Short and Sweet ver.
I am a Canadian Neuroanalytics Scholar in laboratory for brain simulation and exploration (SIMEXP) at Montreal Geriatrics Institute (CRIUGM), working with Prof Lune Bellec. I completed my PhD at the University of York, UK, with Prof Jonny Smallwood.
My current research focuses on discovering brain connectivity-based biomarkers through machine learning. I am passionate about data infrastructure and software engineering in the context of scientific research. You can find out more about my research and open-source contributions on my GitHub Profile.
When I am not doing research, I spend a lot of time in the kitchen, and I have a lot of opinions about groceries. I am a recreational Olympic weightlifter affiliated with Club d’haltérophilie Les Géants de Montréal.
Longer speaker bio, see below:
Last updated: 26 June 2025
Hao-Ting Wang earned her Ph.D. in Cognitive Neuroscience and Neuroimaging from the University of York, United Kingdom, in 2019. Under the guidance of Professors Jonathan Smallwood and Elizabeth Jefferies, her research specialisation focused on applying multivariate statistical methods in cognitive neuroscience. During her Ph.D., she conducted work using sparse canonical correlation analysis to explore the neurocognitive foundations of mind wandering, utilising resting-state functional magnetic resonance imaging (fMRI) and experience sampling. Her research provided a comprehensive guide on applying canonical correlation analysis in brain-behaviour association studies. It has garnered over 200 citations since its publication in 2020.
Currently, Hao-Ting serves as a postdoctoral researcher at le Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM) in laboratory for brain simulation and exploration (SIMEXP) at Montreal Geriatrics Institute (CRIUGM), working with Prof Lune Bellec. She received a fellowship from the Institut de valorisation des données (IVADO) in 2022 and subsequently the Canadian Neuroanalytics Scholarship in 2025 to develop an fMRI-based foundation model for biomarker discovery in neurodegeneration. Her work integrates diverse clinical datasets to enhance clinical predictions and disease progression. Currently, she focuses on adopting fMRI foundation models with publicly available weights for clinical datasets and working towards a protocol for evaluating the performance of these foundation models based on their downstream performance.
The open-source tool serves as a cornerstone of her research. She contributes to the development of technical standards within the Python ecosystem for neuroimaging research. Her contributions include projects such as nibabel, Pydra, fMRIPrep, phys2bids, the Brain Imaging Data Structure (BIDS), and Nilearn. As a core developer of Nilearn, a widely used neuroimaging machine learning library, she offers expertise in fMRI data processing utilities and guides the overall direction of the Nilearn community. Additionally, she is a dedicated advocate for the BIDS ecosystem, actively developing software aligned with BIDS application standards. The effort leads to a fully reproducible fMRI denoising benchmark using open datasets and BIDS-compliant software.