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<title>Ke Wang </title>
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<div class="menu-category">Home</div>
<div class="menu-item"><a href="index.html" class="current">About me</a></div>
<div class="menu-category">Research</div>
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<h1>Ke Wang </h1>
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<table class="imgtable"><tr><td>
<img src="kwang.jpg" alt="alt text" width="244.8px" height="360.6px" /> </td>
<td align="left"><p>Ke Wang<br />
Senior Research Engineer <br />
<a href="https://sra.samsung.com/">Samsung Research America</a> <br />
Ph.D. from <a href="https://eecs.berkeley.edu/">EECS</a> <br />
<a href="https://www.berkeley.edu/">University of California, Berkeley</a> <br />
Contact: <a href="mailto:[email protected]">Email</a>, <a href="https://www.linkedin.com/in/ke-wang-32705a16a/">Linkedin</a>, <a href="https://github.com/KeWang0622">Github</a>, <a href="https://people.eecs.berkeley.edu/~kewang/">Homepage</a>, <a href="https://twitter.com/KewangKe">Twitter</a></p>
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<h2>Short Bio </h2>
<p>I am a senior research engineer at Samsung Research America (SRA), MPI Lab, working on real-world computational imaging and computer vision problems. I obtained my Ph.D. degree from Electrical Engineering and Computer Sciences at UC Berkeley, working with <a href="http://people.eecs.berkeley.edu/~mlustig/">Prof. Miki Lustig</a> and <a href="https://web.eecs.umich.edu/~stellayu/">Prof. Stella Yu</a>. I was a member of <a href="https://bair.berkeley.edu/">Berkeley Artificial Intelligence Research (BAIR)</a>. I also work closely with <a href="http://users.ece.utexas.edu/~jtamir/">Prof. Jon Tamir</a> at UT Austin and <a href="https://www.frankongh.com/">Dr. Frank Ong</a> at Stanford University. I graduated with honor from the department of <a href="http://www.med.tsinghua.edu.cn/">Biomedical Engineering</a> in <a href="http://www.tsinghua.edu.cn/publish/thu2018en/index.html/">Tsinghua University</a> <b>(Happy 110th birthday, Tsinghua!!)</b>. My research interests lie in <b>computational imaging, deep learning, signal processing, inverse problem, medical imaging and computer vision</b>. I am an enthusiast of science, engineering, music, ice skating, rock climbing and everything related to medicine and healthcare! My name in Chinese is 王可.</p>
<p>I did a wonderful internship at Adobe Emerging Products Group in the summer of 2021 (Work successfully deployed at PhotoShope Camera)!</p>
<p>I interned at Adobe Research as a research scientist intern from May 2022 to March 2023.</p>
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<h2>News</h2>
<ul>
<li><p>[6-2023] Our image harmonization work (<a href="https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Semi-Supervised_Parametric_Real-World_Image_Harmonization_CVPR_2023_paper.pdf">Semi-supervised Parametric Real-world Image Harmonization</a>) is presented at CVPR 2023. <a href="https://kewang0622.github.io/sprih/">[Project page]</a><a href="https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Semi-Supervised_Parametric_Real-World_Image_Harmonization_CVPR_2023_paper.pdf">[Paper]</a><a href="https://www.youtube.com/watch?v=SGAyDbJPyps">[Video]</a><a href="https://github.com/adobe/PIH">[Code]</a><a href="cvpr23_poster_8236.pdf">[Poster]</a></p>
</li>
<li><p>[6-2023] Our paper High-fidelity Direct Contrast Synthesis from MR Fingerprinting was accepted by MRM is now published online! Please check it out! <a href="https://onlinelibrary.wiley.com/doi/10.1002/mrm.29766">[Paper]</a></p>
</li>
<li><p>[6-2023] I join Samsung Research America (SRA) as a senior research engineer, working on real-worled computational imaging and computer vision! Lets keep making impacts!</p>
</li>
<li><p>[5-2023] <b>Graduation time! I offically obtained my Ph.D degree from EECS, UC Berkeley! Go Bears!</b></p>
</li>
<li><p>[3-2023] Our image harmonization work is accepted to <a href="https://cvpr2023.thecvf.com/">CVPR 2023</a>! Check out our <a href="http://people.eecs.berkeley.edu/~kewang/sprih">project website</a>!</p>
</li>
<li><p>[1-2023] I will be serving as reviewer for MICCAI 2023, Neurips 2023, Siggraph Asia 2023.</p>
</li>
<li><p>[12-2022] Our Direct Contrast Synthesis (MRF) paper is out on arXiv. Please check it out! <a href="https://arxiv.org/abs/2212.10817">[paper]</a></p>
</li>
<li><p>[5-2022] I presented our work on Rigorous Uncertainty Estimation for MRI Reconstruction at ISMRM 2022 as an oral presentation.</p>
</li>
<li><p>[4-2022] Our UFLoss paper was accecpted by MRM and is now published online! Please check it out! <a href="https://onlinelibrary.wiley.com/doi/10.1002/mrm.29227">[paper]</a><a href="https://cds.ismrm.org/protected/20MPresentations/videos/dcvz/0994.htm">[talk]</a><a href="https://github.com/mikgroup/UFLoss">[code]</a></p>
</li>
<li><p>[2-2022] Three abstracts (1 first-authored and 2 co-authored) were accepted by ISMRM 2022 as oral presentations!</p>
</li>
<li><p>[2-2022] Our Data Crimes paper was accpeted for publication in PNAS! More infromation and details for this paper are avaible on <a href="https://www.efratshimron.com/efrat-shimron-home">Efrat's website</a>.</p>
</li>
<li><p>[1-2022] I will be joining <a href="https://research.adobe.com">Adobe Research</a> as a research scientist intern this summer, working on Computer Vision and Computational Photography with <a href="https://research.adobe.com/person/eli-shechtman/">Eli Shechtman</a> and <a href="http://mgharbi.com/">Michaël Gharbi</a>!</p>
</li>
<li><p>[12-2021] I passed my qualifying exam and became a Ph.D. candidate in EECS at UC Berkeley!</p>
</li>
<li><p>[9-2021] I will be presenting our work on <i>Memory-efficient Learning for High-dimensional MRI Reconstruction</i> at <a href="https://miccai2021.org/en/">MICCAI 2021</a>. Date & Time: <b>September 29th (Wednesday), 09:30 - 11:00 (UTC)</b>. Welcome to check it out! <a href="https://arxiv.org/abs/2103.04003">[Preprint]</a> <a href="MICCAI.pdf">[Poster]</a> <a href="https://www.youtube.com/watch?v=zkCjlOnURSc&ab_channel=KeWang">[Video]</a></p>
</li>
<li><p>[9-2021] Our preprint paper <a href="https://arxiv.org/abs/2109.08237">“Subtle Inverse Crimes: Naïvely training machine learning algorithms could lead to overly-optimistic results”</a> is out on arXiv! This work raises a red flag regarding Naïve off-label usage of Big Data and reveals the vulnerability of mordern inverse problem solvers to the resulting bias. Welcome to check it out!</p>
</li>
<li><p>[8-2021] Our preprint paper <a href="https://arxiv.org/abs/2108.12460">“High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Loss”</a> is out on arXiv! Welcome to check it out! Code is available <a href="https://github.com/mikgroup/UFLoss">here</a>! In this work, we present a patch-based unsupervised learned feature loss (UFLoss), which allows the training of DL-based reconstruction to obtain more detailed texture, finer features and sharper edges with higher overall image quality. Previously presented at ISMRM 2020.</p>
</li>
<li><p>[5-2021] I will be spending this summer at Adobe EPG team as a research intern, working on Computer Vision and Computational Photography with <a href="https://www.linkedin.com/in/xin-lu-1130053a/">Xin Lu</a> and <a href="https://www.linkedin.com/in/zichuan-liu-a2a199154/?originalSubdomain=sg">Zichuan Liu</a>. Let's make some impact!!</p>
</li>
<li><p>[3-2021] Our preprint full paper on <a href="https://arxiv.org/abs/2103.04003">“Memory-efficient learning for High-dimensional MRI Reconstruction”</a> is out on arXiv! <b>6-2021 Update: This paper has been accepted by MICCAI 2021!</b> In this work, we demonstrate a memory-efficient learning (MEL) framework, which uses far less GPU memory for training unrolled networks and enables new applications of DL to high-dimensional MRI.</p>
</li>
<li><p>[3-2021] Our preprint full paper on <a href="https://arxiv.org/abs/2103.04566">“OUTCOMES: Rapid Under-sampling Optimization achieves up to 50% improvements in reconstruction accuracy for multi-contrast MRI sequences”</a> is out on arXiv!</p>
</li>
<li><p>[2-2021] First authored abstract “Memory-efficient learning for High-dimensional MRI Reconstruction” has been accepted by ISMRM 2021 (<a href="magna2021.pdf"><b>Magna cum laude award</b></a>). Co-authored abstracts “DSLR+: Enhancing deep subspace learning reconstruction for high-dimensional MRI”, “Subtle Inverse Crimes: Naively using Publicly Available Images Could Make Reconstruction Results Seem Misleadingly Better!”(<b>Magna cum laude award</b>), “A GPU-accelerated Extended Phase Graph Algorithm for differentiable optimization and learning” have been accepted by ISMRM 2021.</p>
</li>
<li><p>[11-2020] Co-authored paper <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.28569">“CG‐SENSE revisited: Results from the first ISMRM reproducibility challenge”</a> accpeted to Magnetic Resonance in Medicine. <a href="https://arxiv.org/abs/2008.04308">Preprint</a></p>
</li>
<li><p>[1-2020] First authored abstract “High-Fidelity Reconstruction with Instance-wise Discriminative Feature Matching Loss” has been accepted by ISMRM 2020 as an oral presentation.(<a href="magna2020.pdf"><b>Magna cum laude award</b></a>)</p>
</li>
<li><p>[1-2020] First authored abstract “High Fidelity Direct-Contrast Synthesis from Magnetic Resonance Fingerprinting in Diagnostic Imaging” has been accepted by ISMRM 2020 as an oral presentation.(<a href="summa2020.pdf"><b>Summa cum laude award</b></a>)</p>
</li>
<li><p>[10-2019] Our direct contrast synthesis work “Towards High Fidelity Direct-Contrast Synthesis from Magnetic Resonance Fingerprinting” has been accepted by Med-Neurips 2019 (Neurips workshop).</p>
</li>
<li><p>[9-2019] Our review paper <a href="https://arxiv.org/pdf/1906.11410.pdf">“Computational MRI with Physics-based Constraints: Application to Multi-contrast and Quantitative Imaging”</a> has been accepted by IEEE Singal Processing Magazine.</p>
</li>
<li><p>[5-2019] Our full paper “Non-Invasive Remote Temperature Monitoring Using Microwave-Induced Thermoacoustic Imaging” has been accepted as an oral presentation in EMBC 2019.</p>
</li>
<li><p>[11-2018] First authored paper “Reconstruction and Registration of Large-Scale Medical Scene Using Point Clouds Data from Different Modalities” won the <b>Best Poster Award</b> in ACCAS 2018.</p>
</li>
</ul>
<h2>Education </h2>
<ul>
<li><p>Ph.D. student, Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, CA USA, Aug. 2018 - Present</p>
</li>
<li><p>B.E., Department of Biomedical Engineering, Tsinghua University, Beijing, China, Sep. 2014 - July 2018</p>
</li>
</ul>
<h2>Research Interests </h2>
<ul>
<li><p>Magnetic Resonance Imaging (MRI)</p>
</li>
<li><p>Image Reconstruction</p>
</li>
<li><p>Medical Imaging</p>
</li>
<li><p>Computer Vision</p>
</li>
<li><p>Computational Imaging</p>
</li>
<li><p>Inverse Problem</p>
</li>
<li><p>Deep Learning </p>
</li>
</ul>
<h2>Service</h2>
<ul>
<li><p>Reviewer for ISMRM 2022; EMBC 2022; MICCAI 2022</p>
</li>
<li><p>Reviewer for IEEE Transactions on Circuits and Systems for Video Technolog </p>
</li>
</ul>
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