Sara Taskinen

Apulaisprofessori (Associate Professor)

Yksikkö

Matemaattis-luonnontieteellinen tiedekunta

Laitos / Vastuualue

Matematiikan ja tilastotieteen laitos

Huonenumero

Ag B312.5

Sähköposti

sara.l.taskinen@jyu.fi

Matkapuhelin

+358408053466

Postiosoite

Mattilanniemi 2

Tieteenalat

112 Tilastotiede

Esittely

Tutkin ja kehitän tilastotieteen menetelmiä, joiden avulla erilaisia moniulotteisia ja kompleksisia aineistoja voidaan analysoida tehokkaammin. Menetelmiä voidaan soveltaa vaikkapa ekologian ja aivokuvantamisen aineistoihin.

Tutkimuskuvaus

Julkaisut

Julkaisu

2026

A critical comparison of handling zeros in high-dimensional compositional count data

Chemometrics and Intelligent Laboratory Systems.

Tang, Wenqi

Fačevicová, Kamila

Nordhausen, Klaus

Taskinen, Sara

Julkaisu

2026

Approximating prediction error variances and reliabilities in a multiple-trait genomic prediction model using Monte Carlo sampling

Theoretical and Applied Genetics.

Heikkilä, Antero

Strandén, Ismo

Lidauer, Martin H.

Nordhausen, Klaus

Taskinen, Sara

Julkaisu

2026

Brown Trout Abundance in Boreal Streams With Large Wood

Ecology of Freshwater Fish.

Koski, Alisa

Taskinen, Sara

Väätäinen, Reetta

Syrjänen, Jukka Tapani

Julkaisu

2026

Discussion to the paper “Independent component analysis by robust distance correlation”

Advances in Data Analysis and Classification.

Nordhausen, Klaus

Radojičić, Una

Saarela, Perttu

Taskinen, Sara

Julkaisu

2026

Effect of smoking and smoking cessation intervention on postoperative complications of orthopedic and hand surgery: A single-center interventional study

Tobacco Prevention and Cessation.

Kyrö, Antti

Kainiemi, Kalle

Malmivaara, Antti

Taskinen, Sara

Laatikainen, Tiina

Julkaisu

2026

Enhancing Identifiable Variational Autoencoder in the Presence of Missing Values and Auxiliary Covariates for Spatio‐Temporal Ozone Modeling

Environmetrics.

Sipilä, Mika

De Iaco, Sandra

Cappello, Claudia

Nordhausen, Klaus

Palma, Monica

Taskinen, Sara

Julkaisu

2026

Identifiable variational autoencoders for interpretable dimension reduction and prediction in multivariate spatio-temporal processes

Spatial Statistics.

Sipilä, Mika

Cappello, Claudia

De Iaco, Sandra

Nordhausen, Klaus

Palma, Monica

Taskinen, Sara

Julkaisu

2026

Independent vector analysis : an introduction for statisticians

Statistics Surveys.

Arvila, Miro

Nordhausen, Klaus

Sipilä, Mika

Taskinen, Sara

Julkaisu

2026

Monte Carlo sampling to approximate prediction error variances and reliabilities in multiple-trait genomic models

World Congress on Genetics Applied to Livestock Production. World Congress on Genetics Applied to Livestock Production Digital Archive. Iowa State University Digital Press.

Heikkilä, A.

Strandén, I.

Lidauer, M.

Nordhausen, K.

Taskinen, S.

Julkaisu

2026

Time‐Aware UNet and Super‐Resolution Deep Residual Networks for Spatial Downscaling

Environmetrics.

Sipilä, Mika

Maggio, Sabrina

De Iaco, Sandra

Nordhausen, Klaus

Palma, Monica

Taskinen, Sara

Sara Taskinen

Esittely

Tutkimuskuvaus

Julkaisut

A critical comparison of handling zeros in high-dimensional compositional count data

Approximating prediction error variances and reliabilities in a multiple-trait genomic prediction model using Monte Carlo sampling

Brown Trout Abundance in Boreal Streams With Large Wood

Discussion to the paper “Independent component analysis by robust distance correlation”

Effect of smoking and smoking cessation intervention on postoperative complications of orthopedic and hand surgery: A single-center interventional study

Enhancing Identifiable Variational Autoencoder in the Presence of Missing Values and Auxiliary Covariates for Spatio‐Temporal Ozone Modeling

Identifiable variational autoencoders for interpretable dimension reduction and prediction in multivariate spatio-temporal processes

Independent vector analysis : an introduction for statisticians

Monte Carlo sampling to approximate prediction error variances and reliabilities in multiple-trait genomic models

Time‐Aware UNet and Super‐Resolution Deep Residual Networks for Spatial Downscaling

Monimuuttujamenetelmät

Tilastollinen ekologia: Latenttimuuttujamallit monimutkaisille datarakenteille