Main Focus

  • statistical modeling
  • reproducible research workflows
  • machine learning for nested/hierarchical data
Technical Skills
  • data analysis in R, Julia, and Python
  • developing software with version control, unit tests, and continues integration
  • typesetting in Latex, HTML and CSS

Curriculum Vitae

Academic Track

2023-now Group leader of “Formal Methods in Lifespan Psychology” (Max Planck Institute for Human Development)

2021-2023 Fellow “Computational Methods in Psychiatry and Ageing Research” (International Max Planck Research School based at MPIB and UCL)

2019-2020 Student Research Assistant (Formal Methods in Lifespan Psychology | Max Planck Institute for Human Development)

2018 Student Research Assistant (Emmy Noether Research Group “Adaption to major life events”)

2017-2019 Student Research Assistant (Department of Diagnostics | Humboldt-Universität zu Berlin)

Education

2020-2023 Research doctorate (Max Planck Institute for Human Development)

    • Topic: “Towards Transparency and Open Science: A Principled Perspective on Computational Reproducibility and Preregistration”
    • Supervisors: Prof. Dr. Ulman Lindenberger, Prof. Dr. Andreas M. Brandmaier
    • summa cum laude (“with highest honors,” numerical grade < 1.0)

    2018-2020 M.Sc. Psychology (Humboldt-Universität zu Berlin)

      • Grade 1.1 (very good)
      • Track: Psychological Research Methods and Diagnostics
      • Thesis: “Automatic Reproducibility made simple: Automating reproducible research workflows”
      • Supervisors: Prof. Manuel Voekle, Prof. Dr. Andreas M. Brandmaier (grade 1.0)

      2015-2018 B.Sc. Psychology (Humboldt-Universität zu Berlin)

        • Grade 1.5 (very good)
        • Thesis: “Data Driven Development Diagnostics”
        • Supervisors: Prof. Matthias Ziegler, Dr. Martin Hecht (grade 1.0)

        2009-2015 Abitur (Gymnasium Villa Elisabeth)

        • Grade 1.2

        Honors and Funding

        2019-2020 Scholarship (German Academic Scholarship Foundation “Studienstiftung”)

        2014 “Jugend forscht” Award (regional—informatic/mathematics)

        Peer-reviewed Journal Articles
        1. Ernst, M. S., Peikert, A., Brandmaier, A. M., & Rosseel, Y. (2022). A Note on the Connection Between Trek Rules and Separable Nonlinear Least Squares in Linear Structural Equation Models. Psychometrika. https://doi.org/10.1007/s11336-022-09891-5
        2. Friemelt, B., Bloszies, C., Ernst, M. S., Peikert, A., Brandmaier, A. M., & Koch, T. (2022). On the Performance of Different Regularization Methods in Bifactor-(S-1) Models with Explanatory Variables—Caveats, Recommendations, and Future Directions. Structural Equation Modeling: A Multidisciplinary Journal, 0(0), 1–14. https://doi.org/10.1080/10705511.2022.2140664
        3. Peikert, A., van Lissa, C. J., & Brandmaier, A. M. (2021). Reproducible Research in R: A Tutorial on How to Do the Same Thing More Than Once. Psych, 3(4), 836–867. https://doi.org/10.3390/psych3040053
        4. Van Lissa, C. J., Brandmaier, A. M., Brinkman, L., Lamprecht, A.-L., Peikert, A., Struiksma, M. E., & Vreede, B. M. I. (2021). WORCS: A workflow for open reproducible code in science. Data Science, 4(1), 29–49. https://doi.org/10.3233/DS-210031
        5. Peikert, A., & Brandmaier, A. M. (2021). A Reproducible Data Analysis Workflow. Quantitative and Computational Methods in Behavioral Sciences, 1, e3763. https://doi.org/10.5964/qcmb.3763
        6. Ziegler, M., & Peikert, A. (2018). How Specific Abilities Might Throw “g” a Curve: An Idea on How to Capitalize on the Predictive Validity of Specific Cognitive Abilities. Journal of Intelligence, 6(3), 41. https://doi.org/10.3390/jintelligence6030041
        Conference Talks / Workshops
        1. Aaron Peikert. (2023, December 19). A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker. Banco de Portugal | Workshop on automating the research process.
        2. Aaron Peikert, & Hannes Diemerling. (2023, September 27). Reproducible Research in R | How to do the same thing more than once. Herbstakademie Forschungsdatenzentrum am Institut zur Qualitätsentwicklung im Bildungswesen. https://aaronpeikert.github.io/repro-workshop/self-paced/
        3. Aaron Peikert, Maximilian S. Ernst, Moritz Ketzer, & Hannes Diemerling. (2023, September 11). Introduction to Julia | A fresh approach to scientific computing. Ludwig Maximilian University of Munich | Open Research Hybrid Summer School 2023, Munich. https://formal-methods-mpi.github.io/Workshop.jl/stable/
        4. Aaron Peikert, & Elisabeth Buchberger. (2023, June 22). Reproducible Research in R | How to do the same thing more than once. Society for the Improvement of Psychological Science Conference 2023. https://aaronpeikert.github.io/repro-workshop/self-paced/
        5. Aaron Peikert, & Andreas M. Brandmaier. (2022, September 14). Why does preregistration increase the persuasiveness of evidence? A Bayesian rationalization. 52. DGPs-Kongress, Hildesheim.
        6. Maximilian S. Ernst, & Aaron Peikert. (2022, September 14). StructuralEquationModels.jl - a Fast and Flexible SEM Framework. 52. DGPs-Kongress, Hildesheim.
        7. Aaron Peikert. (2022, August 29). Automating Reproducibility - Challenges and what it takes to meet them. 5th Symposium and Advanced Course on Computational Psychiatry and Ageing Research of the Max Planck UCL Centre.
        8. Ernst, M. S., & Peikert, A. (2022, May 12). Writing Extensible Software for Researchers - Principles and an Example in Julia. Future Opportunities for Software in Research, Max Planck Institute for Evolutionary Biology.
        9. Peikert, A., & Ernst, M. S. (2022, May 12). Automating Reproducibility - Challenges and what it takes to meet them. Future Opportunities for Software in Research, Max Planck Institute for Evolutionary Biology.
        10. Peikert, A., M. Brandmaier, A., & Caspar J. Van Lissa. (2021, September 15). Preregistration as Code. Research Synthesis & Big Data Conference 2021.
        11. Ziegler, M., & Peikert, A. (2021, July 12). Artificial Intelligence and Psychological Assessment: Between Revolution and Reality. ITC Colloquium 2021.
        12. Peikert, A., & M. Brandmaier, A. (2021, May 18). Reproducibility in Big Data with the repro package. Research Synthesis & Big Data Conference 2021.
        13. Ziegler, M., & Peikert, A. (2019, September 17). Symposium “Challenges in and Approaches to Behavioral Assessment & Prediction”. DPPD 2019.
        Teaching

        2022-2023 Multivariate Statistics for Psychologists (Humboldt-Universität zu Berlin)

        2021-2022 Advanced Statistical Methods for Psychologists (Medical School Berlin)

        22.09.2022 Half-day workshop “Introduction to reproducibility” (Berlin|Oxford Summer School on Open Research)

        18.03.2021 Half-day workshop on Docker and Make (Max Planck Institute for Human Development)

        29.03.2020 Two-day workshop on Reproducible Research (Universitätsklinikum Hamburg-Eppendorf/Universität Hamburg)

        20.02.2020 Full-day workshop “Reproducibility” with Andreas Brandmaier (Max Planck UCL Centre for Computational Psychiatry and Ageing Research)


        Software
        1. Ernst, M. S., & Peikert, A. (2022). StructuralEquationModels.jl [Manual]. https://github.com/StructuralEquationModels/StructuralEquationModels.jl/
        2. Peikert, A., & Ernst, M. S. (2022). StenoGraphs.jl: A concise language to write meta graphs [Manual]. https://github.com/aaronpeikert/StenoGraphs.jl
        3. Peikert, A., Brandmaier, A. M., & van Lissa, C. J. (2021). Repro: Automated setup of reproducible workflows and their dependencies [Manual]. https://github.com/aaronpeikert/repro
        4. van Lissa, C. J., Peikert, A., & Brandmaier, A. M. (2021). Worcs: Workflow for open reproducible code in science [Manual]. https://CRAN.R-project.org/package=worcs
        Invited Research Visits

        2023 Visting the LMU Open Science Center (Ludwig-Maximilians-University Open Science Center)

          • Workshop on Computational Reproducibility with RMarkdown, Docker, GitHub Actions
          • Talk on Preregistration for the Munich ReproducibiliTea
          • Visiting Prof. Felix Schönbrodt (Managing director of the LMU Open Science Center)

          2023 Short term visiting scholar Prof. Dr. Ray Dolan (University College London)

            • Workshop on Scientific Computing in Julia
            • Talk on L0 Regularization
            • Talk on Open Science

            2023 Visiting MPI CBS Open Science Initiative (Max Planck Institute for Human Cognitive and Brain Sciences)

            • Talk on Open Science

            2021 Visiting Prof. Dr. Caspar van Lissa (Professor for Data Science) (University of Utrecht)

            2021 Visiting Prof. Dr. Yves Rosseel (Professor for Data Science) (University of Gent)

            Engagement

            2021-now Representative of the Human Sciences (Max Plank Open Science Forum)

            2020-now Statistical Consultant (Methods Group Berlin, Humboldt Universität zu Berlin)

            2019-2020 Coorganizer of “μΣ” Reading Club on Statistical Modelling and Philosophy of Science (pmOne Group)

            2017-2019 Representing the student body in: (Humboldt-Universität zu Berlin)

            • three habilitations
            • two appointment processes

            17.01.2020-2020 Two-year Collaborative Project on Information Theory (German Academic Scholarship Foundation “Studienstiftung”)

            Experiences

            2017 Internship (ROC Institute GmbH)

            • developing and implementing production ready machine learning algorithms to determine most profitible human resource interventions on individual and group level

            2015-2016 Product Owner/Startup Founder (ShareLock)

            • leading prototype development
            • supervision of small team (front-end & back-end developer, system architect, designer)

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