Research
Computational neuroscience, biomedical data science, and machine learning applied to complex clinical datasets.
Publications
First-Author & Lead Contributions
MedRxiv (preprint)
Topography-aware brain–behavior data integration. Links individual differences in sensory sensitivity to how the brain dynamically coordinates information during social perception, and shows that sensory traits map onto context-dependent brain network changes.
Biocomputing 2025. World Scientific; 2024:614–630.
Representation learning framework; tests how different dimensionality reduction methods behave across heterogeneous clinical datasets, showing that the structure of the data determines whether meaningful biomarkers can be recovered.
Frontiers in Neuroscience. 2022;16:1040085.
Used behavioral data from multiple sites to show that autism differences are structured along interpretable dimensions, especially diagnosis and sex, rather than being random or easily separable into clean groups.
Traumatic Brain Injury: Rehabilitation, Treatment, and Case Management, 4th Ed. 2017; Ch. 12, pp. 167–178.
Outlines why standard brain imaging methods often fail in extreme clinical cases and proposes practical strategies for making analyses reliable in highly atypical brain anatomies.
Additional Publications
Imaging Neuroscience. 2025;3.
Links between behavior and brain structure in autism shift with development, suggesting that apparent "subtypes" may reflect age-dependent changes rather than fixed categories.
PLoS ONE. 2024;19(4):e0301964.
Uses advanced MRI-derived metrics to show that differences in signal transmission along white matter pathways may underlie variability in how information is processed in autism.
Neuroinformatics. 2023:1–3.
Argues that neuroscience needs stronger theoretical frameworks, not just wider data collection efforts, to meaningfully understand how brain connectivity relates to disorders and behavior.
Frontiers in Computational Neuroscience. 2018;12:93.
Combines machine learning and imaging to show that sex meaningfully changes how autism-related brain differences appear, rather than simply adding variability.
IEEE Transactions on Affective Computing. 2018;9(1):76–89.
Develops a method to recover reliable signals from inconsistent human annotations by treating each annotator as a noisy observer of an underlying truth.
NeuroImage: Clinical. 2017;16:355–368.
Reviews how early brain imaging can be used to predict developmental outcomes in preterm infants, while highlighting the limits of current predictive approaches.
Scientific Reports. 2017;7:46401.
Shows that large-scale brain wiring differs between males and females with autism, helping explain differences in how the condition presents.
Frontiers in Psychiatry. 2017;7:205.
Synthesizes a fragmented literature on brain connectivity in autism, highlighting inconsistent findings and the need for more structured, theory-driven approaches.
Autism Research. 2021;14(5).
Demonstrates that sex and early development shape when autism is recognized, suggesting diagnostic timing is influenced by more than symptom severity alone.
Brain. 2021;awab064.
Integrates genetic and brain imaging data to investigate why autism is diagnosed less frequently in females, pointing to distinct biological pathways.
Translational Psychiatry. 2020;10:178.
Shows that reward-related brain responses during social interactions differ in autistic females, offering insight into how social motivation varies across groups.
Journal of Neuroscience Research. 2018;96(4):652–660.
Case study linking early brain injury to later-life cognitive decline, illustrating how long-term structural changes can unfold over decades.
Full record: 14 journal articles, 1 book chapter, 15+ conference abstracts (OHBM 2016–2023). See Google Scholar for complete list.