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PhD CandidateHarvard-MIT Health Sciences and Technology
Group: Computer Science and Artificial Intelligence Laboratory
Advisor: Dr. Polina Golland

I am a third-year PhD candidate in Medical Engineering and Medical Physics at MIT. Motivated by unmet clinical needs, my research develops machine learning methods that deepen our ability to understand and treat diseases.

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Differentiable Rendering + Minimally Invasive Surgery


Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering

Vivek Gopalakrishnan, Neel Dey, Polina Golland

CVPR, 2024

We use X-ray renderering to develop DiffPose, a self-supervised framework for differentiable 2D/3D image registration that achieves sub-millimeter registration accuracy.


Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imaging

Vivek Gopalakrishnan, Polina Golland

MICCAI Clinical Image-based Procedures Workshop, 2022

We present DiffDRR, a differentiable X-ray renderer that can be used to solve inverse problems in X-ray imaging with deep learning (e.g., registration or reconstruction).

Drug Discovery


Learning Interpretable Single-Cell Morphological Profiles from 3D Cell Painting Images

Vivek Gopalakrishnan, Jingzhe Ma, Zhiyong Xie

CVPRW, 2024

Society of Biomolecular Imaging and Informatics, 2023 (President's Innovation Award)

Supervised deep learning models are used ubiquitously throughout image-based drug discovery. We discover a mechanism by which these models cheat and propose an interpretability metric to quantify the level of confounding.

Statistical Graph Theory

corpus callosum

Multiscale Comparative Connectomics

Vivek Gopalakrishnan, Jaewon Chung, Eric Bridgeford, Benjamin D. Pedigo, Jesús Arroyo, Lucy Upchurch, G. Allan Johnson, Nian Wang, Carey E. Priebe, Joushua T. Vogelstein

arXiv, 2021

We introduce a set of multiscale hypothesis tests to facilitate the robust and reproducible discovery of hierarchical brain structures that vary in relation with phenotypic profiles.

ASE clustering

Statistical Connectomics

Jaewon Chung, Eric Bridgeford, Jesús Arroyo, Benjamin D. Pedigo, Ali Saad-Eldin, Vivek Gopalakrishnan, Ling Xiang, Carey E. Priebe, Joushua T. Vogelstein

Annual Review of Statistics and Its Application, 2021

From a statistical graph theory perspective, we review models, assumptions, problems, and applications that are theoretically and empirically justified for analysis of connectome data.