I lead an interdisciplinary research program where single-cell & spatial transcriptomics, AI, and neurotechnology converge.
We aim to transform high-dimensional biological data into clinical insights and to advance fundamental understanding of how the brain responds to tumors, trauma, and intervention.
As a computational biologist, I build the tools to explore them.
As a neuroscientist, I collaborate to drive discovery across disciplines.
- Spatial & single-cell transcriptomics of human glioblastoma (primary & revision)
- Mapping tumor-immune-neuron interactions and cell state plasticity
- Developing graph-based models and entropy metrics for spatial niche dynamics
- Single-cell time series after micro-TBI in animal models
- Studying plasticity and resilience of cortical circuits post-trauma
- Linking transcriptomic and electrophysiological changes
- Single-cell atlas of ligamentum flavum hypertrophy
- Investigating TGF-β-driven fibrosis and ECM remodeling
- FTIR spectroscopy for CSF leak detection
- Building ML models for real-time decision support
- Integrating calcium imaging, electrophysiology, and transcriptomics
- Quantifying functional plasticity at the cellular level
- R: Seurat, SpatialExperiment, fgsea, WGCNA, custom entropy pipelines
- Python: PyTorch, scVI, Pyro, Scanpy
- Graph modeling: PyG, NetworkX
- Machine learning: VAEs, interpretable ML (SHAP, IG), prototype learning
- Spatial analysis: building alternatives to BayesSpace
- Signal processing: electrophysiology, calcium imaging
- Group leader in NeuroEngineering
- Proven expertise in:
- Glioblastoma biology
- Brain tumor microenvironment
- Fibrosis & ECM remodeling
- Trauma-induced CNS responses
- Neuroimmune interactions
- Single-cell & spatial transcriptomics
I am open to collaborations with:
- Advanced postdocs & ML scientists interested in building new methods
- Biologists eager to drive computational innovation
- Clinicians exploring AI-driven translational tools
- Industry partners interested in neuroscience & digital twins