Posts

Mon, 01 May 2023 00:00:00 +0000

About Me

Mon, 01 Jan 0001 00:00:00 +0000

I am Yixuan Qiu, currently an associate professor in School of Statistics and Data Science at Shanghai University of Finance and Economics (SUFE). Before joining SUFE in 2020, I was a postdoctoral researcher in Department of Statistics and Data Sciencec at Carnegie Mellon University, working with Dr. Kathryn Roeder and Dr. Jing Lei. I obtained my PhD degree in statistics from Purdue University in 2018, advised by Dr. Lingsong Zhang and Dr. Xiao Wang. Prior to my PhD study in Purdue, I graduated from Renmin University of China for B.A. and M.A. in statistics and actuarial science, respectively.

My research focuses on statistical machine learning and inference. I have great interest in statistical computing, deep learning, exact inference methods, large-scale data analysis, and data visualization. You can find some of my works in the Research page.

I am also enthusiastic about programming and developing high-performance software packages for statistics and machine learning. The Software page lists some open source software packages that I have written and maintained over the years. Feel free to stop by my GitHub page, and even better, give me a star for projects that interest you.

I have been constantly (although slowly!) writing technical blog posts on statistics and programming. Just leave a comment there if you have any thoughts on some topics.

You can always reach me through email, yixuanq [at] gmail [dot] com.

Projects

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Open source software

Here lists some open source software written or maintained by me, mostly R packages.

Statistical Modeling and Machine Learning

Spectra: C++ library for large scale eigenvalue and SVD problems (Honorable Mention of John Chambers Award 2016)
RSpectra: R package for large scale eigenvalue and SVD problems
recosystem: Recommender system using parallel matrix factorization
ADMM: Solving statistical optimization problems using the ADMM algorithm
fdaplus: A faster and redesigned implementation of the fda package for functional data analysis.

High Performance and Big Data Computing

fastncdf: Fast computation of normal CDF
ADMM-Spark: Implementation of ADMM algorithm on Apache Spark
bigspline: Smoothing spline on Apache Spark

Graphics and Data Visualization

showtext: An R package that makes it easier to use fonts in R graphics.
R2SWF: Creating Flash animations in R graphics.

Documentation

Rcpp-note: An API reference of the Rcpp package, an interface between R and C++ for high performance computing in R.

Research

Mon, 01 Jan 0001 00:00:00 +0000

I have great interest in statistical computing, deep learning, exact inference methods, large-scale data analysis, and data visualization.

Publication

Ouyang, X., Zheng, H., Liang, H., Zhang, J., Qiu, Y., Meng, C., and Li, M., Sparsification Techniques for Large-Scale Optimal Transport Problems. WIREs Computational Statistics, 2026. Link
Wang, C. and Qiu, Y., The Sparse-Plus-Low-Rank Quasi-Newton Method for Entropic-Regularized Optimal Transport. International Conference on Machine Learning (ICML 2025), 2025. Link PDF Package Code
Qiu, Y.*, Gao, Q.*, and Wang, X., Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks. *Joint first authors. Journal of the American Statistical Association, 2025. Link PDF Code
Gao, T., Dai, B., and Qiu, Y., ReLU-ReHU Representations of Piecewise Linear-Quadratic Losses. Journal of Data Science, 2025. Link PDF Code
Yin, H., Qiu, Y., and Wang, X., Wasserstein Coreset via Sinkhorn Loss. Transactions on Machine Learning Research, 2025. Link PDF
Tang, Z. and Qiu, Y., Safe and Sparse Newton Method for Entropic-Regularized Optimal Transport. Advances in Neural Information Processing Systems (NeurIPS 2024), 2024. Link PDF Package Code
Zheng, A. Y., He, T., Qiu, Y., Wang, M., and Wipf, D., Graph Machine Learning through the Lens of Bilevel Optimization. Artificial Intelligence and Statistics (AISTATS 2024), 2024. Link PDF
Lin, K. Z., Qiu, Y., and Roeder, K., eSVD-DE: Cohort-wide Differential Expression in Single-cell RNA-seq Data Using Exponential-family Embeddings. BMC Bioinformatics, 2024. Link PDF Code
Qiu, Y. and Wang, X., Efficient Multimodal Sampling via Tempered Distribution Flow. Journal of the American Statistical Association, 2024. Link PDF Code
Guo, X., Qiu, Y., Zhang, H., and Chang, X., Randomized Spectral Co-Clustering for Large-Scale Directed Networks. Journal of Machine Learning Research, 2023. Link PDF Code
Dai, B.* and Qiu, Y.*, ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence. *Joint first authors. Advances in Neural Information Processing Systems (NeurIPS 2023), 2023. Link PDF Project Page Code
Qiu, Y., Lei, J., and Roeder, K., Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning. Biometrika, 2023. Link PDF Code
Zheng, Y., He, T., Qiu, Y., and Wipf, D., Learning Manifold Dimensions with Conditional Variational Autoencoders. Advances in Neural Information Processing Systems (NeurIPS 2022), 2022. Link PDF
Qiu, Y., Wang, J., Lei, J., and Roeder, K., Identification of Cell-type-specific Marker Genes from Co-expression Patterns in Tissue Samples. Bioinformatics, 2021. Link PDF Code
Qiu, Y. and Wang, X., ALMOND: Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion. Journal of the American Statistical Association, 2021. Link Code
Qiu, Y. and Wang, X., Stochastic Approximate Gradient Descent via the Langevin Algorithm. AAAI Conference on Artificial Intelligence (AAAI 2020), 2020. Link PDF
Qiu, Y., Zhang, L., and Wang, X., Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models. International Conference on Learning Representations (ICLR 2020), 2020. Link PDF Code
Lu, J.*, Qiu, Y.*, and Deng, A., A Note on Type S/M Errors in Hypothesis Testing. *Joint first authors. British Journal of Mathematical and Statistical Psychology, 2019. Link PDF
Qiu, Y., Zhang, L., and Liu, C., Exact and Efficient Inference for Partial Bayes Problems. Electronic Journal of Statistics, 2018. Link PDF
Qiu, Y. and Wei, W., A Scalable Sequential Principal Component Analysis Algorithm (SeqPCA) with Application to User Access Control Analysis. IEEE International Conference on Big Data, 2017. Link
Abraham, G., Qiu, Y., and Inouye, M., FlashPCA2: Principal Component Analysis of Biobank-scale Genotype Datasets. Bioinformatics, 2017. Link PDF Code
Qiu, Y., Wang, X. et al., Web Usage Cluster Analysis Based on Prediction Strength. International Conference on Instrumentation, Measurement, Circuits and Systems, 2011.

Invited Talks

ReHLine: Regularized composite ReLU-ReHU loss minimization with linear computation and linear convergence
The 16th International Conference of the ERCIM WG on Computational and Methodological Statistics, 2023.
Efficient, Stable, and Analytic Differentiation of the Sinkhorn Loss
The 9th RUC International Forum on Statistics, 2023. Slides
Efficient Multi-Modal Sampling via Tempered Distribution Flow
The 12th ICSA International Conference, 2023. Slides
Gradient-based Sparse Principal Component Analysis with Applications to Gene Co-expression Analysis
City University of Hong Kong Biostatistics Seminar (online), 2023. Slides
Efficient Multi-Modal Sampling via Tempered Distribution Flow
Statistical Learning Methods in Modern AI, Tianyuan Mathematical Center in Northwest China (website), 2021.
Efficient Multi-Modal Sampling via Tempered Distribution Flow
University of Missouri (online), 2021.
Prettier R Graphs and Documents with {showtext}+{prettydoc}
Cleveland R User Group (website), 2020. Slides
Gradient-based Sparse Principal Component Analysis
The 11th ICSA International Conference, 2019. Slides
Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion
The 36th Annual Quality and Productivity Research Conference, 2019. Slides
Exact Inference with Partially Specified Bayesian Models
2017 ICSA Applied Statistics Symposium, 2017. Slides
SupR: Multi-threaded R Environment
The 9th China-R Conference, 2016. Slides
Large-Scale SVD and Matrix Completion
The 7th China-R Conference, 2014.

Book Translation

Applied Predictive Modeling by Max Kuhn and Kjell Johnson.
ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham.
The Art of R Programming by Norman Matloff.
R Graphics Cookbook by Winston Chang.

Software

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Teaching

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Current (Fall 2025)

Computational Statistics
Bayesian Statistics

Instructor @ SUFE

Bayesian Statistics (Spring 2023, Spring 2024)
Computational Statistics (Fall 2022, Fall 2023, Fall 2024)
Distributed Computing (Fall 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025)
Deep Learning (Fall 2021, Fall 2022, Fall 2023, Fall 2024, Spring 2025)
Introduction to Artificial Intelligence (Spring 2021)

Teaching Assistant @ Purdue

STAT 598Z: Introduction to Computational Statistics (Spring 2014)
STAT 525: Intermediate Statistical Methodology (Fall 2013)
STAT 301: Elementary Statistical Methods (Fall 2013, 2014)

Yixuan's Homepage

Posts

About Me

Projects

Open source software

Statistical Modeling and Machine Learning

High Performance and Big Data Computing

Graphics and Data Visualization

Documentation

Research

Publication

Invited Talks

Other Talks and Posters

Book Translation

Software

Teaching

Current (Fall 2025)

Instructor @ SUFE

Teaching Assistant @ Purdue