Yusu Wang
Professor
Develops geometric and topological data analysis methods and graph neural networks with theoretical guarantees.
Yusu Wang's lab at UCSD works on learning theory & optimization, graph learning and geometry processing & modeling. 35 papers since 2023, 24 at top venues, mostly ICML and NeurIPS. Applications close .
At a glance
- papers, 2023–2026
- 9/ year
- at T1 venues
- 69%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 8 papers2026 · T1 6 · collecting
- 9 papers2025 · T1 8
- 10 papers2024 · T1 5
- 8 papers2023 · T1 5
T1 venuesOtherVenues: ICML, NeurIPS, AISTATS, ALT, AAAI
Research focuspaper titles that use each topic's words
- Graph learning16 papers
- Learning theory & optimization9 papers
- Geometry processing & modeling0 papers
- develops geometric0 papers
- analysis methods0 papers
Paper list
From the lab homepage.
20268 papers
- NeurIPS (T1)Beyond FlatWalks: Compositional Abstraction for Autoregressive Graph Generation
- FCCMDiffRouter: A Differentiable Routing Framework for UltraScale FPGAs
- NeurIPS (T1)Do Graph Neural Networks Learn Generalizable Algorithms for Clustering Graphs?
- ICML (T1)General and Efficient Steering of Unconditional Diffusion
- COLT (T1)Graph neural networks extrapolate out-of-distribution for shortest paths
- ACM Transactions on Design Automation of Electronic SystemsTopoGeoNet: A Heterogeneous Model for End-to-End Congestion Prediction and Optimization in FPGA Placement
- ICML (T1)Two Calm Ends and the Wild Middle: A Geometric Picture of Memorization in Diffusion Models
- ICML (T1)Which Algorithms Can Graph Neural Networks Learn?
20259 papers
- CGTAApproximation algorithms for 1-Wasserstein distance between persistence diagrams
- ICML (T1)De-Coupled NeuroGF for Shortest Path Distance Approximations on Large Terrains
- NeurIPS (T1)Differentiable extensions with rounding guarantees for combinatorial optimization over permutations
- NeurIPS (T1)Effective Neural Approximations for Geometric Optimization Problems
- ICML (T1)Elucidating Flow Matching ODE Dynamics with Respect to Data Geometries
- JMLR (T1)Enhancing Graph Representation Learning with Localized Topological Features
- KDD (T1)Explaining GNN Explanations with Edge Gradients
- PNAS (T1)Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning
- NeurIPS (T1)Seeds of Structure: Patch PCA Reveals Universal Compositional Cues in Diffusion Models
202410 papers
- ICML (T1)Comparing graph transformers via positional encodings
- AISTATS (T1)DE-HNN: An effective neural model for Circuit Netlist representation
- ALTDistances for Markov Chains, and Their Differentiation
- Annual AAAI Conference on Artificial Intelligence (AAAI )Learning ultrametric trees for optimal transport regression
- AAAI (T1)NN-Steiner: A Mixed Neural-Algorithmic Approach for the Rectilinear Steiner Minimum Tree Problem
- AISTATS (T1)On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers
- ICML (T1)Position paper: Challenges and Opportunities in Topological Deep Learning
- IEEE Trans. Signal ProcessingPrincipal Component Analysis in Space Forms
- SIAM Journal on Matrix Analysis and ApplicationsRandom Walks, Conductance, and Resistance for the Connection Graph Laplacian
- ALTUniversal Representation of Permutation-Invariant Functions on Vectors and Tensors
20238 papers
- SoCGA generalization of the persistent Laplacian to simplicial maps
- LoGCycle Invariant Positional Encoding for Graph Representation Learning
- AISTATS (T1)Implicit Graphon Neural Representation
- NeurIPS (T1)Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions
- RSAOn the clique number of noisy random geometric graphs
- ICML (T1)On the Connection Between MPNN and Graph Transformer
- ICML (T1)The Numerical Stability of Hyperbolic Representation Learning
- ICML (T1)Understanding Oversquashing in GNNs through the Lens of Effective Resistance
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UCSDFall 2027
UCSD CSE Computer Science PhD
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