Short and Sweet ver.
I am an Assistant Professor in Data Science in the Department of Psychiatry and Djavad Mowafaghian Centre for Brain Health (DMCBH), University of British Columbia, Vancouver, BC, Canada. Previously I was 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 using brain encoding and decoding to improve fMRI-based model prediction in clinical settings, and understanding cognition in naturalistic context for transdiagnostic description of psychiatric conditions. 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 have a growing collection of knitted garments. I am a recreational Olympic weightlifter, previously affiliated with Club d’haltérophilie Les Géants de Montréal. I am a certified referee in Quebec and am looking for opportunities to volunteer in Western Canada now.
Short speaker bio (click to expand)
Last updated: 2026-02-26
Hao-Ting Wang is an Assistant Professor in Data Science in the Department of Psychiatry at the University of British Columbia, Vancouver, BC, Canada, where she works at the intersection of data science, neuroimaging, and psychiatry. Her research focuses on brain encoding and decoding to improve fMRI-based model prediction in clinical settings, and understanding cognition in naturalistic contexts for transdiagnostic description of psychiatric conditions. She is a core developer of Nilearn, a widely used Python library for machine learning in neuroimaging. She has contributed to large-scale neuroimaging preprocessing projects involving datasets of over 40,000 participants. She is a recipient of the 2023 Neuro-Irv and Helga Cooper Open Science Prize. In addition to her research, she is active in education and outreach and is committed to fostering inclusivity in the open science community. Previously, she co-led workshops for Brainhack School and the Montreal AI and Neuroscience initiative.
Long speaker bio (click to expand)
Last updated: 2026-02-26
Hao-Ting Wang is an Assistant Professor in Data Science in the Department of Psychiatry at the University of British Columbia, Vancouver, BC, Canada. Her research focuses on brain encoding and decoding to improve fMRI-based model prediction in clinical settings, and understanding cognition in naturalistic contexts for transdiagnostic description of psychiatric conditions. She develops and evaluates fMRI foundation models for biomarker discovery in neurodegeneration, integrating diverse clinical datasets to enhance clinical prediction and track disease progression.
Hao-Ting 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, where she applied multivariate statistical methods to study the neurocognitive foundations of mind wandering using resting-state fMRI and experience sampling. Her work provided a comprehensive guide on applying canonical correlation analysis in brain-behaviour association studies, which has garnered over 200 citations since its publication in 2020. She subsequently held a postdoctoral position at the Centre de recherche de l’Institut universitaire de gériatrie de Montréal (CRIUGM), supported by fellowships from the Institut de valorisation des données (IVADO) and the Canadian Neuroanalytics Scholarship, where she developed fMRI-based approaches for biomarker discovery in neurodegeneration.
Open-source tools are a cornerstone of her research. As a core developer of Nilearn, a widely used Python library for machine learning in neuroimaging, she guides the library’s direction and contributes expertise in fMRI data processing. She actively contributes to the broader neuroimaging Python ecosystem, including nibabel, Pydra, fMRIPrep, phys2bids, and the Brain Imaging Data Structure (BIDS). Her advocacy for reproducible, BIDS-compliant software led to a fully reproducible fMRI denoising benchmark using open datasets, and she has contributed to large-scale neuroimaging preprocessing projects involving datasets of over 40,000 participants.
She is a recipient of the 2023 Neuro-Irv and Helga Cooper Open Science Prize. In addition to her research, she is active in education and outreach, having co-led workshops for Brainhack School and the Montreal AI and Neuroscience initiative, and is committed to fostering inclusivity in the open science community.