IFoDS 2024 Program

All times are Beijing Time.

Sunday, July 21 2024

• 9:00-9:30: Opening Ceremony
• 9:30-11:30: Keynote
  • 9:30-10:30: Digital Twin of Economic Systems (Speaker: Songxi Chen, Chair: Jun Yan)
  • 10:30-11:30: First Principles of Advanced Data Analysis: the Prediction Principle (Speaker: Chuanhai Liu, Chair: Xiaoling Lu)
• 13:00-15:00:
  • Parallel Session A: Complex Data Analysis (Chair: Zhibo Cai)
    • Quantifying Individual Risk for Binary Outcome: Bounds and Inference (Speaker: Yue Liu)
    • MedReader: a query-based multisource AI learner of medical publications (Speaker: Wenxuan Zhong)
    • Statistics in Hospital Research and Quality Improvement Projects (Speaker: Liping Tong)
    • On detecting the effect of exposure mixture (Speaker: Zhezhen Jin)
    • Fitting an Accelerated Failure Time Model with Time-dependent Covariates via Nonparametric Mixture (Speaker:Ju-Young Park)
  • Parallel Session B: Mordern Statitistical Methods on Time Series and Funictional Data (Chair: Hui Huang)
    • A Stock Price Trend Prediction Model Based on Supply Chain Matrix (Speaker: Wu Wang)
    • Testing conditional quantile independence with functional covariate (Speaker: Jie Li)
    • Unified Principal Components Analysis of Irregularly Observed Functional Time Series (Speaker: Zerui Guo)
    • Forecasting Interval for Autoregressive Time Series with trend (Speaker: Qin Shao)
    • Inference for Quantile Change Points in High-Dimensional Time Series (Speaker: Mengyu Xu)
• 15:30-17:30:
  • Parallel Session A: Efficient Analysis in Statistics and Related Fields (Chair: Chunyan Wang)
    • Subsampling Spectral Clustering for Stochastic Block Models in Large-Scale Networks (Speaker: Danyang Huang)
    • Interval-censored linear quantile regression (Speaker: Sangbum Choi)
    • Recent developments for multi-channel factor analysis (Speaker: Haonan Wang)
    • Statistical Models for Categorical Data Analysis (Speaker: Jie Yang)
    • Statistical Computing Meets Quantum Computing (Speaker: Ping Ma)
  • Parallel Session B: Machine Learning and Data Science (Chair: Jie Li)
    • Accelerating Convergence in Bayesian Few-Shot Classification (Speaker: Feng Zhou)
    • A Variable Selection Tree and Its Random Forest (Speaker: Zhibo Cai)
    • U.S.-U.K. PETs Prize Challenge: Anomaly Detection via Privacy-Enhanced Federated Learning (Speaker: Xinyue Wang)
    • Partition-Insensitive Parallel ADMM Algorithm for High-dimensional Linear Models (Speaker: Jiancheng Jiang)
    • Deep Neural Network-based Accelerated Failure Time Models Using Rank Loss (Speaker: Sangwook Kang)