I am a PhD candidate at MIT CSAIL advised by Polina Golland. My research focuses on 3D vision, including neural fields and trajectory estimation. I also work on medical image analysis, particularly robust and interpretable techniques for both generative and discriminative models. I use diffusion models to make things.

I currently collaborate with Andrea Tagliasacchi, Daniel Duckworth, and Vincent Sitzmann. I previously worked with Jim Duncan and Julius Chapiro in the Yale Radiology Research Lab, and interned at Iterative Scopes. I am supported by the Takeda Fellowship and Siebel Scholarship. I received a B.S. in Biomedical Engineering at Yale, where I did research with Stuart Campbell.

Selected Publications

	Approximate Discretization Invariance for Deep Learning on Neural Fields Clinton Wang, Polina Golland New England Computer Vision Workshop 2022 Oral ICLR Neural Fields Workshop 2023 NeurIPS Workshop on Symmetry and Geometry in Neural Representations 2022 Project | Paper | Video | Code
	Automatic Segmentation of the Placenta in BOLD MRI Time Series Mazdak Abulnaga, Sean Young, Katherine Hobgood, Eileen Pan, Clinton Wang, Ellen Grant, Esra Abaci Turk, Polina Golland Medical Image Computing and Computer Assisted Intervention PIPPI Workshop 2022 Paper | Code
	Pre-Trained Language Models for Interactive Decision-Making Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, Jacob Andreas, Igor Mordatch, Antonio Torralba, Yuke Zhu NeurIPS 2022 Oral Project | Paper | Code
	Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver Paula Oestmann, Clinton Wang, Lynn Savic, Charlie Hamm, Sophie Stark, Isabel Schobert, Bernhard Gebauer, Todd Schlachter, MingDe Lin, Jeffrey Weinreb, Ramesh Batra, David Mulligan, Xuchen Zhang, James Duncan, Julius Chapiro European Radiology 2021 Paper
	Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation Clinton Wang, Natalia Rost, Polina Golland Medical Image Computing and Computer Assisted Intervention 2020 MIT MGB AI Cures Conference 2022 Project | Paper | Video | Code
	Automated feature quantification of Lipiodol as imaging biomarker to predict therapeutic efficacy of conventional transarterial chemoembolization of liver cancer Sophie Stark, Clinton Wang, Lynn Savic, Brian Letzen, Isabel Schobert, Milena Miszczuk, Nikitha Murali, Paula Oestmann, Bernhard Gebauer, MingDe Lin, James Duncan, Todd Schlachter, Julius Chapiro Scientific Reports 2020 Paper | Code
	A probabilistic approach for interpretable deep learning in liver cancer diagnosis Clinton Wang, Charlie Hamm, Brian Letzen, James Duncan SPIE Medical Imaging Conference 2019 Oral Project | Paper | Talk | Code
	Deep learning for liver tumor diagnosis part II: interpretable deep learning to characterize tumor features Clinton Wang*, Charlie Hamm*, Lynn Savic, Marc Ferrante, Isabel Schobert, Todd Schlachter, MingDe Lin, Jeffrey Weinreb, James Duncan, Julius Chapiro, Brian Letzen European Radiology 2019 Project | Paper | Code
	Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI Charlie Hamm*, Clinton Wang*, Marc Ferrante, Isabel Schobert, Todd Schlachter, MingDe Lin, James Duncan, Jeffrey Weinreb, Julius Chapiro, Brian Letzen European Radiology 2019 Paper | Code
	The Role of Artificial Intelligence in Interventional Oncology: A Primer Brian Letzen, Clinton Wang, Julius Chapiro Journal of Vascular and Interventional Radiology 2019 Paper
	Slowing of contractile kinetics by myosin-binding protein C can be explained by its cooperative binding to the thin filament Clinton Wang, Jonas Schwan, Stuart Campbell Journal of Molecular and Cellular Cardiology 2016 Paper

Fun AI Creations (more)