Area of Research

Research Vision

I develop computational and multi-modal neuroimaging approaches to understand the mechanisms underlying brain disorders and aging. My long-term goal is to bridge mechanistic neuroscience and clinical translation—moving from understanding what changes in the brain to understanding why, and ultimately to inform early detection and intervention. While Alzheimer's disease and related dementias (ADRD) are my primary focus, the methods I develop are designed to generalize across psychiatric conditions and normative aging.

Multi-modal Neuroimaging & Data Fusion

No single data modality captures the full complexity of brain disorders. I develop computational frameworks for integrating structural and functional MRI, fluid biomarkers, electronic health records, and cognitive assessments to build mechanistically grounded models of disease. I am particularly interested in understanding why certain multi-modal patterns predict clinical outcomes—not just that they do—which opens the door to identifying intervention targets.

Computational Approaches to Neurodegeneration

Alzheimer's disease and related dementias unfold over decades, with heterogeneous trajectories across individuals. I use machine learning and neuroimaging to characterize these trajectories—identifying subtypes, predicting progression, and understanding the biological factors that drive cognitive decline. At MGH, I contributed to tools for large-scale neuroimaging analysis (including SynthSeg+, published in PNAS) and applied NLP methods to clinical text data; my current work at McLean/HMS focuses on neuropsychiatric symptoms in ADRD, normative modeling, and multi-modal data fusion.

Spatial Navigation & Cognitive Neuroscience

My doctoral research established that travel direction is a fundamental and independent component of human navigation—distinct from the well-studied head direction signal—using fMRI and novel psychophysical paradigms. Spatial navigation is among the earliest cognitive domains affected in Alzheimer's disease, making the behavioral and neural measures from this work potentially relevant to early detection and intervention.

Individual Differences Across Populations

A thread running through all my research is attention to individual differences—across sex, culture, age, and clinical status. During my doctoral work, I examined how handedness and biological sex influence spatial cognition using both human participants and computational models, and conducted large-scale analyses of navigation behavior across countries using data from nearly 770,000 participants.