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Yu Sun
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Yu Sun

@YuSunMark
Assistant Professor @JHUECE, Postdoc @Caltech, Ph.D. @wustlcig, Researcher in Computational imaging.
Baltimore, MD
hcig.wse.jhu.edu
Joined November 2018
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  • user avatar
    Yu Sun
    @YuSunMark
    Jun 10
    🎓 Congratulations to my four graduating MS students — Haoyue Guan, Yuan Gao, Xuzhe Zha, and Yetao He! Proud to see them head off to outstanding PhD programs: Haoyue to Emory, Yuan to UNC, Xuzhe to JHU, and Yetao to UCLA. Wishing them all the best in their journeys ahead! 🌟
  • user avatar
    Yu Sun
    @YuSunMark
    Apr 18
    Happy to announce that I received the NSF CAREER Award 🤓
    engineering.jhu.edu
    Sun Receives NSF CAREER Award for Advancing AI-Driven Imaging - Department of Electrical and...
    Johns Hopkins Electrical and Computer Engineering Assistant Professor Yu Sun has received an NSF CAREER Award, one of the National Science Foundation’s highest honors for early-career faculty. The...
  • user avatar
    Yu Sun
    @YuSunMark
    Mar 5
    Paid summer intern opportunities for undergraduate at JHU DSAI!
    ai.jhu.edu
    Summer 2026 Internship Program - Data Science and AI Institute
    Applications are now closed. The Johns Hopkins Data Science and AI Institute (DSAI) invites Johns Hopkins undergraduate (rising juniors or seniors) and graduate students to apply for the DSAI Summer...
  • user avatar
    Yu Sun
    @YuSunMark
    Feb 23
    If you work on computational imaging / synthetic apertures: submit to CISA at Albany, 26-29 May 2026! ✅ 2-page abstract • ⏰ Feb 27 deadline (may extend) Keynotes: cisa-conference.org/keynotes/ Details: cisa-conference.org RT appreciated 🙏
  • user avatar
    Yu Sun
    @YuSunMark
    Jan 26
    PSI3D is in at IEEE Asilomar 2025: Plug-and-Play 3D Stochastic Inference with a Slice-wise Latent Diffusion Prior. Scalable 3D reconstruction by pairing your forward model with a 2D diffusion prior—it turns out to suffice! Paper:
    arXiv logo
    arxiv.org
    PSI3D: Plug-and-Play 3D Stochastic Inference with Slice-wise...
    Diffusion models are highly expressive image priors for Bayesian inverse problems. However, most diffusion models cannot operate on large-scale, high-dimensional data due to high training and...

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