CV — Florence Bockting

Experiences

Research Scientist / Research Software Engineer

Bayesian Workflow Group, Computer Science Department

Aalto University, Espoo, Finland · PI: Prof. Dr. Aki Vehtari

Current
  • Postdoctoral research software engineer implementing Bayesian statistics in software, primarily within the Stan ecosystem (Python and R)
  • R package development: implementing novel research results in open-source software; improving, refactoring, and maintaining existing R packages

Research Scientist / Research Software Engineer

Climate Resource GmbH

Berlin, Germany

  • Research scientist and software engineer developing greenhouse-gas concentration data pipelines for earth system modelling groups
  • ESA-funded project: designed and implemented a data pipeline covering scraping, preprocessing, and assimilation of multi-source observational data (ground-based and satellite), resulting in a unified, standardised data product

Consultant

Jacobs Foundation, Zürich, Switzerland

  • Focus area: Expert prior elicitation
  • Prepare and inform about expert prior elicitation to support internal decision-making processes

Research Scientist

Computational Statistics, Statistics Department

TU Dortmund University, Dortmund, Germany · PI: Prof. Dr. Paul-Christian Bürkner

  • 2023 – 2025 TU Dortmund University
  • 2022 – 2023 SC SimTech, University of Stuttgart
  • Doctoral research in computational statistics on simulation-based expert prior elicitation
  • Developed a method for learning prior distributions in Bayesian models from expert knowledge; implemented the approach as the open-source Python package elicito (PyPI and conda-forge)

Research Scientist

Methods & Statistics, Psychology Department

Philipps-University Marburg, Marburg, Germany · PI: Prof. Dr. Daniel W. Heck

  • Focus areas: Statistical method development, computational and mathematical modelling, social psychology, formalisation of verbal theories
  • Formalised a verbal theory of truth judgment and the truth effect in social psychology by developing a computational model in R

Tutor & Student Assistant

  • Supported lecture preparation and led exercise seminars across a range of courses, including Data Ethics, Statistics, Computational Data Analysis, Bayesian Data Analysis, General Psychology, and Experimental Psychology

Research Assistant

Produkt+Markt GmbH, Osnabrück

  • Conducted qualitative and quantitative market research in the healthcare sector
  • Responsibilities included coding qualitative questionnaires, descriptive data analysis, and preparing presentations and reports

Project member – Student Skills Matching Platform

  • Contributed to the development of a platform for matching students based on skills; carried out a needs analysis and led the conceptual design of the matching system

Internships

  • Qualitative Market Research in Healthcare – Ipsos GmbH, Hamburg
  • General Psychology and Methodology (PI: Prof. Dr. Claus-Christian Carbon), University of Bamberg

Education

Doctorate in Computational Statistics

TU Dortmund University, Dortmund, Germany

Simulation-based expert prior elicitation: Method and software development

  • Doctoral thesis at SimTech / University of Stuttgart and TU Dortmund University
  • Awarded magna cum laude

M.Sc. in Cognitive Science

University of Osnabrück, Osnabrück, Germany

  • Majors: Cognitive modelling & Artificial intelligence
  • Graduated with distinction

B.Sc. in Business Psychology

University of Applied Sciences Harz, Wernigerode, Germany

  • Majors: Market research & Consumer behaviour
  • Graduated with distinction

Marketing Communications Specialist

Vocational training certified by IHK (Chamber of Industry & Commerce)

Dresden-Informatik GmbH, Dresden, Germany

Teaching & Thesis Supervision

Master Seminar on Multilevel Modelling

  • Target audience: Master students in Data Science, Statistics, and Econometrics
  • Introduction to the theory and analysis of multilevel models using R, from both Bayesian and frequentist perspectives
  • Language of instruction: English

Programming course: Introduction into Python

  • Target audience: Master and Bachelor students in Data Science, Statistics, and Econometrics
  • Fundamentals of Python, documentation with Sphinx, testing with Pytest, and version control with Git and GitHub
  • Language of instruction: English, German

Supervision of theses

  • BScAnalysis of different initialization approaches for hyperparameter optimization with mini-batch stochastic gradient descent: A simulation study · TU Dortmund
  • MScSensitivity analysis and performance evaluation of varying upper thresholds for discrete likelihoods · TU Dortmund
  • BScThe influence of response scales on the knowledge gain of underlying cognitive mechanisms: The role of uncertainty and truth perception in the Truth Effect · Philipps-Universität Marburg
  • BScEmpirical test of core assumptions of the Referential Theory: Influence of repetition on perceived coherence · Philipps-Universität Marburg
  • BScIdentification and testing of relevant psychological factors on truth judgments and the truth effect according to the Referential Theory · Philipps-Universität Marburg
  • BScTruth Effect — The role of the response scale in truth effect designs with short delay · Philipps-Universität Marburg

Publications & Talks

2026 Bockting, F. Simulation-based expert prior elicitation: Method and software development. Doctoral Thesis. TU Dortmund University.

2025 Bockting, F. Invited Talk: Predictive Prior Elicitation: State of the Art & Current Challenges, Workshop on Bayesian Modelling, Lund University

2025 Bockting, F. & Bürkner, P. C. elicito: A Python Package for Expert Prior Elicitation. arXiv preprint

2025 Bockting, F., Radev, S. T., & Bürkner, P. C. Expert-elicitation method for non-parametric joint priors using normalizing flows. Statistics and Computing, 35(5), 132.

2024 Bockting, F., Radev S. T., & Bürkner P. C. Contributed talk: Normalizing Flows for Simulation Based Expert Prior Elicitation. MathPsych

2024 Bockting, F., Radev S. T., & Bürkner P. C. Contributed talk: Simulation-Based Prior Knowledge Elicitation for Parametric Bayesian Models. ISBA

2024 Bockting, F., Radev, S. T., & Bürkner, P. C. Invited talk: Simulation Based Prior Knowledge Elicitation for Parametric Bayesian Models. Bayes@Lund

2024 Bockting, F., Radev, S. T. & Bürkner, P. C. Simulation-based prior knowledge elicitation for parametric Bayesian models. Scientific Reports 14, 17330.

2023 Heck, D. W., & Bockting, F. Benefits of Bayesian model averaging for mixed-effects modeling. Computational Brain & Behavior, 6(1), 35–49.

2023 van Doorn, J., …, Bockting, F. & Aust, F. Bayes factors for mixed models: A discussion. Computational Brain & Behavior, 6(1), 1–13.

2021 Bockting, F. & Heck, D. W. Measuring Individual Differences in the Truth Effect: A formal analysis. Fast Talk at MathPsych

2021 Stephan, A., …, Bockting, F., … Nachwort. In Turing A. M. Computing Machinery and Intelligence. Können Maschinen Denken? (pp. 131–201). Reclam.

Workshops & Courses

2026
  • Introduction to cybersecurity, FITech, University of Turku
  • Introductory penetration testing and security assessment, FITech, University of Jyväskylä
  • Developing Secure Software, OpenSSF, Linux Foundation
2024
  • Copyright for Computer Programs & Software, TU Dortmund University
  • Research Software Engineering Summer School, Karlsruhe Institute of Technology (KIT)
2023
  • Theory of Science, Prof. Dr. Zoglauer, University of Stuttgart
  • Foundations of Deep Learning for the Social Sciences, University of Tübingen
  • The Statistics Wars and Their Casualties (online seminar series), Prof. Dr. Deborah Mayo, Prof. Dr. Roman Frigg, & Prof. Dr. Margherita Harris
2022
  • Summer School on Advanced Bayesian Data Analysis with Stan, Dr. Bruno Nicenboim, University of Potsdam
  • Interval Hypothesis Testing, Prof. Dr. Daniël Lakens, University of Eindhoven
  • Robust Cognitive Bayesian Analysis, Prof. Dr. Jeffrey N. Rouder, University of California
  • Bayesian Evaluation of (informative) Hypotheses, Prof. Dr. Herbert Hoijtink, University of Utrecht
2021
  • Multinomial-Processing-Tree Modeling – Foundations and Recent Advances, Prof. Dr. Edgar Erdfelder & Prof. Dr. Daniel Heck, University of Mannheim
  • Single- vs. Dual-Process Theories, Prof. Dr. Mandy Hütter, University of Tübingen
  • Introduction into Bayesian Statistics, Prof. Dr. Daniel Heck, Philipps-University Marburg

Attended Conferences

MathPsych

Society for Mathematical Psychology · Tilburg

ISBA

International Society for Bayesian Analysis · Venice

DGP

German Society for Psychology · Leipzig

EGU

European Geosciences Union · Vienna

Bayes@Lund

Lund

Upcoming

StanCon

Stan Conference · Uppsala

Nordic-RSE Conference

Tromsø

Other Experiences

Carpentries lesson maintainer

Mentor, Google Summer of Code 2026 – R packages in the Stan ecosystem

Volunteer at KANA Dortmund (soup kitchen), Dortmund

Volunteer at a residential home for children and adolescents with disabilities, Lebenshilfe e.V.

Fellow of the Studienstiftung des deutschen Volkes (German Academic Scholarship Foundation), 2015 – 2020