I am a Wu Tsai Interdisciplinary Postdoctoral Scholar, working in collaboration with Dr. Russell Poldrack and Dr. Scott Linderman.
I have a background in cognitive and computational psychology, with a PhD in Neuroscience from McGill University and an MA in Developmental Psychology from Cornell University.

Currently, my research focuses on expanding our statistical toolkit for drawing inferences from high-dimensional, naturalistic datasets measured with modalities such as functional magnetic resonance imaging (fMRI). To do this, I am developing new methods and accompanying open source tools.

Honors & Awards

  • Wu Tsai Interdisciplinary Scholar Award, Wu Tsai Neurosciences Institute (1 June 2022 - 1 June 2024)

Stanford Advisors

Lab Affiliations

All Publications

  • Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective. GigaScience Levitis, E., van Praag, C. D., Gau, R., Heunis, S., DuPre, E., Kiar, G., Bottenhorn, K. L., Glatard, T., Nikolaidis, A., Whitaker, K. J., Mancini, M., Niso, G., Afyouni, S., Alonso-Ortiz, E., Appelhoff, S., Arnatkeviciute, A., Atay, S. M., Auer, T., Baracchini, G., Bayer, J. M., Beauvais, M. J., Bijsterbosch, J. D., Bilgin, I. P., Bollmann, S., Bollmann, S., Botvinik-Nezer, R., Bright, M. G., Calhoun, V. D., Chen, X., Chopra, S., Chuan-Peng, H., Close, T. G., Cookson, S. L., Craddock, R. C., De La Vega, A., De Leener, B., Demeter, D. V., Di Maio, P., Dickie, E. W., Eickhoff, S. B., Esteban, O., Finc, K., Frigo, M., Ganesan, S., Ganz, M., Garner, K. G., Garza-Villarreal, E. A., Gonzalez-Escamilla, G., Goswami, R., Griffiths, J. D., Grootswagers, T., Guay, S., Guest, O., Handwerker, D. A., Herholz, P., Heuer, K., Huijser, D. C., Iacovella, V., Joseph, M. J., Karakuzu, A., Keator, D. B., Kobeleva, X., Kumar, M., Laird, A. R., Larson-Prior, L. J., Lautarescu, A., Lazari, A., Legarreta, J. H., Li, X. Y., Lv, J., Mansour L, S., Meunier, D., Moraczewski, D., Nandi, T., Nastase, S. A., Nau, M., Noble, S., Norgaard, M., Obungoloch, J., Oostenveld, R., Orchard, E. R., Pinho, A. L., Poldrack, R. A., Qiu, A., Raamana, P. R., Rokem, A., Rutherford, S., Sharan, M., Shaw, T. B., Syeda, W. T., Testerman, M. M., Toro, R., Valk, S. L., Van Den Bossche, S., Varoquaux, G., Váša, F., Veldsman, M., Vohryzek, J., Wagner, A. S., Walsh, R. J., White, T., Wong, F. T., Xie, X., Yan, C. G., Yang, Y. F., Yee, Y., Zanitti, G. E., Van Gulick, A. E., Duff, E., Maumet, C. 2021; 10 (8)


    As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.

    View details for DOI 10.1093/gigascience/giab051

    View details for PubMedID 34414422

  • Analysis of task-based functional MRI data preprocessed with fMRIPrep. Nature protocols Esteban, O., Ciric, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., Kent, J. D., Goncalves, M., DuPre, E., Gomez, D. E., Ye, Z., Salo, T., Valabregue, R., Amlien, I. K., Liem, F., Jacoby, N., Stojic, H., Cieslak, M., Urchs, S., Halchenko, Y. O., Ghosh, S. S., De La Vega, A., Yarkoni, T., Wright, J., Thompson, W. H., Poldrack, R. A., Gorgolewski, K. J. 2020


    Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time consuming, error prone and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (, a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure to standardize both the input datasets (MRI data as stored by the scanner) and the outputs (data ready for modeling and analysis), fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

    View details for DOI 10.1038/s41596-020-0327-3

    View details for PubMedID 32514178

  • fMRIPrep: a robust preprocessing pipeline for functional MRI NATURE METHODS Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., Gorgolewski, K. J. 2019; 16 (1): 111-+
  • PyBIDS: Python tools for BIDS datasets. Journal of open source software Yarkoni, T., Markiewicz, C. J., de la Vega, A., Gorgolewski, K. J., Salo, T., Halchenko, Y. O., McNamara, Q., DeStasio, K., Poline, J., Petrov, D., Hayot-Sasson, V., Nielson, D. M., Carlin, J., Kiar, G., Whitaker, K., DuPre, E., Wagner, A., Tirrell, L. S., Jas, M., Hanke, M., Poldrack, R. A., Esteban, O., Appelhoff, S., Holdgraf, C., Staden, I., Thirion, B., Kleinschmidt, D. F., Lee, J. A., Visconti di Oleggio Castello, M., Notter, M. P., Blair, R. 2019; 4 (40)

    View details for DOI 10.21105/joss.01294

    View details for PubMedID 32775955