Clinton Wang

I am an ML Research Scientist at Scale AI, focusing on expanding LLM capabilities and improving alignment. My previous research includes computer vision, neural radiance fields, and medical image analysis. I did a PhD at MIT CSAIL advised by Polina Golland. I was a student researcher at Google with Daniel Duckworth and Peter Hedman, and interned at Adobe with Jiawen Chen and Cecilia Zhang. I worked with Jim Duncan and Julius Chapiro in the Yale Radiology Research Lab, and as an undergrad I did research with Stuart Campbell.

Selected Publications (full list)

	Implicit Representations via Operator Learning Sourav Pal, Harshavardhan Adepu, Clinton Wang, Polina Golland, Vikas Singh ICML 2024 Paper \| Code

	InterNeRF: Scaling Radiance Fields via Parameter Interpolation Clinton Wang, Peter Hedman, Polina Golland, Jonathan T. Barron, Daniel Duckworth CVPR Neural Rendering Intelligence 2024 Paper

	Discretization Invariant Networks for Learning Maps between Neural Fields Clinton Wang, Polina Golland Transactions on Machine Learning Research 2023 Project \| Paper \| Code

	Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series Zeen Chi, Zhongxiao Ciao, Clinton Wang, Yingcheng Liu, Esra Abaci Turk, Ellen Grant, Mazdak Abulnaga, Polina Golland, Neel Dey Medical Imaging Meets NeurIPS 2023 Paper \| Code

	Interpolating between Images with Diffusion Models Clinton Wang, Polina Golland ICML Workshop on Challenges of Deploying Generative AI 2023 Project \| Paper \| Code

	Spatial-Intensity Transforms for Medical Image-to-Image Translation Clinton Wang, Natalia Rost, Polina Golland IEEE Transactions on Medical Imaging 2023 Project \| Paper \| Code

	Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series Mazdak Abulnaga, Neel Dey, Sean Young, Eileen Pan, Katherine Hobgood, Clinton Wang, Ellen Grant, Esra Abaci Turk, Polina Golland Journal of Machine Learning for Biomedical Imaging 2023 Paper \| Code

	Geometry-Aware Field-to-Field Transformations for 3D Semantic Segmentation Dominik Hollidt, Clinton Wang, Polina Golland, Marc Pollefeys Arxiv Preprint 2023 Project \| 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

	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 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

	Implicit Representations via Operator Learning Sourav Pal, Harshavardhan Adepu, Clinton Wang, Polina Golland, Vikas Singh ICML 2024 Paper \| Code

	InterNeRF: Scaling Radiance Fields via Parameter Interpolation Clinton Wang, Peter Hedman, Polina Golland, Jonathan T. Barron, Daniel Duckworth CVPR Neural Rendering Intelligence 2024 Paper

	Discretization Invariant Networks for Learning Maps between Neural Fields Clinton Wang, Polina Golland Transactions on Machine Learning Research 2023 Project \| Paper \| Code

	Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series Zeen Chi, Zhongxiao Ciao, Clinton Wang, Yingcheng Liu, Esra Abaci Turk, Ellen Grant, Mazdak Abulnaga, Polina Golland, Neel Dey Medical Imaging Meets NeurIPS 2023 Paper \| Code

	Interpolating between Images with Diffusion Models Clinton Wang, Polina Golland ICML Workshop on Challenges of Deploying Generative AI 2023 Project \| Paper \| Code

	Spatial-Intensity Transforms for Medical Image-to-Image Translation Clinton Wang, Natalia Rost, Polina Golland IEEE Transactions on Medical Imaging 2023 Project \| Paper \| Code

	Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series Mazdak Abulnaga, Neel Dey, Sean Young, Eileen Pan, Katherine Hobgood, Clinton Wang, Ellen Grant, Esra Abaci Turk, Polina Golland Journal of Machine Learning for Biomedical Imaging 2023 Paper \| Code

	Geometry-Aware Field-to-Field Transformations for 3D Semantic Segmentation Dominik Hollidt, Clinton Wang, Polina Golland, Marc Pollefeys Arxiv Preprint 2023 Project \| 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

	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 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

Clinton Wang

Selected Publications (full list)

Fun AI Creations (more)