Industrial Statistics and Data Analytics Lab

Industrial Statistics and Data Analytics Lab

Industrial Statistics and Data Analytics Lab, Department of Industrial Engineering, Tsinghua University

Awards

Society, National and International Awards

• Elsevier Highly Cited Chinese Researchers, 2023.

• 8th China Association for Science and Technology Young Talent Support Program, 2022-2024.

• Ministry of Education Outstanding Scientific Research Achievement Award (Science and Technology) – Second Prize, 2023.

• Brumbaugh Award, American Society for Quality (ASQ), 2019.

• Tsinghua University Young Faculty Teaching Competition, Second Prize, 2024.

• Winner of Best Paper Competition in 2025 INFORMS Conference on Quality, Statistics and Reliability, for the paper “Spatial In-Profile Monitoring via Latent Tensor Gaussian Process with Mixed Effects”.

• Best Application Paper Award in IISE Transactions for the paper “A Markov-Switching Hidden Heterogeneous Network Autoregressive Model for Multivariate Time Series Data with Multimodality” 2025.

• Best Paper Finalist Award in the QCRE Section of Industrial and Systems Engineering Research Conference (ISERC), for the paper “Interactive Resource Planning and Change Detection via Multi-agent Reinforcement Learning,” 2025.

• Feature Article “Federated multi-task Bayesian network learning in the presence of overlapping and distinct variables”, in ISE Magazine, 2025.

• Runner-up of INFORMS 2024 Data Mining and Decision Analytics Workshop Best Paper Competition, for the paper “Heterogeneous Multivariate Functional Time Series Modeling: A State Space Approach”, 2024.

• Best Paper Finalist Award of the INFORMS 2024 Quality Statistics and Reliability Section, for the paper “Spatial In-Profile Monitoring via Latent Tensor Gaussian Process with Mixed Effects,” 2024.

• Feature Article “Online Directed-structural Change-point Detection: A Segment-wise Time-varying Dynamic Bayesian Network Approach”, in ISE Magazine, 2024.

• Runner-up of INFORMS 2023 Data Mining and Decision Analytics Workshop Best Paper Competition, for the paper “Thompson Sampling based Partially Observable Online Monitoring Approach for Large Dynamic Networks”, 2023.

• Best Paper Finalist Award of 2023 QCRE Best Track Paper Competition, for the paper “Graph-aware Tensor Topic Models for Individualized Passenger Travel Pattern Clustering”, 2023.

• Second Place of 16th INFORMS Data Mining and Decision Analytics Workshop Poster Competition, for the paper “Holistic Prediction for Public Transport Crowd Flows: A Spatio Dynamic Graph Network Approach”, 2021.

• Runner-up of 16th INFORMS Data Mining and Decision Analytics Workshop Theoretical Best Paper Competition, for the paper “Low-rank Sparse Tensor Decomposition with Ridge Regularized Subspace Clustering for Metro Passenger Flow Modeling”, 2021.

• Best Paper Award of 2020 IEEE Conference on Automation Science and Engineering for the paper “Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile”.

• Best Paper Award in IISE Transactions for the paper “Multichannel Profile Monitoring based on Sparse Multichannel Functional Principal Component Analysis” 2019.

• Best Paper Award in IISE Transactions for the paper “State space modeling of autocorrelated multivariate Poisson counts. IISE Transactions,” 2018.

• Winner of 2017 INFORMS Data Mining Best Paper Competition, for the paper “Dynamic Multivariate Functional data Modeling via Sparse Subspace Learning”.

• Winner of 2017 INFORMS QSR Best Student Poster Competition, for the paper “Multichannel Profile Monitoring based on Sparse Multichannel Functional Principal Component Analysis”.