Mengwei Ren

I am a Ph.D. candidate at the NYU Visualization and Data Analytics Research (VIDA) Center , supervised by Prof. Guido Gerig. I have been fortunate to intern at Adobe, Google Research, and Siemens Healthineers.

My research broadly lies at the intersection of computer vision , deep learning, and biomedical image analysis. Particularly, I am interested in generative models, representation learning and spatiotemporal analysis.

I will graduate in Spring 2024 and I am actively seeking a full-time opportunity in the field of generative AI. Happy to connect :-)

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Image-conditioned Diffusion Probablistic Models (icDPMs) for restoration work well on benchmarks but not real images. We introduce a simple yet effective structure guidance that leads to significantly better visual quality on unseen images.

We propose a local and multi-scale spatiotemporal representation learning method for image-to-image architectures trained on longitudinal (non i.i.d) images.

Based on an underlying assumption that morphological shape is consistent across imaging sites, we propose a segmentation-renormalized image translation framework to reduce inter-scanner heterogeneity while preserving anatomical layout.

We propose a generative adversarial translation framework for high-quality DW image estimation with arbitrary Q-space sampling given commonly acquired structural images.

We reformulate deformable registration and conditional template estimation as an adversarial game wherein we encourage realism in the moved templates with a generative adversarial registration framework conditioned on flexible image covariates.

cr: Jon Baron