Jiaming Liu (@Jiaming__Liu) header
Jiaming Liu (@Jiaming__Liu) profile picture

Postdoc @Stanford,PhD in EE,computational imaging group (CIG)@wustlcig

University City, MO
Joined December 2018
Nice work!
Replying to @ZihaoZou1217
The paper is accepted to SIGGRAPH Asia 2026. Big Thanks to @praneethchk, @Jiaming__Liu, Hengyu Lian, and Nagasai Thadishetty
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Jiaming Liu retweeted
The paper is accepted to SIGGRAPH Asia 2026. Big Thanks to @praneethchk, @Jiaming__Liu, Hengyu Lian, and Nagasai Thadishetty
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Jiaming Liu retweeted
🧵 [1/3] New preprint: Generative Translation Priors: Bayesian Imaging with Cross-Modality Image Translation arxiv.org/abs/2608.28872 #BayesianImaging #GenerativeAI
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🚀 New preprint: Next-Dense-Stride Prediction for Multimodal Autoregressive Visual Modeling Better Autoregressive visual modeling via coarse-to-fine on a single compact grid: sparse tokens first, then denser strides, decoded in parallel. 🌐 Project page: uw-cig.github.io/densear_pag…
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Jiaming Liu retweeted
Excited to announce my internship work @GoogleAI has been accepted to #NeurIPS2025. This work introduces Kernel Density Steering (KDS), a training-free method to improve the performance and stability of image-to-image diffusion models. KDS achieves this by simultaneously sampling an N-particle ensemble to derive an empirical density while applying explicit mode-seeking, which leads to better consistency and fewer artifacts. Grateful for my incredible hosts and collaborators: @2ptmvd, @docmilanfar, @KangfuM, Mojtaba Sahraee-Ardakan and @ukmlv.
For better consistency and fewer artifacts in im2im diffusion models, consider Kernel Density Steering (KDS): It is inference time and training-free; uses an N-particle ensemble to derive an empirical density, & explicit mode-seeking to push samples to high-density regions 1/2
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Jiaming Liu retweeted
Excited to announce my work during @GoogleAI internship is now on arXiv! Grateful for my incredible hosts and collaborators: @2ptmvd, @docmilanfar, @KangfuM, Mojtaba Sahraee-Ardakan and @ukmlv. Please check out the paper here: [arxiv.org/abs/2507.05604].
For better consistency and fewer artifacts in im2im diffusion models, consider Kernel Density Steering (KDS): It is inference time and training-free; uses an N-particle ensemble to derive an empirical density, & explicit mode-seeking to push samples to high-density regions 1/2
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Jiaming Liu retweeted
The @OdenInstitute Peter O'Donnell Jr. Postdoc Fellowship program is accepting applications (deadline Dec 1). Contact me if you are interested in applying and working together on 💻🧲🖼️ (computational magnetic resonance imaging)! oden.utexas.edu/research/pro…
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Jiaming Liu retweeted
I am looking to hire several Ph.D. students starting Fall 2025 @JHUECE. Anyone who is passionate for computational imaging/machine learning/statistical inference is welcome to apply. MS/Postdoc positions are also available! Here is the link: forms.gle/dFyftuPTSCqSTaMK9 #PhDposition
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➤ M. Renaud, J. Liu, V. de Bortoli, A. Almansa, and U. S. Kamilov, “Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models” Authors on X: @Jiaming__Liu @ValentinDeBort1 @AndresAlmansaR @ukmlv Link: arxiv.org/abs/2310.03546 (3/3)
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New paper "FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration" presents a new conditional diffusion model for efficiently restoring videos using dedicated priors on faces. ⭑ Read here: wustl-cig.github.io/flairwww… ⭑ Video: youtu.be/BKuvhpfsOWM?feature…
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📢 We have an open postdoc position at ENS Lyon on multilevel unrolled & plug-and-play methods. See details here: tachella.github.io/assets/pd… We would highly appreciate a retweet
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Good news in medical generative AI!🎉🎉 We just published the preprint of the MONAI Generative Models Extension! Check out our latest experiments with 2D and 3D data, ControlNets and 3D Cascaded Diffusion Models! arxiv.org/abs/2307.15208 #AI #MedicalImaging #GenerativeModels
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📢📢 Release of DeepInverse library 📢📢 After months of intense work, we are releasing the first stable version of DeepInverse deepinv.github.io/deepinv/, a PyTorch library for solving inverse problems with deep learning. with @HuraultSamuel, @MatthieuTerris and @ddongchen A 🧵
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ICCP 2023 call for posters and demo submissions now out! Posters and demos provide an opportunity to showcase previously published or yet-to-be-published work. Due on June 15, 2023. iccp2023.iccp-conference.org…
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CIG is seeking a postdoctoral researcher to join our team in 2023. The successful candidate will work on an exciting collaborative project at the intersection of computational imaging, neuroscience, and deep learning. Please help us to spread the word: cigroup.wustl.edu/opportunit….
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Polyak-Łojasiewicz inequality generalizes strong convexity, and a simple condition to ensure linear converge of gradient descent even for non-convex functions. You should read the very nice and clear paper of @MarkSchmidtUBC and collaborators about this! arxiv.org/abs/1608.04636
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Computational Imaging Group (CIG), Spring 2022
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Our paper "Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition" has been accepted to @NeurIPSConf 2021. We present theory that relates compressive sensing using generative models to plug-and-play priors (PnP). arxiv.org/abs/2106.03668
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