Rose Yu is an associate professor at UC San Diego department of Computer Science and Engineering. She is a primary faculty with the AI Group and is affiliated with Halıcıoğlu Data Science Institute.

Her research interests lie primarily in machine learning, especially for large-scale spatiotemporal data. She is particularly excited about AI for scientific discovery. She has won DARPA Young Faculty Award, ECASE Award, NSF CAREER Award, Hellman Fellowship, Faculty Awards from JP Morgan, Meta, Google, Amazon, and Adobe, several Best Paper Awards, Best Dissertation Award at USC. She was named as MIT Technology Review Innovators Under 35 in AI.

For more details, see Curriculum Vitae.

Contact Info

  • E-mail: roseyu [at] ucsd [dot] edu
  • Phone: (858) 246-4724
  • Office: CSE 4216
  • Address: 9500 Gilman Drive, Mail Code 0404, La Jolla, CA 92093

News

Old News

Research

Scalable Deep Spatiotemporal Point Processes

Learning dynamics and detecting anomalies in spatiotemporal events

Accurate modeling of spatiotemporal events is critical for disaster response, logistic optimization and human mobility. We research efficient techniques to model spatiotemporal events by integrating deep learning with point processes, with the goal to improve forecasting and anomaly detection.

Read More
  • Automatic Integration for Spatiotemporal Neural Point Processes
    Zihao Zhou, Rose Yu
    Advances in Neural Information Processing Systems (NeurIPS), 2023
    [Paper] [Code]
  • Neural Point Process for Learning Spatiotemporal Event Dynamics
    Zihao Zhou, Xingyi Yang, Ryan Rossi, Handong Zhao, Rose Yu
    Annual Conference on Learning for Dynamics and Control (L4DC), 2022
    [Paper] [Code]

Sample Efficient Learning for Spatiotemporal Decision Making

Probabilistic deep sequence models for Bayesian optimization

Decision-making under uncertainty requires models that can generate not only point estimates but also confidence intervals. We investigate deep sequence models for Bayesian optimization in spatiotemporal domain, with the goal to reduce sample complexity, provide risk assessment, and guide policy making.

Read More
  • Disentangled Multi-Fidelity Deep Bayesian Active Learning
    Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu
    International Conference on Machine Learning (ICML), 2023
    [Paper]
  • Deep Bayesian Active Learning for Accelerating Stochastic Simulation
    Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023
    [Paper]

Physics-Guided Deep Learning for Spatiotemporal Dynamics

Incorporate first-principles into deep sequence models

Our physical world is intrinsically spatiotemporal. We design deep learning models that bridge the expressiveness of neural networks and the rich spatiotemporal structures from the data, addressing fundamental challenges of high-dimensionality, high-order correlation, non-linear dynamics and multi-resolution dependencies.

Read More
  • Generative Adversarial Symmetry Discovery
    Jianke Yang, Robin Walters, Nima Dehmamy, Rose Yu
    International Conference on Machine Learning (ICML), 2023
    [Paper] [Code]
  • Incorporating Symmetry into Deep Dynamics Models for Improved Generalization
    Rui Wang*, Robin Walters*, Rose Yu
    International Conference on Learning Representations (ICLR), 2021
    [Paper] [Code]

Deep Generative Models for Spatiotemporal Graphs

Structured learning models for spatiotemporal graphs

A graphical model can conveniently encode high-level structures. The nodes of the graph typically represent the problem components, and the edges capture their spatiotemporal interactions. We aim to study a novel deep learning framework with a primary focus on combining graphical models with black-box deep neural networks.

Read More
  • On the Connection Between MPNN and Graph Transformer
    Chen Cai, Truong Son Hy, Rose Yu, Yusu Wang
    International Conference on Machine Learning (ICML), 2023
    [Paper] [Code]
  • Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
     Nima Dehmamy, Albert-László Barabási, Rose Yu.
    Advances in Neural Information Processing Systems (NeurIPS), 2019
    [Paper] [Code]
For most up-to-date list of publications, see my Google Scholar page.

Conferences
  • Discovering Mixtures of Structural Causal Models from Time Series Data
    Sumanth Varambally, Yi-An Ma, Rose Yu
    International Conference on Machine Learning (ICML), 2024
    [Paper] [Code]
  • Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
    Ruijia Niu, Dongxia Wu, Kai Kim, Yi-An Ma, Duncan Watson-Parris, Rose Yu
    International Conference on Machine Learning (ICML), 2024
    [Paper] [Code]
  • Latent Space Symmetry Discovery
    Jianke Yang, Nima Dehmamy, Robin Walters, Rose Yu
    International Conference on Machine Learning (ICML), 2024
    [Paper] [Code]
  • Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
    Dongxia Wu, Tsuyoshi Ide, Aurelie Lozano, Georgios Kollias, Jirı Navratil, Naoki Abe, Yian Ma, Rose Yu
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
    [Paper] [Code]
  • On the Theoretical Expressive Power and Design Space of High Order Graph Transformers
    Cai Zhou, Rose Yu, Yusu Wang.
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
    [Paper] [Code]
  • Data-Driven Simulator for Mechanical Circulatory Support with Domain Adversarial Neural Process
    Sophia Sun, Wenyuan Chen, Zihao Zhou,Sonia Fereidooni,Elise Jortberg, Rose Yu
    Annual Conference on Learning for Dynamics and Control (L4DC), 2024
    [Paper] [Code]
  • Understanding the Difficulty of Solving Cauchy Problems with PINNs
    Tao Wang, Bo Zhao, Sicun Gao, Rose Yu
    Annual Conference on Learning for Dynamics and Control (L4DC), 2024
    [Paper]
  • Copula Conformal Prediction for Multi-step Time Series Forecasting
    Sophia Sun, Rose Yu
    International Conference on Learning Representations (ICLR), 2024
    [Paper] [Code]
  • Improving Convergence and Generalization Using Parameter Symmetries
    Bo Zhao, Robert M. Gower, Robin Walters, Rose Yu
    International Conference on Learning Representations (ICLR), 2024
    [Paper] [Code]
  • DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting
    Salva Rühling Cachay, Bo Zhao, Hailey James, Rose Yu
    Advances in Neural Information Processing Systems (NeurIPS), 2023
    [Paper] [Code] [Blog]
  • Automatic Integration for Spatiotemporal Neural Point Processes
    Zihao Zhou, Rose Yu
    Advances in Neural Information Processing Systems (NeurIPS), 2023
    [Paper] [Code]
  • Deep Bayesian Active Learning for Accelerating Stochastic Simulation
    Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023
    [Paper] [Code]
  • Generative Adversarial Symmetry Discovery
    Jianke Yang, Robin Walters, Nima Dehmamy, Rose Yu
    International Conference on Machine Learning (ICML), 2023
    [Paper] [Code]
  • Disentangled Multi-Fidelity Deep Bayesian Active Learning
    Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu
    International Conference on Machine Learning (ICML), 2023
    [Paper] [Code]
  • On the Connection Between MPNN and Graph Transformer
    Chen Cai, Truong Son Hy, Rose Yu, Yusu Wang
    International Conference on Machine Learning (ICML), 2023
    [Paper] [Code]
  • Probabilistic Symmetry for Multi-Agent Dynamics
    Sophia Sun, Robin Walters, Jinxi Li, Rose Yu
    Annual Conference on Learning for Dynamics and Control (L4DC), 2023
    [Paper] [Code]
  • Automatic Integration for Fast and Interpretable Neural Point Processes
    Zihao Zhou, Rose Yu
    Annual Conference on Learning for Dynamics and Control (L4DC), 2023
    [Paper] [Code]
  • Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts
    Rui Wang, Yihe Dong, Sercan Ö. Arik, Rose Yu
    International Conference on Learning Representations (ICLR), 2023
    [Paper] [Code]
  • Symmetries, flat minima, and the conserved quantities of gradient flow.
    Bo Zhao, Iordan Ganev, Robin Walters, Rose Yu, Nima Dehmamy
    International Conference on Learning Representations (ICLR), 2023
    [Paper] [Code]
  • Symmetry Teleportation for Accelerated Optimization
    Bo Zhao, Nima Dehmamy, Robin Walters, Rose Yu
    Advances in Neural Information Processing Systems (NeurIPS), 2022
    [Paper] [Code]
  • Meta-Learning Dynamics Forecasting Using Task Inference
    Rui Wang, Robin Walters, Rose Yu
    Advances in Neural Information Processing Systems (NeurIPS), 2022
    [Paper] [Code]
  • Multi-fidelity Hierarchical Neural Processes
    Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022
    [Paper] [Code]
  • Approximately Equivariant Networks for Imperfectly Symmetric Dynamics
    Rui Wang, Robin Walters, Rose Yu
    International Conference on Machine Learning (ICML), 2022
    [Paper] [Code]
  • LIMO: Latent Inceptionism for Targeted Molecule Generation
    Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael Gilson, Rose Yu
    International Conference on Machine Learning (ICML), 2022
    [Paper] [Code] [Website]
  • Neural Point Process for Learning Spatiotemporal Event Dynamics
    Zihao Zhou, Xingyi Yang, Ryan Rossi, Handong Zhao, Rose Yu
    Annual Conference on Learning for Dynamics and Control (L4DC), 2022
    [Paper] [Code]
  • Automatic Symmetry Discovery with Lie Algebra Convolutional Network
    Nima Dehmamy, Robin Walters, Yanchen Liu, Dashun Wang, Rose Yu
    Advances in Neural Information Processing Systems (NeurIPS), 2021
    [Paper] [Code]
  • Quantifying Uncertainty in Deep Spatiotemporal Forecasting
    Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021
    [Paper] [Code]
  • Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems
    Rui Wang, Danielle Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu
    Annual Conference on Learning for Dynamics and Control (L4DC), 2021
    [Paper] [Code]
  • Traffic Forecasting using Vehicle-to-Vehicle Communication
    Steven Wong, Lejun Jiang, Robin Walters, Tamás G. Molnár, Gábor Orosz, Rose Yu
    Annual Conference on Learning for Dynamics and Control (L4DC), 2021
    [Paper] [Code]
  • Trajectory Prediction using Equivariant Continuous Convolution
    Robin Walters, Jinxi (Leo) Li, Rose Yu
    International Conference on Learning Representations (ICLR), 2021
    [Paper] [Code]
  • Incorporating Symmetry into Deep Dynamics Models for Improved Generalization
    Rui Wang*, Robin Walters*, Rose Yu
    International Conference on Learning Representations (ICLR), 2021
    [Paper] [Code]
  • Learning Disentangled Representations of Video with Missing Data
    Armand Comas Massague, Chi Zhang, Zlatan Feric, Octavia Camps, Rose Yu
    Advances in Neural Information Processing Systems (NeurIPS), 2020
    [Paper] [Code]
  • Deep Imitation Learning for Bimanual Robotic Manipulation
    Fan Xie, Alex Chowdhury, Clara De Paolis, Linfeng Zhao, Lawson Wong, Rose Yu
    Advances in Neural Information Processing Systems (NeurIPS), 2020
    [Paper] [Code]
  • Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
    Jung Yeon (John) Park, Kenneth (Theo) Carr, Stephan Zheng, Yisong Yue, Rose Yu
    International Conference on Machine Learning (ICML), 2020
    [Paper] [Code]
  • Towards Physics-informed Deep Learning for Turbulent Flow Prediction
     Rui Wang, Karthik Kashinath, Mustafa Mustafa, Adrian Albert, Rose Yu.
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020
    [Paper] [Code] [Blog]

  • Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
     Nima Dehmamy, Albert-László Barabási, Rose Yu.
    Advances in Neural Information Processing Systems (NeurIPS), 2019
    [Paper] [Code]

  • NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
     Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue.
    Advances in Neural Information Processing Systems (NeurIPS), 2019
    [Paper] [Code]

  • Neural Lander: Stable Drone Landing Control using Learned Dynamics
    Guanya Shi*, Xichen Shi*, Michael O'Connell*, Rose Yu, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
    International Conference on Robotics and Automation (ICRA), 2019
    [Paper] [Demo]

  • Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
     Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu.
    International Conference on Learning Representations (ICLR), 2018
    [Paper] [Code]

  • Tensor Regression Meets Gaussian Processes
     Rose Yu, Guangyu Li, Yan Liu.
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2018
    [Paper]

  • Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting
     Rose Yu, Yaguang Li, Ugur Demiryurek, Cyrus Shahabi, Yan Liu.
    SIAM International Conference on Data Mining (SDM), 2017
    [Paper]

  • Learning from Multiway Data: Simple and Efficient Tensor Regression
     Rose Yu, Yan Liu.
    International Conference on Machine Learning (ICML), 2016
    [Paper] [Slide] [BibTex]

  • Latent Space Model for Road Networks to Predict Time-Varying Traffic
     Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, Linhong Zhu, Rose Yu, Yan Liu
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016
    [Paper]

  • Geographic Segmentation via Latent Poisson Factor Model
     Rose Yu, Andrew Gelfand, Suju Rajan, Cyrus Shahabi, Yan Liu.
    ACM International Conference on Web Search and Data Mining (WSDM), 2016
    [Paper] [BibTex]

  • Accelerated Online Low-Rank Tensor Learning for Multivariate Spatio-Temporal Streams
     Rose Yu, Dehua Cheng, Yan Liu.
    International Conference on Machine Learning (ICML), 2015
    [Paper] [Supplementary] [Code] [Video] [Slide] [BibTex]

  • Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning
     Rose Yu*, Mohammad Taha Bahadori*, Yan Liu. (*Equal Contributions)
    Advances in Neural Information Processing Systems (NeurIPS), 2014 Spotlight
    [Paper] [Supplementary] [Code] [BibTex]

  • GLAD: Group Anomaly Detection in Social Media Analysis
     Rose Yu, Xinran He, Yan Liu.
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2014
    [Paper] [Code] [ACM Dataset] [Slide] [BibTex]

  • A Feasible Nonconvex Relaxation Approach to Feature Selection
     Cuixia Gao, Naiyan Wang, Qi Yu, Zhihua Zhang.
    AAAI Conference on Artificial Intelligence (AAAI), 2011
    [Paper] [BibTex]

    • Journals
  • Learning dynamical systems from data: An introduction to physics-guided deep learning
     Rose Yu, Rui Wang
    Proceedings of the National Academy of Sciences, 2024
    [Paper]
  • Long-term Forecasting with TiDE: Time-series Dense Encoder
     Abhimanyu Das, Weihao Kong, Andrew Leach, Shaan Mathur, Rajat Sen, Rose Yu
    Transactions on Machine Learning Research, 2023
    [Paper] [Code] [Blog]
  • Accelerating network layouts using graph neural networks
     Csaba Both, Nima Dehmamy, Rose Yu, and Albert-Laszlo Barabasi
    Nature Communications, 2023
    [Paper]
  • Physics-informed machine learning: case studies for weather and climate modelling
    K. Kashinath, M. Mustafa, A. Albert, J-L. Wu, C. Jiang, S. Esmaeilzadeh, K. Azizzadenesheli, R. Wang, A. Chattopadhyay, A. Singh, A. Manepalli, D. Chirila, R. Yu, R. Walters, B. White, H. Xiao, H. A. Tchelepi, P. Marcus, A. Anandkumar, P. Hassanzadeh.
    Philosophical Transactions of the Royal Society A, 2021
    [Paper]
  • Spatio-Temporal Analysis of Social Media Data
    Rose Yu, Yan Liu.
    Encyclopedia of GIS , 2016
    [Paper]
  • A Survey on Social Media Anomaly Detection
     Rose Yu, Huida Qiu, Zhen Wen, Ching-Yung Lin, Yan Liu
    ACM SIGKDD Explorations , 2016
    [Paper]
  • GLAD: Group Anomaly Detection in Social Media Analysis (journal version)
    Rose Yu, Xinran He, Yan Liu.
    ACM Transactions on Knowledge Discovery in Data (TKDD), 2015
    [Paper]

    • Technical Talks
  • Incorporating Symmetry for Learning Spatiotemporal Dynamics, IPAM 2023
  • Machine Learning for Climate KITP conference 2021
  • One World MINDS Seminar 2021
  • MICS Research Summit 2021
  • Keynote at Climate Informatics 2018

    • Group

    Current

    PhD Students & Postdocs
    Undergraduates & Master's Students

    Alumni

    PhD Students & Postdocs
    Undergraduates & Master's Students

    Teaching

    CSE 291 (B00) Generative AI

    Fall 2023   Fall 2022   Fall 2020

    Description: Deep generative models combine the generality of probabilistic reasoning with the scalability of deep learning. This research area is at the forefront of deep learning and has given state-of-the-art results in text generation, video synthesis, and molecular design, among many others. This course will cover recent advances in deep generative models, including variational autoencoders, generative adversarial networks, autoregressive models, normalizing flow, and diffusion models. This is a graduate-level course with an emphasis on mathematical principles as well as practical know-how. The course will be a combination of lectures, student presentations, and team projects.

    CSE 251B Deep Learning

    Spring 2023

    Description: This course covers the fundamentals of deep neural networks at the graduate level. We introduce multi-layer perceptrons, back-propagation, and automatic differentiation. We will also discuss Convolutional Neural Networks, Recurrent Neural Networks, Transformers, and advanced topics in deep learning. The course will be a combination of lectures, presentations, and machine learning competitions.

    CSE 151B Deep Learning

    Spring 2023   Spring 2022   Spring 2021

    Description: This course covers the fundamentals of deep neural networks at the undergraduate level. We introduce linear regression, multi-layer perceptrons, back-propagation, and automatic differentiation. We will also discuss Convolutional Neural Networks, Recurrent Neural Networks, and Transformers. The course will be a combination of lectures, presentations, and machine learning competitions.

    CSE 259 AI Seminar

    Spring 2022

    Description: This seminar course focuses on discussing the state-of-the-art research and cutting edge technology in AI. We will invite researchers from academia and industry to share their most recent work in AI and machine learning.

    CSE 291 (5) Deep Reinforcement Learning

    Fall 2021

    Description: Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research is at the forefront of machine learning. Deep RL is able to solve a wide range of complex decision-making tasks, opening up new opportunities in domains such as healthcare, robotics, smart grids, finance, and many more. This class will cover recent advances in deep RL, including imitation learning, Policy Gradients, Deep Q-learning, Actor-Critic algorithms, model-based RL, and inverse RL. The course will be a combination of lectures, student presentations, and projects.

    Courses Taught at Northeastern University

    CS 7140 Advanced Machine Learning, Spring 2020

  • Course Website

    • CS 3950/4950 Introduction to Computer Science Research, Fall 2019

  • 3950 Course Website    4950 Course Website

    • CS 7180 Special Topics in AI: Deep Learning, Spring 2019

  • Course Website

    • CS 6140 Machine Learning, Fall 2018

  • Course Website    Course Syllabus