Welcome to my GitHub!
I work at the intersection of geometry processing, medical imaging, and geometric deep learning, with a focus on anatomical shape analysis and computational tools for 3D morphometry.
My work revolves around:
- 🧩 Statistical shape modeling of anatomical structures
- 🧠 Medical image → mesh pipelines (segmentation, meshing, cleaning, registration)
- 📐 Differential geometry for curvature, shape descriptors, and intrinsic measures
- 🔁 Shape correspondence & deformation modeling
- 🤖 Geometric deep learning (mesh CNNs, point cloud networks, spectral GNNs)
- 🧱 Nonlinear shape spaces, manifold learning, and latent geometric representations
- 🌐 3D data preprocessing: remeshing, smoothing, Laplacian operators, spectral embeddings
Geometry Processing:
CGAL · libigl · VTK · Open3D · MeshLab · trimesh · PyMesh · Polyscope
Machine Learning / GDL:
PyTorch · PyTorch Geometric (PyG) · PyTorch3D · Kaolin · MinkowskiEngine
Medical Imaging:
ITK · SimpleITK · NiBabel · ANTs · FSL · FreeSurfer
Core Languages:
Python · C++ · MATLAB · Bash
- 📐 Shape analysis pipeline for brainstem and cerebellar deformities (MICCAI 2025)
- 🧠 Developing robust geometric descriptors for MRI-derived meshes
- 🕳️ Improving mesh-based correspondence in pathological anatomy
- 🔍 Exploring graph neural networks for anatomical structure classification
- Unraveling Brainstem Deformations in Joubert Syndrome — MICCAI 2025
Statistical shape analysis of MRI-derived structures using remeshing, Laplacian-based smoothing, and deformation mapping.
(More papers available on my website or the publications/ folder.)
- 🧩 GeoMed-ShapeTools — utilities for spectrally processing anatomical meshes
- 🧬 Brainstem-Morphometry — end-to-end MRI → mesh → shape descriptor pipeline
- 🎛️ GDL-Experiments — mesh CNNs, spectral GCNs, and manifold networks
- 📐 MeshOps — scripts for remeshing, smoothing, curvature estimation, etc.
- 🌐 Website: your URL
- ✉️ Email: your email
- 🧠 Google Scholar: your profile
- 🔗 LinkedIn: your link