Bowen Song

I am a Ph.D. candidate in Electrical and Computer Engineering at University of Michigan, Ann Arbor. Previously I worked at Microsoft as an applied scientist.

I obtained my Master’s degree in Computational and Mathematical Engineering from Stanford University, advised by Prof. Lei Xing; Bachelor’s degree in Mathematics and Statistics from University of Illinois, Urbana Champaign.

My research interests lie in applications and theories of generative models and diffusion models. Recently, my projects focus on efficient training and inference on high-dimensional data with diffusion models. My previous projects are also related to test-time adaptation and implicit neural representation learnings.

Selected Publications

DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction
Bowen Song*, Jason Hu*, Zhaoxu Luo, Jeffrey A Fessler, Liyue Shen
ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling

SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models
Zhaoxu Luo*, Bowen Song*, Liyue Shen
ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling

CoSIGN: Few-Step Guidance of ConSIstency Model to Solve General INverse Problems
Jiankun Zhao*, Bowen Song*, Liyue Shen
ECCV 2024

Solving inverse problems with latent diffusion models via hard data consistency
Bowen Song*, Soo Min Kwon*, Zecheng Zhang, Xinyu Hu, Qing Qu, Liyue Shen
ICLR 2024

Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing
Xinyu Hu, Pengfei Tang, Simiao Zuo, Zihan Wang, Bowen Song, Qiang Lou, Jian Jiao, Denis X Charles
ICLR 2024

PINER: Prior-Informed Implicit Neural Representation Learning for Test-Time Adaptation in Sparse-View CT Reconstruction
Bowen Song, Liyue Shen*, Lei Xing*
WACV 2023

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In this blog, we will explore interesting stuff here. Topics including: thoughts on technology development and societal impacts, reflection on methodological approach to problem solving, training for physical fitness, recovering from injury, classical music analysis and so on.

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