustering of random hypergraphs
  • I Välimaa, Aalto University, 2024.
• Community recovery with variational inference and stochastic block models
  • T Köse, Aalto University, 2023.
• Impact of air passengers on COVID-19 transmission
  • T Eklund, Aalto University, 2023.
• Connectivity of passive random intersection graphs and their intersection with Erdős–Rényi graphs
  • E Bergman, Aalto University, 2022.
• Consistent Bayesian community detection
  • K Alaluusua, Aalto University, 2021.
• Approximate Bayesian computation and compartmental metapopulation model to describe COVID-19 in Finland
  • A Ponce de León Chávez, Aalto University, 2021.
• Predicting residential property prices with decision tree methods
  • S Oksanen, Aalto University, 2021.
• Quantum Monte Carlo simulation of positron annihilation radiation in solids
  • J Härkönen, Aalto University, 2020.
• Estimation of daily batch balances of perishable products in retail
  • H Pantsar, Aalto University, 2019.
• Optimizing dense wi-fi deployments using Markov chain models and simulated annealing
  • A Pöllänen, Aalto University, 2019.
• A study of LDL aggregation and its correlation with the LDL lipidome and clinical data in bariatric surgery patients
  • M Mihaylova, Aalto University, 2019.
• Estimating energy consumption of electric heating pppliances in households
  • J Salmelin, Aalto University, 2019.
• Ruin theoretical comparisons
  • V Pekkanen, Aalto University, 2016.
• Convergence of estimators in large stochastic affiliation network models
  • J Karjalainen, Aalto University, 2015.
• Wattsin ja Strogatzin satunnaisverkkomallin klusteroituneisuus
  • H Meyer, University of Jyväskylä, 2015.
• Optional and predictable projection of a normal integrand
  • M Kiiski, Aalto University, 2014.
• Information spreading process in the complete graph
  • P Aalto, University of Jyväskylä, 2013.
• The simulated annealing convergence theorem
  • A Luoto, University of Jyväskylä, 2013.
• Discretization of the Langevin algorithm
  • L Liukkonen, University of Jyväskylä, 2012.
• Model constructions in stochastic geometry
  • M Kuronen, University of Jyväskylä, 2011.
• Cutoff phenomenon of a random walk on a permutation group
  • J Leskinen, University of Jyväskylä , 2011.
• Stochastic comparison of Markov queueing networks using coupling
  • L van den Berg, Utrecht University, 2010.

BSc projects supervised

• Experiments on reconstructing hypergraphs from network data
  • K Zhou, Aalto University, 2024.
• Yhteisöjen tunnistaminen moniulotteisesta Hawkes-prosessista
  • V Moilanen, Aalto University, 2024.
• Algorithms for playing cooperative partially observable games
  • S Peltonen, Aalto University, 2021.
• Matkamatriisien estimointi havaituista liikennevirroista
  • S Paukkonen, Aalto University, 2021.
• A parametric approach for clustering in dynamic-network: The dynamic stochastic block model
  • E Clemente, Aalto University, 2020.
• Transitiivisuus ja perkolaatio hyperbolisissa stokastisissa verkkomalleissa
  • M Runolinna, Aalto University, 2019.
• Epidemics on large CM connected graphs with active and passive nodes
  • K Fransen, Eindhoven University of Technology, 2018.
• Klikkien lukumäärät harvassa satunnaisessa leikkausverkossa
  • T Gröhn, Aalto University, 2018.
• Enemmistöäänestys ja iteratiiviset algoritmit binaarisissa joukkoistamistehtävissä
  • S Sorvari, Aalto University, 2019.
• Diskreetti momenttiongelma stokastisen lohkomallin estimoinnissa
  • J Lehtonen, Aalto University, 2018.
• Satunnaiskävelyt ja niiden kasvattamat puut
  • J Laukka, Aalto University, 2018.
• Stokastisen lohkomallin sovittaminen verkkoon mixer-algoritmilla
  • T Aaltonen, Aalto University, 2017.
• Suurten satunnaisten leikkausverkkojen simulointi ja estimointi
  • T Koskinen, Aalto University, 2017.
• Satunnaiset leikkausverkot
  • A Kallio, Aalto University, 2017.
• Kytkennät ja pullonkaulamenetelmät Markov-ketjujen sekoittumisaikojen määrittämisessä
  • A Suominen, Aalto University, 2015.
• Moniulotteiset Moran-prosessit
  • J Salmelin, Aalto University, 2015.
• Finanssiriskien stokastisia vertailumenetelmiä
  • S Turunen, Aalto University, 2015.
• Sijoitusportfolion riskimittojen ositus
  • H Pantsar, Aalto University, 2015.
• Erdősin–Rényin satunnaisverkon yhtenäisyys
  • P Kääriäinen, University of Jyväskylä , 2014.
• Fosterin lause
  • J Keppo, University of Jyväskylä, 2014.
• Satunnaisen leikkausverkon asteluvun jakauma
  • H Taattola, University of Jyväskylä, 2013.
• Pörssiosakkeen tarjouskirjan stokastinen dynamiikka
  • J Heino, University of Jyväskylä, 2012.
• Ahneesta satunnaiskävelystä
  • P Aalto, University of Jyväskylä, 2012.
• Google's PageRank
  • A Batra, Indian Institute of Technology Guwahati, 2009.
• The convergence of a scaled Poisson shot noise process: The Lévy representation approach
  • AP Perkkiö, Helsinki University of Technology, 2004.
  Updated: 2024-12-13. URL http://math.aalto.fi/~lleskela/