My name is Dan Wang, and I am an incoming Assistant Professor at the University of Arizona. I am currently a Postdoc at the University of California San Diego, where I am advised by Professor Ravi Ramamoorthi. Previously, I worked at the University of Copenhagen in association with the AI Pioneer Centre, where I was advised by Professor Serge Belongie. I received my Ph.D. degree in Electrical and Computer Engineering at the University of British Columbia, where I was supervised by Professor Z. Jane Wang and Professor Tim Salcudean. I obtained my Master's and Bachelor's degrees from Beihang University.

My research lies at the intersection of Computer Graphics, Computer Vision, and Artificial Intelligence, with the overarching goal of advancing AI systems that seamlessly integrate environmental understanding with adaptive interaction to enhance human life. I focus on developing next-generation 3D reconstruction and rendering methods for human characters and complex dynamic scenes. By unifying classical graphics principles with modern deep learning approaches, my work aims to build systems that are not only high-performing, but also interpretable and physically grounded.

I am recruiting Ph.D. students to join my research group, with openings expected as early as Spring 2027 or Fall 2027. Please apply through the official Ph.D. application process at the University of Arizona and list my name in your application. You are also welcome to send me your CV/resume, transcripts, and a brief description of your research interests.

Research Vision

  1. Physics-Informed World Simulation: Faithfully capture and understand real-world physical rules and actions and enable simulating and generarizing to physical actions, material behaviors, and complex real-world scenarios.
  2. Physical Scene Perception and Reasoning: Infer geometry, motion, physical properties, and causal relationships from visual and multimodal observations, enabling machines to reason about how physical scenes evolve and how agents can interact with them.
  3. Dynamic World Generation and Reconstruction: Reconstruct and generate temporally and semantically consistent 3D/4D worlds from sparse or partial observations, supporting controllable scene creation, simulation-ready digital twins, and immersive embodied-AI environments.
  4. Explainable & Trustworthy AI: Transform opaque neural models into transparent and interpretable systems with explicit semantic structure, ensuring reliable, controllable, and accountable AI-driven decisions.

Selected Awards

Publications

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(* indicates equal contributions)

PhysConvex teaser
PhysConvex: Physics-Informed 3D Dynamic Convex Radiance Fields for Reconstruction and Simulation
Dan Wang, Xinrui Cui, Serge Belongie, Ravi Ramamoorthi
arXiv 2026 / Project Page
ArtMesh: Part-Aware Articulated Mesh Fields with Motion-Consistent Dynamics
Sylvia Yuan*, Dan Wang*, Ravi Ramamoorthi, Xinrui Cui
arXiv 2026 / Dataset
SR teaser
Revisiting the Perception-Distortion Trade-off with Spatial-Semantic Guided Super-Resolution
Dan Wang, Haiyan Sun, Shan Du, Z. Jane Wang, Zhaochong An, Serge Belongie, Xinrui Cui
arXiv 2026 / Project Page
SR teaser
Interpretable Concept Evolution Flow in Diffusion Models
Yuxuan Liu, Dan Wang, Xinrui Cui
Project Page / Coming soon, 2026
RAIGen teaser
RAIGen: Rare Attribute Identification in Text-to-Image Generative Models
Silpa Vadakkeeveetil Sreelatha, Dan Wang, Serge Belongie, Muhammad Awais, Anjan Dutta
ICML 2026
Differentiable Light Transport with Gaussian Surfels via Adapted Radiosity for Efficient Relighting and Geometry Reconstruction
Kaiwen Jiang, Jia-Mu Sun, Zilu Li, Dan Wang, Tzu-Mao Li, Ravi Ramamoorthi
SIGGRAPH Asia 2025 (ACM TOG) / Project Page
InNeRF teaser
StyleMorpheus: Learning a StyleGAN-based 3D-aware morphable face model with a disentangled style space
Peizhi Yan, Rabab Ward, Dan Wang, Qiang Tang, Shan Du
Neurocomputing 2025 / Project Page
Coarse-To-Fine Tensor Trains for Compact Visual Representations
Sebastian Loeschcke, Dan Wang, Christian Leth-Espensen, Serge Belongie, J Michael Kastoryano, Sagie Benaim
ICML 2024 / Project Page
InNeRF teaser
InNeRF: Learning Interpretable Radiance Fields for Generalizable 3D Scene Representation and Rendering
Dan Wang, Xinrui Cui
ACM Multimedia 2024 / Project Page
COSeg teaser
Rethinking Few-shot 3D Point Cloud Semantic Segmentation
Zhaochong An, Guolei Sun, Yun Liu, Fayao Liu, Zongwei Wu, Dan Wang, Luc Van Gool, Serge Belongie
CVPR 2024 / Project Page
InNeRF teaser
Contrastive-enhanced Domain Generalization with Federated Learning
Xinhui Yu, Dan Wang, Martin McKeown, Z. Jane Wang
IEEE TAI 2023
HeadNerf+ teaser
Learning Disentangled Features for Nerf-Based Face Reconstruction
Peizhi Yan, Rabab Ward, Dan Wang, Qiang Tang, Shan Du
ICIP 2023 / Project Page
GRSL teaser
Cross-Domain Few-Shot Contrastive Learning for Hyperspectral Images Classification
Suhua Zhang, Zhikui Chen, Dan Wang, Z. Jane Wang
IEEE GRSL 2022
IEEE TIM 2022 teaser
Transformer-Based End-to-End Anatomical and Functional Image Fusion
Jing Zhang, Aiping Liu, Dan Wang, Yu Liu, Z. Jane Wang, Xun Chen
IEEE TIM 2022
EVolT teaser
Multi-View 3D Reconstruction With Transformers
Dan Wang, Xinrui Cui, Xun Chen, Zhengxia Zou, Tianyang Shi, Septimiu Salcudean, Z. Jane Wang, Rabab Ward
ICCV 2021 Oral Presentation / Project Page
CHAIN teaser
Interpreting Bottom-Up Decision-Making of CNNs via Hierarchical Inference
Dan Wang, Xinrui Cui, Xun Chen, Rabab Ward, Z. Jane Wang
IEEE TIP 2021 / Project Page
CHIP teaser
CHIP: Channel-Wise Disentangled Interpretation of Deep Convolutional Neural Networks
Xinrui Cui, Dan Wang, Z. Jane Wang
IEEE TNNLS 2020 / Project Page
FLOWIN teaser
Feature-Flow Interpretation of Deep Convolutional Neural Networks
Xinrui Cui, Dan Wang, Z. Jane Wang
IEEE TMM 2020 / Project Page
MINT teaser
Multi-Scale Interpretation Model for Convolutional Neural Networks: Building Trust Based on Hierarchical Interpretation
Xinrui Cui, Dan Wang, Z. Jane Wang
IEEE TMM 2019 / Project Page
FLOWIN teaser
Robust Sparse Unmixing for Hyperspectral Imagery
Dan Wang, Zhenwei Shi, Xinrui Cui
IEEE TGRS 2018
teaser
Multi-feature Sea-land Segmentation based on Pixel-wise Learning for Optical Remote-sensing Imagery
Dan Wang, Xinrui Cui, Fengying Xie, Zhiguo Jiang, Zhenwei Shi
IJRS 2017
teaser
Collaborative sparse unmixing of hyperspectral data using L2,P norm
Dan Wang, Zhenwei Shi, Wei Tang
IGARSS 2016

Contact

I welcome research collaborations and academic conversations related to computer vision, graphics, machine learning, neural rendering, and interpretable AI. For research inquiries, please reach out through my academic profile or social links above.