Contact
Head
Secretary
Postal address
Maps and directions
Dr. Daniel Kristanto
Research Interests
- Interpretable artificial intelligence in neuroimaging research
- Meta-science
- Structural equation modeling
Academic positions
| Since 03/2025 | Postdoctoral Researcher at the Department of Psychology, Division for Psychological Methods and Statistics, Carl von Ossietzky Universität Oldenburg. (Funded by Carl von Ossietzky Young Researchers’ Fellowship) |
| 04/2022 – 02/2025 | Postdoctoral Researcher at the Department of Psychology, Division for Psychological Methods and Statistics, Carl von Ossietzky Universität Oldenburg. (Funded by DFG Priority Program: META-REP) |
| 07/2022 – 01/2023 | Research Fellow, Joint Research Fellowship between HWK (Hanse-Wissenschaftskolleg) and Faculty of Medicine, Carl von Ossietzky Universität Oldenburg |
| 09/2021– 02/2022 | Postdoctoral Researcher at the Department of Physics, Hong Kong Baptist University |
Education
09/2018 – 08/2021
| PhD Student at the Department of Physics, Hong Kong Baptist University |
| 01/2016 – 10/2017 | Master of Science at Sirindhorn International Institute of Technology, Thammasat University, Thailand |
| 09/2011 – 05/2015 | Bachelor of Engineering at Gadjah Mada University, Indonesia |
Publications
Wang, R., Chang, Z., Liu, X., Kristanto, D., Gartner, E. G. G., Liu, X., Liu, M., Wu, Y., Lui, M., & Zhou, C. (2025). Weak but influential: Nonlinear contributions of structural connectivity to human cognitive abilities and brain functions. arXiv. https://doi.org/10.48550/arXiv.2505.24125
Leung, A. Y.*, Kristanto, D.*, Gießing, C., Ioannidis, J.P.A., Hildebrandt, A., & Schmalz, X. (2025). Multiverse of developmental dyslexia subtyping methods: A Shiny app for analytical decision-making. https://doi.org/10.1101/2025.07.23.25332032. (*shared first authorship).
Short, C. A., Hildebrandt, A., Bosse, R., Debener, S., Özyağcılar, M., Paul, K., Wacker, J., & Kristanto, D. (2025). Lost in a Large EEG Multiverse? Comparing Sampling Approaches for Representative Pipeline Selection. Neuroscience. https://doi.org/10.1101/2025.04.08.647779.
Short, C., Breznau, N., Bruntsch, M., Burkhardt, M., Busch, N., Cesnaite, E., Frank, M., Gießing, C., Krähmer, D., Kristanto, D., Lonsdorf, T. B., Neuendorf, C., Nguyen, H. H. V., Rausch, M., Schmalz, X., Schneck, A., Tabakci, C., & Hildebrandt, A. (2025). Multi-curious: A Multi-Disciplinary Guide to Multiverse Analysis. MetaArXiv. https://doi.org/10.31222/osf.io/4yzeh_v1.
Leung, A. Y., Kristanto, D., & Schmalz, X. (2025). Re-SearchTerms: A Shiny app for exploring terminology variations in psychology and metascience. OSF. https://doi.org/10.31219/osf.io/qsp7x_v2.
Jacobsen, N. S. J., Kristanto, D., Welp, S., Inceler, Y. C., & Debener, S. (2025). Preprocessing choices for P3 analyses with mobile EEG: A systematic literature review and interactive exploration. Psychophysiology, 62, e14743. https://doi.org/10.1111/psyp.14743
Burkhardt, M., Hildebrandt, A., Gießing, C., & Kristanto, D. (2024). Quantifying Similarity between Graph-Theoretic Resting-State fMRI Data Processing Pipelines for Efficient Multiverse Analysis. Brainiacs Journal of Brain Imaging And Computing Sciences, 5, Issue 2 Edoc. https://doi.org/10.48085/XEE8F298E
Kristanto, D., Burkhardt, M., Thiel, C. M., Debener, S., Gießing, C., & Hildebrandt, A. (2024). The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis. Neuroscience & Biobehavioral Reviews, 105846. https://doi.org/10.1016/j.neubiorev.2024.105846
Kristanto, D., Gießing, C., Marek, M., Zhou, C., Debener, S., Thiel, C., & Hildebrandt, A. (2023, October). An Extended Active Learning Approach to Multiverse Analysis: Predictions of Latent Variables from Graph Theory Measures of the Human Connectome and Their Direct Replication. In 2023 Guardians Workshop (Guardians) (pp. 1-13). IEEE. https://doi.org/10.48085/J962E0F53
Kristanto, D., Hildebrandt, A., Sommer, W., & Zhou, C. (2023). Cognitive abilities are associated with specific conjunctions of structural and functional neural subnetworks. NeuroImage, 279, 120304. https://doi.org/10.1016/j.neuroimage.2023.120304
Kristanto, D., Liu, X., Sommer, W., Hildebrandt A., & Zhou, C. (2021). What do neuroanatomical networks reveal about the ontology of human cognitive abilities?. iScience, Volume 25, Issue 8. https://doi.org/10.1016/j.isci.2022.104706
Kristanto, D., Liu, M., Liu, X., Sommer, W., & Zhou, C. (2020). Predicting Reading Ability from Brain Anatomy and Function: From Areas to Connections. NeuroImage, 116966. https://doi.org/10.1016/j.neuroimage.2023.120304
Kristanto, D., & Leephakpreeda, T. (2018). Effective dynamic prediction of air conditions within car cabin via bilateral analyses of theoretical models and artificial neural networks. Journal of Thermal Science and Technology, 13(2), JTST0020-JTST0020. https://doi.org/10.1299/jtst.2018jtst0020
Kristanto, D., & Leephakpreeda, T. (2017). Sensitivity analysis of energy conversion for effective energy consumption, thermal comfort, and air quality within car cabin. Energy Procedia, 138, 552-557. https://doi.org/10.1016/j.egypro.2017.10.158
Kristanto, D., & Leephakpreeda, T. (2017, March). Energy Conversion for Thermal Comfort and Air Quality Within Car Cabin. In IOP Conference Series: Materials Science and Engineering (Vol. 187, No. 1, p. 012037). IOP Publishing. https://doi.org/10.1088/1757-899X/187/1/012037
Kristanto, D., Wardhana, A., & Rosita, W. (2016) Comparison of Valve Static Friction Detection Method Based on Graphical Fitting. Journal of Automation, Control, and Intrumentation. Vol. 8, No. 2. https://doi.org/10.5614/joki.2016.8.2.4
Please visit the Google Scholar profile for a more detailed list of publications.