Apulaisprofessori (tenure track) Lassi Roininen

E-mail: Lassi.Roininen@lut.fi

Mobile phone: +358406754885

ORCID ID: orcid.org/0000-0002-7014-6684 (Go to ORCID profile)

CV:

Uploaded on: 09/10/2020

Current Organisation

Computational and Process Engineering (Superior organisation: LUT School of Engineering Science)

Biography

Lassi Roininen holds the position of Associate Professor (tenure track) in applied mathematics in LUT University, Finland. He is also a docent in applied mathematics at the University of Oulu, Finland. He develops rigorous numerical and computational tools for inverse problems with applications in near-space remote sensing, subsurface imaging, and X-ray tomography.

Research Areas

Computational Mathematics

Geophysics

Numerical Analysis

Statistics and Probability

Projects as Principal Investigator

Sawing Optimization via Deep Learning and Multi-instrument Imaging
Sahauksen optimointi syväoppimisen ja useiden kuvantavien instrumenttien avulla (01/04/2020 - 31/03/2023)
Funder: Academy of Finland

Data Assimilation and Bayesian Inversion for Large-scale Atmospheric Models
Data-assimilaatio ja bayesilainen inversio laajan skaalan ilmakehämalleille (01/02/2020 - 31/12/2021)
Funder: Academy of Finland

Hypermodels and stable priors for Bayesian inversion with applications in ionospheric tomography and subsurface imaging (01/09/2018 - 31/08/2020)
Funder: Academy of Finland

Probabilistic deep learning via hierarchical stochastic partial differential equations (01/09/2018 - 31/12/2019)
Funder: Academy of Finland

Publications

1 of 4

A Bayesian-based approach to improving acoustic Born waveform inversion of seismic data for viscoelastic media (2020)
A1 Journal article (refereed), original research
Muhumuza Kenneth, Huttunen Janne, Roininen Lassi, et al.

Inverse Problems

Bayesian Quantification for Coherent Anti-Stokes Raman Scattering Spectroscopy (2020)
A1 Journal article (refereed), original research
Härkönen Teemu, Roininen Lassi, Moores Matthew T., et al.

Journal of Physical Chemistry B

Blind Hierarchical Deconvolution (2020)
A4 Conference proceedings
Arjas Arttu, Roininen Lassi, Sillanpää Mikko J, et al.

Enhancing industrial X-ray tomography by data-centric statistical methods (2020)
A1 Journal article (refereed), original research
Suuronen Jarkko, Emzir Muhammad, Lasanen Sari, et al.

Data-Centric Engineering

Neural Network Based Identification of Fuel Injection Rate Profiles for Diesel Engines (2020)
A4 Conference proceedings
Immonen Eero, Lauren Mika, Roininen Lassi, et al.
2020 9th International Conference on Industrial Technology and Management (ICITM)

Posterior inference for sparse hierarchical non-stationary models (2020)
A1 Journal article (refereed), original research
Monterrubio-Gómez Karla, Roininen Lassi, Wade Sara, et al.

Computational Statistics and Data Analysis

Accelerated Whole-Core Analysis Optimization With Wellsite Tomography Instrumentation and Bayesian Inversion (2019)
A1 Journal article (refereed), original research
Mendoza Alberto, Roininen Lassi, Girolami Mark, et al.

Petrophysics

Cauchy difference priors for edge-preserving Bayesian inversion (2019)
A1 Journal article (refereed), original research
Markkanen Markku, Roininen Lassi, Huttunen Janne M J, et al.

Journal of Inverse and Ill-posed Problems

Hyperpriors for Matérn fields with applications in Bayesian inversion (2019)
A1 Journal article (refereed), original research
Roininen Lassi, Girolami Mark, Lasanen Sari, et al.

Inverse Problems and Imaging

Statistical methods to enable practical on-site tomographic imaging of whole-core samples (2019)
A1 Journal article (refereed), original research
Mendoza Alberto, Roininen Lassi, Girolami Mark, et al.

Geophysics

Keywords

Incoherent scatter radar

Inverse problems

Random fields

Subsurface imaging

Tomography

Last updated on 2020-09-10 at 01:18