Chenyu You
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Chenyu You
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Introduction
I am a final year Ph.D. candidate in the Department of Electrical Engineering, Yale University, where I am very fortunately advised by Prof. James S. Duncan. I obtained a Master degree in Electrical Engineering from Stanford University in 2019, working with Prof. Daniel Rubin. Prior to that, I received my bachelor degree (with honors) in Electrical Engineering and Mathematics from Rensselaer Polytechnic Institute (RPI) in 2017, advised by Prof. Ge Wang. I also spent time at Facebook AI Research (FAIR), New York and Google Research, Mountain View.
My research interest is in Biomedical AI, which lies in the interdisciplinary areas of machine learning theory and algorithms, biomedical computing and analysis, signal and image processing, natural language processing, biomedical imaging, and data science. I am particularly interested in building solid practical and theoretical foundations for healthcare and machine learning that enable the sustained and trustworthy deployments of AI systems within the complex landscape of real-world clinical settings. See my publications and research topics for more details.
I am always looking for strong grads and senior undergrads to collaborate. I also actively engage in mentorship, so feel free to drop a line if you are starting out in the field and are confused about publication expectations, PhD Admissions, or any associated topics. Remote collaboration is also welcome!
News
[2023.11] Our paper “Segment Anything in Medical Images” has been formally accepted by Nature Communications (Impact Factor: 17.0). [code]
[2023.10] Two papers accepted at WACV 2024.
[2023.10] One paper accepted at IEEE BIBM 2023.
[2023.10] Thrilled to be recognized as IEEE Transactions on Medical Imaging (TMI) Platinum Distinguished Reviewer.
[2023.09] Our paper “Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective” has been accepted at NeurIPS 2023! [code]
[2023.09] Two papers accepted at NeurIPS 2023!
[2023.09] One paper accepted at npj Digital Medicine (Impact Factor 15.357).
[2023.09] One paper accepted at Physics in Medicine and Biology.
[2023.08] One paper accepted at CIKM 2023.
[2023.07] One paper accepted at MICCAI-MLCN 2023.
[2023.07] Our new preprint “Segment Anything in Medical Images” is online on Arxiv. [code]
[2023.06] Four papers accepted at IEEE NSS-MIC-RTSD 2023 (1 Oral).
[2023.06] Six papers accepted at MICCAI 2023.
[2023.06] One paper accepted at RecSys 2023.
[2023.05] One paper accepted at Investigative Radiology (Impact Factor: 10.065).
[2023.05] Three papers early accepted at MICCAI 2023!
[2023.05] Two papers accepted at ACL 2023.
[2023.02] Our paper “Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation” has been accepted at IPMI 2023. [code]
[2023.02] One paper accepted at IPMI 2023.
[2023.01] One paper accepted at Medical Physics.
[2022.10] One paper accepted at WACV 2023.
[2022.09] Our new preprint “Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels” is online on Arxiv.
[2022.09] Our paper “Class-Aware Adversarial Transformers for Medical Image Segmentation” has been accepted at NeurIPS 2022!
[2022.09] Two papers accepted at NeurIPS 2022!
[2022.08] Gave a talk at Meta AI Research (FAIR) Eestcoast Vision Group about Contrastive Learning.
[2022.08] Very honored to be recognized as IEEE Transactions on Medical Imaging (TMI) Platinum Distinguished Reviewer.
[2022.08] One paper accepted at WACV 2023.
[2022.07] One paper accepted at MICCAI-STACOM 2022.
[2022.07] One paper accepted at MICCAI-DeCaF 2022.
[2022.07] One paper accepted at MICCAI-DGM4MICCAI 2022.
[2022.06] Two papers accepted at MICCAI 2022.
[2022.05] One paper accepted at KDD 2022 (Oral).
[2022.05] One paper early accepted at MICCAI 2022!
[2022.04] One paper accepted at NAACL 2022.
[2022.03] Our paper “SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation” has been formally accepted by IEEE Transactions on Medical Imaging (IEEE-TMI) (Impact Factor: 10.048).
[2022.03] I will be interning at Facebook AI Research (FAIR), New York, starting from June 2022.
[2022.03] One paper accepted at ICLR-PAIR2Struct 2022.
[2022.02] Very proud to be recognized as a Distinguished Referee for Medical Physics.
[2022.01] Our new preprint “Class-Aware Generative Adversarial Transformers for Medical Image Segmentation” is online on Arxiv.
[2021.12] Awarded 2021 - 2022 Yale Fellowship.
[2021.12] One paper accepted at AAAI 2022.
[2021.11] One paper has been formally accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE-TPAMI) (Impact Factor 17.861).
[2021.10] One paper accepted at WACV 2022.
[2021.09] One paper accepted at NeurIPS 2021.
[2021.08] One paper accepted at EMNLP 2021.
[2021.06] Three papers accepted at Interspeech 2021.
[2021.05] Glad to be recognized as CVPR 2021 outstanding reviewer.
[2021.04] One paper accepted at IJCAI 2021 (Acceptance Rate: 13.9%).
[2021.03] Very proud to be recognized as Medical Physics Journal Distinguished Reviewer.
[2021.02] Very honored to be recognized as IEEE Transactions on Medical Imaging (TMI) Distinguished Reviewer.
[2021.01] Two papers accepted at IEEE-ICASSP 2021.
[2021.01] One paper accepted at ICLR 2021 (Spotlight).
[2020.11] Awarded 2020 - 2021 Yale Fellowship.
[2020.10] Proud to be an Outstanding Reviewer for MICCAI 2020.
[2020.09] We have released all the training/testing code and pretrained models for NuSeT, a high quality Tool box for Cell Segmentation and Analysis.
[2020.08] One paper accepted at MICCAI-MIL3ID 2020.
[2020.07] One paper accepted at PLOS Computational Biology.
[2019.10] Awarded 2019 - 2020 Yale Fellowship.
[2019.09] Three full papers accepted at Proceedings of SPIE.
[2019.07] We have released all the training/testing code for GAN-CIRCLE (IEEE-TMI 2020) at Github.
[2019.06] Our paper “Recovery of Latent Vectors and Exploration of Latent Space from StyleGAN” received the Stanford CS230 Final Project Prize, mentored by Prof. Andrew Ng.
[2019.06] Our paper “CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)” has been formally accepted by IEEE Transactions on Medical Imaging (IEEE-TMI) (Impact Factor: 10.048).
[2019.01] One papers accepted at Medical Physics.
Contact
Email: chenyu.you [AT] yale [DOT] edu
Address: 300 Cedar Street, New Haven, CT 06520-8042