Samuel Wiqvist

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Timeline

Work experience

Senior Data Scientist, Center For Surgical Science, Department of Surgery, Zealand University Hospital

Feb 2024 – now

Data Scientist, Danske Bank

Nov 2021 – Dec 2023

Teaching Assistant, Lund University

Sep 2016 – Mar 2021

Education

Ph.D. Mathematical Statistics,
Thesis: Simulation-based Inference: From Aprproximate Bayesian Computation and Particle Methods to Density Estimation,
Lund University, Sweden

Sep 2021

MsC in Engineering, Engineering Mathematics
Master thesis: An Adaptive Iterated Filtering Algorithm
Focus: Statistics & Finance
Lund University, Sweden

Jul 2016

Courses

Advanced Topics in Machine Learning: Computational Tools for Machine Learning in Python,
Technical University of Denmark

Introduction to Deep Learning,
Lund University, Sweden

Bayesian Statistics,
University of Copenhagen

Publications

PEPSDI: Scalable and flexible inference framework for stochastic dynamic single-cell models,
Fourth author, PLOS Computational Biology. 2022

19 May 2022

Efficient inference for stochastic differential mixedeffects
models using correlated particle pseudo-marginal algorithms,
First author, Computational Statistics & Data Analysis

May 2021

Partially exchangeable networks and architectures for
learning summary statistics in approximate Bayesian computation,
First author, International Conference on Machine
Learning 2019 May 24 (pp. 6798-6807). PMLR.

24 May 2019

Preprints

Sequential Neural Posterior and Likelihood Approximation,
First author, ArXiv preprint

5 Jun 2021

Sequential Neural Posterior and Likelihood Approximation,
First author, arXiv preprint arXiv:1806.05982.

15 Jun 2018

Computer projects

Code for the paper Sequential Neural Posterior and Likelihood Approximation
language: Python, frameworks: PyTorch, matplotlib, Jupyter notebooks, link: https://github.com/SamuelWiqvist/snpla

Code for the paper Efficient inference for stochastic differential mixed-effects models using correlated
particle pseudo-marginal algorithms,
language: Julia/R,
link: https://github.com/SamuelWiqvist/efficient SDEMEM

Code for the paper Partially Exchangeable Networks and Architectures for Learning Summary Statistics
in Approximate Bayesian Computation
language: Julia, framework: Knet, link: https://github.com/SamuelWiqvist/PENs-and-ABC

Code for the paper Accelerating delayed-acceptance Markov chain Monte Carlo algorithms language: Julia, link: http://www.github.com/SamuelWiqvist/adamcmcpaper

Implementation of some Approximate Bayesian Computation algorithms language Julia,
link: http://www.github.com/SamuelWiqvist/ApproximateBayesianComputation.jl

Jupyter notebook with some simple likelihood-free examples
language: Julia, framework: Jupyter
www.github.com/SamuelWiqvist/introlikelihoodfree