Name: Qiu Yixuan
Title: Professor
Research Direction: Statistical Computing, Deep Learning
Courses Taught: Deep Learning, Distributed Computing, Bayesian Statistics, Computational Statistics
E - mail：qiuyixuan@sufe.edu.cn
Phone: 65901035
Research Field
Statistical computation, large-scale data analysis, deep learning models
Education Background
2012-2018 Purdue University Department of Statistics, PhD
2010-2012 Renmin University of China, School of Statistics, Master's Degree
2006-2010 Renmin University of China, School of Statistics, Bachelor's Degree
Work Experience
2026.7 to pressent,Shanghai University of Finance and Economics, School of Statistics and Management, Professor
2020 to 2026.6 Shanghai University of Finance and Economics, School of Statistics and Management, Associate Professor
2018-2020 Carnegie Mellon University, Department of Statistics and Data Science, Postdoctoral Researcher
Wang, C. and Qiu, Y. (2025). The Sparse-Plus-Low-Rank Quasi-Newton Method for Entropic-Regularized Optimal Transport. International Conference on Machine Learning (ICML 2025).
Qiu, Y.*, Gao, Q.*, and Wang, X. (2025). Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks. *Joint first author Journal of the American Statistical Association.
Yin, H., Qiu, Y., and Wang, X. (2025). Wasserstein Coreset via Sinkhorn Loss. Transactions on Machine Learning Research.
Tang, Z. and Qiu, Y. (2024). Safe and Sparse Newton Method for Entropic-Regularized Optimal Transport. Advances in Neural Information Processing Systems (NeurIPS 2024).
Zheng, A. Y., He, T., Qiu, Y., Wang, M., and Wipf, D. (2024). Graph Machine Learning through the Lens of Bilevel Optimization. Artificial Intelligence and Statistics (AISTATS 2024).
Lin, K. Z., Qiu, Y., and Roeder, K. (2024). eSVD-DE: Cohort-wide Differential Expression in Single-cell RNA-seq Data Using Exponential-family Embeddings. BMC Bioinformatics.
Qiu, Y. and Wang, X. (2024). Efficient Multimodal Sampling via Tempered Distribution Flow. Journal of the American Statistical Association.
Guo, X., Qiu, Y., Zhang, H., and Chang, X. (2023). Randomized Spectral Co-Clustering for Large-Scale Directed Networks. Journal of Machine Learning Research.
Dai, B.* and Qiu, Y*. (2023). ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence. *Joint first authors. Advances in Neural Information Processing Systems (NeurIPS 2023).
Qiu, Y., Lei, J., and Roeder, K. (2023). Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning. Biometrika.
Zheng, Y., He, T., Qiu, Y., and Wipf, D. (2022). Learning Manifold Dimensions with Conditional Variational Autoencoders. Advances in Neural Information Processing Systems (NeurIPS 2022).
Qiu, Y., Wang, J., Lei, J., and Roeder, K. (2021). Identification of Cell-type-specific Marker Genes from Co-expression Patterns in Tissue Samples. Bioinformatics.
Qiu, Y. and Wang, X. (2021). ALMOND: Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion. Journal of the American Statistical Association.
Qiu, Y., Zhang, L., and Wang, X. (2020). Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models. International Conference on Learning Representations (ICLR 2020).
Qiu, Y. and Wang, X. (2020). Stochastic Approximate Gradient Descent via the Langevin Algorithm. AAAI Conference on Artificial Intelligence (AAAI 2020).
Lu, J.*, Qiu, Y.*, and Deng, A. (2019). A Note on Type S/M Errors in Hypothesis Testing. *Joint first authors. British Journal of Mathematical and Statistical Psychology.
Qiu, Y., Zhang, L., and Liu, C. (2018). Exact and Efficient Inference for Partial Bayes Problems. Electronic Journal of Statistics.
Qiu, Y. and Wei, W. (2017). A Scalable Sequential Principal Component Analysis Algorithm (SeqPCA) with Application to User Access Control Analysis. IEEE International Conference on Big Data.
Abraham, G., Qiu, Y., and Inouye, M. (2017). FlashPCA2: Principal Component Analysis of Biobank-scale Genotype Datasets. Bioinformatics.
Bilsland Dissertation Fellowship, Purdue University, 2017
Honorable Mention of John Chambers Award, American Statistical Association, 2016
ICSA Midwest Chapter Meeting Poster Award, ICSA Midwest Chapter, 2015
Ross Fellowship, Purdue University, 2012
Academic Services
Journal of Statistical Theory and Practice Guest Editor
Journal of Data Science Associate Editor
Vice President of the Artificial Intelligence Branch of the Chinese Society of Business Statistics
Executive Director of the Machine Learning Branch of the Chinese Society of Statistical Research
Executive Director of the National Industrial Statistics Teaching Research Association Youth Statisticians Association
