Scholar
Vivek Gopalakrishnan
Google Scholar ID: kYGmJpAAAAAJ
Harvard-MIT Division of Health Sciences and Technology
Citations & Impact
Citations
251
H-index
8
i10-index
7
Publications
18
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- PolyPose: Deformable 2D/3D Registration via Polyrigid Transformations, NeurIPS, 2025
- Rapid Patient-Specific Neural Networks for Intraoperative X-ray to Volume Registration, arXiv, 2025
- Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering, CVPR, 2024
- Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imaging, MICCAI Clinical Image-based Procedures Workshop, 2022
- Learning Interpretable Single-Cell Morphological Profiles from 3D Cell Painting Images, CVPRW, 2024; Society of Biomolecular Imaging and Informatics, 2023 (President's Innovation Award)
- Multiscale Comparative Connectomics, Imaging Neuroscience, 2025
- Statistical Connectomics, Annual Review of Statistics and Its Application, 2021
Research Experience
- Conducting research at the Harvard-MIT Program in Health Sciences and Technology, focusing on differentiable rendering, minimally invasive surgery, 2D/3D registration, drug discovery, and statistical graph theory.
Education
- PhD candidate at Harvard-MIT Health Sciences and Technology, advised by Dr. Polina Golland.
Background
- PhD student at the Harvard-MIT Program in Health Sciences and Technology, advised by Polina Golland. My research combines computer vision and medical physics to develop spatial models to extract quantitative 3D/4D information from widely used 2D medical modalities (e.g., X-ray and ultrasound). My work uses these models to solve unmet clinical needs in diagnostics, image-guided interventions, and surgical robotics.
Miscellany
- Other personal interests and hobbies are not provided.
Co-authors