Heng (Henry) Yu

hengyu[at]andrew.cmu.edu

I am a master student (MSR) at CMU Robotics Institute, where I work on 3D vision advised by Prof. Laszlo Jeni . I also closely collaborated with Prof. Berkin Bilgic from Harvard Medical School on MRI reconstruction topics and Prof. Cheng Jin from Institute of Medical Robotics, Shanghai Jiao Tong University on medical image analysis topics.

I obtained my Bachelor's degree from Tsinghua University with a major in Department of Automation , and a second major in School of Economics and Management . During my time at Tsinghua, I worked on computer vision and medical image analysis research under the supervision of Prof. Jie Zhou and Prof. Jianjiang Feng in Intelligent Vision Group . After that, I co-founded Tsingh Technology focused on intelligent logistics with three PhDs. I used to have a few good times at Nebula Link Technology and Sangfor Technologies Inc.

I also worked with Prof. Kris Kitani from CMU RI, Prof. Kawin Setsompop and Prof. Ruijiang Li from Stanford Medicine. My research interest is computer vision and its applications.

CV  /  Google Scholar  /  Github  /  LinkedIn

• Feb 2023: DyLiN paper is accepted by CVPR2023, see you in Vancouver!
• Feb 2023: SubZero abstract is accepted by ISMRM2023 as a power pitch, see you in Toronto!
• Dec 2022: CoNFies paper has been nominated as a best paper candidate!
• Nov 2022: Non-pooling Network paper won Best Paper Award at MICAD 2022!
• Nov 2022: Won gold medal at the 8th China International College Students' 'Internet+' Innovation and Entrepreneurship Competition!
• Sep 2022: CoNFies paper is accepted by FG2023, see you in Hawaii!
• May 2022: I am joining Fujitsu this summer as a machine learning intern, working on controllable NeRF.
• Feb 2022: One paper is accepted by Magnetic Resonance in Medicine!
• Aug 2021: Start my graduate study at CMU RI!
• May 2021: I am joining Sangfor this summer as a machine learning engineer, working on evading web application firewalls with reinforcement learning.
• Mar 2021: One paper is accepted by Nature Communications!
• Feb 2021: eRAKI abstract is accepted by ISMRM2021 as an oral!
• Apr 2020: MixModule paper is accepted by ISBI2020!
• Nov 2019: One paper is accepted by Annals of Surgery!

I have rich experiences in computer vision, MRI reconstruction and medical image analysis. I would like to explore the possibility of AI technology and its applications (e.g. 3D Scene Understanding , Healthcare, etc).

* refers to co-first author. Please refer to my google scholar for more details.

We propose propose the Dynamic Light Field Network (DyLiN) method that can handle non-rigid deformations, including topological changes, which outperformed state-of-the art methods in terms of visual fidelity and compute complexity.

We propose a general adversarial learning framework for solving Unsupervised 2D to Explicit 3D Style Transfer.

We propose a fully-automatic controllable neural representation for face self-portraits.

We develop a scan-specific model that estimates and corrects k-space errors made when reconstructing accelerated MRI data.

We accelerate RAKI by more than 200 times by directly learning a coil-combined target.

We present a multi-task deep learning approach that allows simultaneous tumor segmentation and response prediction of pathologic complete response after neoadjuvant chemoradiotherapy.

We use mixed kernels to improve the performance of existing medical image segmentation networks.

We propose a novel deep neural network (S-net) to construct a CT signature for predicting disease-free survival (DFS) and overall survival.

We develop a deep learning system for predicting lymph node metastasis in multiple nodal stations based on preoperative CT images in patients with gastric cancer.

We develop an image-based person detection algorithm for wearable computing using commodity smartphones.

We propose a robust method for automatic left atrial appendage segmentation on computed tomographic angiography data using fully convolutional neural networks with 3D conditional random fields.

we present a new approach for the detection of substances in the left atrial appendage by spatiotemporal motion analysis and make a detailed judgment and analysis of spatial distribution and classification of most objects in the left atrial appendage.

We propose a robust method for automatic left atrial appendage segmentation on computed tomographic angiography data using fully convolutional neural networks with 3D conditional random fields.

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