Daniel Jacob Tward

Daniel Jacob Tward serves as an Assistant Professor in both the Department of Computational Medicine and Department of Neurology at the David Geffen School of Medicine, University of California Los Angeles. His research bridges computational mathematics with neuroscience and medical imaging, focusing on developing advanced algorithms for brain mapping and disease analysis.

His primary research interests include computational anatomy, neuroimaging, and medical image analysis, with specific expertise in diffeomorphic mapping techniques. Tward's work enables precise alignment of brain structures across different imaging modalities and scales, from microscopic histology to whole-brain MRI. His methodologies are particularly applied to Alzheimer's disease research, where he analyzes neurodegeneration patterns and tau pathology distribution.

Analysis of his recent publications reveals a clear progression toward multiscale integration - connecting molecular, cellular, and tissue-level data through computational frameworks. His 2023-2025 work shows increasing emphasis on spatial transcriptomics integration with imaging data, deep learning applications for image registration, and population-level analyses of neurodegenerative diseases. The BRAIN Initiative Cell Census Network collaboration represents his commitment to open neuroscience infrastructure.

Tward actively contributes to major research consortia including the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the BRAIN Initiative Cell Census Network. His technical developments in diffeomorphic mapping form the foundation for several computational anatomy tools used in both basic neuroscience and clinical research contexts.

His laboratory focuses on developing the Computational Anatomy Gateway - a framework for processing neuroimaging data across multiple scales. Current projects involve integrating spatial transcriptomics with histology, creating 3D digital brain atlases, and developing predictive models for neurodegenerative disease progression using longitudinal imaging data.