Research Interests

• Interpretable machine learning in cognitive neuroscience
• Graph theory in network neuroscience
• Meta-analyses, structural equation modelling

Academic positions 

Education

Publications

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.