Research
My research spans data science, interactive visualization, data-intensive computing, and information retrieval for brain and health sciences. Application-driven contributions that expand computer science into interdisciplinary research are central to exploring and understanding health and scientific problems.
I develop computational techniques and interactive visualization applications through close collaboration with neuropsychologists, including Dr. Stephen Correia at Butler Hospital and Dr. David Tate at Harvard Medical School. This work has produced new computational approaches for virtual histology of brain microstructure using diffusion imaging, providing reliable and sensitive biomarkers for brain disease diagnosis and prevention.
Research Infrastructure and Collaboration
A central aspect of my work is designing and stewarding research workflows and platforms that enable interdisciplinary, funded research. This includes data organization practices, computational pipelines, visualization tools, and shared research environments used by faculty, graduate students, and undergraduate researchers. I work closely with research collaborators and institutional partners to translate research requirements into reliable, well-documented, and reproducible workflows.
This work is informed by sponsor expectations and institutional policies for research quality, documentation, and responsible data management.
Laboratory Leadership and Research Platforms
I direct the Biomedical Computing and Visualization Laboratory, founded in 2016. The lab supports interdisciplinary research in neuroimaging, applied machine learning, and computational drug discovery. I oversee shared research infrastructure, including data pipelines, computational workflows, versioned codebases, and documentation standards that enable collaboration across students, faculty, and external partners.
The laboratory emphasizes reproducible and interpretable methods, with containerized workflows and clear data provenance to support multi-site validation, longitudinal studies, and responsible data reuse.
Methods and Technical Scope
- Reproducible data pipelines for diffusion MRI and functional MRI analysis
- Tract-level feature extraction, supervised and unsupervised learning
- Molecular dynamics and structure-based modeling for drug discovery
- Generative modeling (GAN-based methods) with reproducible evaluation protocols
- Visual analytics and interactive systems for scientific exploration
1. Data-Intensive Computing and Modeling
What we do
- Computational modeling and inference for microstructure measurement using diffusion MRI
- Robust pipelines for quantitative imaging analysis
- Analytical modeling of water diffusion to extract axon sizes, distribution, and volume fraction
- Methods that support reproducible evaluation and cross-study comparison
- Computational drug design pipeline using protein electron structure calculation
Representative outcomes
- Virtual histology-style microstructure measurement with improved sensitivity to small axons (radius < 3 μm), applicable to low-gradient clinical scanners and whole-brain mapping
- Quantitative biomarkers for brain disease studies, validated across imaging protocols
- Unique computational drug design pipeline for cancer treatment, presented at NIH
2. Scientific Visualization
What we do
- Visual analytics for multi-modal brain imaging data (diffusion MRI, spectral MRI, microCT)
- Perceptual coloring schemes for white-matter tract segmentation and similarity mapping
- Circular visualization interfaces for cortical-connectivity integrity assessment
- Integration of computationally extracted microstructural properties with diffusivity measures
Representative outcomes
- ACM SIGGRAPH 2006 Best Poster and First Place in ACM Student Research Competition: perceptual coloring for neural pathway segmentation
- Circular visualization of tractography metrics across 80 cortical regions, enabling identification of connectivity differences between healthy controls and patient groups
3. Interactive Systems
What we do
- Taxonomy and design guidelines for tracts-of-interest (TOI) selection interfaces
- 2D sketching interfaces matched to neuroscientists' training on axis-aligned slices
- 3D stereo virtual reality (VR) environments with higher-input devices for manipulating tractography data
- Educational interactive tools: anatomy learning games (funded by William Beaumont School of Medicine) and mobile applications for CS education
Representative outcomes
- Best Poster Nominee (top 5) at IEEE VIS 2008: haptics-assisted 3D lasso drawing for TOI selection
- Anatomy learning game for first-year medical students; mobile CS education study with ~800 middle school participants
