Skills

A brief summary of my skills and experience.

Applied statistics/Data science

Time-series modelling (statistical and machine learning models).
Bayesian and frequentist statistics (regression, hierarchical models, hypothesis testing)
Extreme value analysis (from 1 to 10000 years return period events, stationary and non-stationary processes, GEV, GP, etc. with special emphasis on fitting models to relatively small datasets using Bayesian statistics).
Deep learning: regression and image analysis (CNN).
Risk assessment and decision theory.

Python: numpy, numba, xarray, scipy, sklearn, pandas, matplotlib, seaborn, pyMC3, nestle, emcee, dynesty, pyABC, tensorflow, keras, pytorch, GPyTorch, ax, jMetalPy, OpenTURNS, catboost, lightgbm, sphinx, requests, flask, jinja2, etc., functional and object oriented programming, and code quality and package development tools.
Julia: Flux.jl, DifferentialEquations.jl, DiffEqFlux.jl, Turing.jl, NLOpt.jl, GalacticOptim.jl, Zygote.jl, Test.jl, Pluto.jl, etc., and code quality and package development tools.
R: dplyr, rstan, bmle, evd, parallel, shiny, ggplot2, leaﬂetr, ggmap, renv, tibble, readr, etc.
MATLAB: Parallel Computing Toolbox, Statistics Toolbox, Neural Network Toolbox, Optimization Toolbox, FERUM, UQLab, Advanpix, etc.
AWS (EC2, S3), Azure (VM, DevOps, Logic Apps, Storage), Heroku.
Git (GitLab, GitHub (both with CI/CD)), Docker, Jenkins, Terraform.
SQL, PostgreSQL.
GIS (QGIS).
css, html, jekyll.
ANSYS, MIDAS Civil, OpenSees, Diana, Atena, AxisVM, ConSteel.
Linux, LaTeX, Microsoft Office products, AutoCAD, Adobe Illustrator.

Optimization: gradient-based (using finite differencing and algorithmic differentiation) and gradient-free methods as well, e.g. Newton-Raphson, Nelder-Mead, BFGS, COBYLA, stochastic gradient descent, Bayesian optimization (with surrogates), heuristic methods, multi-objective optimization, etc.
Finite element method for solving PDEs.
Computational methods for Bayesian parameter estimation and model selection (asymptotically exact and approximate methods for computationally demanding likelihood functions, MH-MCMC, variational Bayes (ADVI), nested sampling, NUTS, affine invariant ensemble sampling).
Scientific machine learning (combining scientific models (e.g. in the form ODEs and PDEs) with machine learning models (e.g. neural networks)).
Surrogate modelling (meta modelling) of computationally demanding models (e.g. computational physics simulations).

Technical lead and research proposal writing for >1 million € budget projects.
Leading and managing multiple research teams of about 3-5 people.
Planning and providing internal educational activities to colleagues on: software development, programming, applied statistics, risk and reliability analysis.
Mentoring and supervising MSc students (>30).