Education
Research Experience
Publications
IMS³: Breaking Distributional Aggregation in Diffusion-Based Dataset Distillation
Chenru Wang*; Yunyi Chen*; Zijun Yang; Joey Tianyi Zhou; Chi Zhang#. (* equal contribution, # corresponding author)
IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026
Developed Inversion-Matching (IM) to align denoising and inversion trajectories, broadening distributional coverage of distilled samples. Designed Selective Subgroup Sampling (S³) to improve inter-class separability and boundary coverage, achieving state-of-the-art performance among diffusion-based distillation methods.
MGPO: Manifold-Guided Diffusion Alignment for Task-Aware Dataset Distillation
Yunyi Chen*; Chenru Wang*; Xinyi Ye; Zexin Zheng; Chi Zhang#. (* equal contribution, # corresponding author)
European Conference on Computer Vision 2026 (Under Review)
Proposed Manifold-Guided Policy Optimization (MGPO), formulating diffusion-based dataset distillation as a multi-objective reinforcement learning problem. Combined pixel-space discriminative rewards with latent-space manifold rewards guided by class-wise MST skeletons, improving downstream classification, detection, and segmentation performance.
Skills
Awards & Scholarships
Course Projects
- Achieved real-time state sync and visualization in Unity using the Default Visualization Suite: scans, map, local/global costmaps, route plan, and goal positions
- Implemented hybrid obstacle avoidance: ROS2 SLAM/Nav stack + Unity NavMesh, with runtime switching and goal-skip for unreachable targets
- Analyzed natural gas usage in Dutch households and visualized energy consumption differences across cities and household types
- Built a linear regression model to predict consumption and identified atypical consumption patterns through data cleaning and visualization
- Implemented recommender systems (SVD), Google PageRank (NBA team ranking), eigenvector clustering (NBA player networks), and data mining (shopping patterns)
- Compared Katz and Exponential centrality for network analysis and performed polynomial fitting using least squares