K
Kai Puolamäki
Researcher at University of Helsinki
Publications - 131
Citations - 2616
Kai Puolamäki is an academic researcher from University of Helsinki. The author has contributed to research in topics: Supersymmetry & Exploratory data analysis. The author has an hindex of 26, co-authored 122 publications receiving 2259 citations. Previous affiliations of Kai Puolamäki include Helsinki Institute of Physics & Helsinki Institute for Information Technology.
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The lightest neutral and doubly charged Higgs bosons of supersymmetric left-right models
TL;DR: In this article, the phenomenology of light Higgs scalars in supersymmetric left-right models was studied and the upper bound on the lightest CP-even neutral Higgs boson in these models was larger than in the minimal supersymmymmetric standard model and the Higgs couplings to fermions approach those of the Standard Model.
Journal ArticleDOI
Radiative symmetry breaking and the b→sγ decay in generalized GMSB models
TL;DR: In this paper, the authors studied a class of generalized models of gauge mediated supersymmetry breaking (GMSB) and found the parameters and the full particle spectrum of the minimal supersymmetric standard model for all GMSB models with messenger multiplicities that satisfy the perturbativity of the gauge couplings up to the GUT scale.
Journal ArticleDOI
SLISEMAP: supervised dimensionality reduction through local explanations
TL;DR: SliSEMAP as mentioned in this paper is a supervised manifold visualisation method that simultaneously finds local explanations for all data items and builds a (typically) two-dimensional global visualisation of the black box model such that data items with similar local explanations are projected nearby.
Posted ContentDOI
Machine learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets
Moritz Johannes Lange,Henri Johannes Suominen,Mona Kurppa,Leena Järvi,Emilia Oikarinen,Rafael Savvides,Kai Puolamäki +6 more
TL;DR: Regression models are used to replicate modelled air pollutant concentrations from LES in urban boulevards and it is shown that in general, log-linear regression gives the best and most robust performance on new independent data.
Book ChapterDOI
Semigeometric Tiling of Event Sequences
TL;DR: The task of finding combinations of temporal segments and subsets of sequences where an event of interest, like a particular hashtag, has an increased occurrence probability is formulated as a novel matrix tiling problem, and two algorithms for solving it are proposed.