Rose Yu
Professor
Develops machine learning for large-scale spatiotemporal data and physics-guided deep learning for scientific discovery.
Rose Yu's lab at UCSD works in Machine Learning. 43 papers since 2023, 38 at top venues, mostly ICML and ICLR. 14 alumni, most often to Google. Applications close .
At a glance
- papers, 2023–2026
- 11/ year
- at T1 venues
- 88%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 14
Papers by yearfrom the lab homepage, 2023–2026
- 9 papers2026 · T1 9 · collecting
- 10 papers2025 · T1 9
- 12 papers2024 · T1 10
- 12 papers2023 · T1 10
T1 venuesOtherVenues: ICML, ICLR, NeurIPS, AISTATS, KDD
Research focuspaper titles that use each topic's words
- discovery4 papers
- spatiotemporal3 papers
- spatiotemporal data1 paper
- scientific1 paper
- scientific discovery0 papers
Paper list
From the lab homepage.
20269 papers
- ICML (T1)Breaking the Factorization Barrier in Diffusion Language Models (opens in a new tab)
- ICML (T1)Demystifying Mergeability: Interpretable Properties to Predict Model Merging Success (opens in a new tab)
- ICML (T1)Divide and Learn: Multi-Objective Combinatorial Optimization at Scale (opens in a new tab)
- ICML (T1)Emergence of Hierarchical Emotion Organization in Large Language Models (opens in a new tab)
- ICLR (T1)Hilbert: Recursively Building Formal Proofs with Informal Reasoning (opens in a new tab)
- ICML (T1)SimulCost: A Cost-Aware Benchmark and Toolkit for Automating Physics Simulations with LLMs (opens in a new tab)
- ICML (T1)Time Series, Vision, and Language: Exploring the Limits of Alignment in Contrastive Representation Spaces (opens in a new tab)
- ICML (T1)U-Cast: A Surprisingly Simple Frontier Probabilistic AI Weather Forecaster (opens in a new tab)
- ICLR (T1)Zephyrus: An Agentic Framework for Weather Science (opens in a new tab)
202510 papers
- ICML (T1)Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation (opens in a new tab)
- ICML (T1)AtlasD: Automatic Local Symmetry Discovery. (opens in a new tab)
- Learning for Dynamics and Control ConferenceBIGE : Biomechanics-informed GenAI for Exercise Science. (opens in a new tab)
- ICLR (T1)Can LLMs Understand Time Series Anomalies? (opens in a new tab)
- ICLR (T1)ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models (opens in a new tab)
- NeurIPS (T1)Conformal Prediction for Time-series Forecasting with Change Points (opens in a new tab)
- ICML (T1)Discovering Latent Structural Causal Models from Spatiotemporal Data. (opens in a new tab)
- NeurIPS (T1)Elucidated Rolling Diffusion Models for Probabilistic Forecasting of Complex Dynamics (opens in a new tab)
- ICML (T1)MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning. (opens in a new tab)
- ICML (T1)Understanding Mode Connectivity via Parameter Space Symmetry. (opens in a new tab)
202412 papers
- ICLR (T1)Copula Conformal Prediction for Multi-step Time Series Forecasting (opens in a new tab)
- Annual Conference on Learning for Dynamics and ControlData-Driven Simulator for Mechanical Circulatory Support with Domain Adversarial Neural Process (opens in a new tab)
- ICML (T1)Discovering Mixtures of Structural Causal Models from Time Series Data (opens in a new tab)
- ICLR (T1)Improving Convergence and Generalization Using Parameter Symmetries (opens in a new tab)
- ICML (T1)Latent Space Symmetry Discovery (opens in a new tab)
- PNAS (T1)Learning dynamical systems from data: An introduction to physics-guided deep learning (opens in a new tab)
- AISTATS (T1)Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes (opens in a new tab)
- ICML (T1)Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling (opens in a new tab)
- AISTATS (T1)On the Theoretical Expressive Power and Design Space of High Order Graph Transformers (opens in a new tab)
- NeurIPS (T1)Probablistic Emulation of a Global Climate Model with Spherical DYffusion (opens in a new tab)
- NeurIPS (T1)Symmetry-Informed Governing Equation Discovery (opens in a new tab)
- Annual Conference on Learning for Dynamics and ControlUnderstanding the Difficulty of Solving Cauchy Problems with PINNs (opens in a new tab)
202312 papers
- Nature Communications (T1)Accelerating network layouts using graph neural networks (opens in a new tab)
- Annual Conference on Learning for Dynamics and ControlAutomatic Integration for Fast and Interpretable Neural Point Processes (opens in a new tab)
- NeurIPS (T1)Automatic Integration for Spatiotemporal Neural Point Processes (opens in a new tab)
- KDD (T1)Deep Bayesian Active Learning for Accelerating Stochastic Simulation (opens in a new tab)
- ICML (T1)Disentangled Multi-Fidelity Deep Bayesian Active Learning (opens in a new tab)
- NeurIPS (T1)DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting (opens in a new tab)
- ICML (T1)Generative Adversarial Symmetry Discovery (opens in a new tab)
- ICLR (T1)Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts (opens in a new tab)
- TMLR (T1)Long-term Forecasting with TiDE: Time-series Dense Encoder (opens in a new tab)
- ICML (T1)On the Connection Between MPNN and Graph Transformer (opens in a new tab)
- Annual Conference on Learning for Dynamics and ControlProbabilistic Symmetry for Multi-Agent Dynamics (opens in a new tab)
- ICLR (T1)Symmetries, flat minima, and the conserved quantities of gradient flow. (opens in a new tab)
Where alumni went14 listed, 38% to industry
Industry 5Academia 8
Most often to
Apply to UCSDFall 2027
UCSD CSE Computer Science PhD
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- Single round
- Opens
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- GRE
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- English
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Not yet confirmed for this intake.
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