Research

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

Work in Progress

• Qiu, Y., Lei, J., and Roeder, K., Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning. Under review.
• Qiu, Y. and Zhang, L., Exact Tests for the Multivariate Behrens–Fisher Problem. To be submitted.

Publication

• Qiu, Y. and Wang, X., ALMOND: Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion. Accepted by Journal of the American Statistical Association, 2019+.
• Qiu, Y., Zhang, L., and Wang, X., Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models. Accepted by International Conference on Learning Representations (ICLR 2020), 2019+.
• Qiu, Y. and Wang, X., Stochastic Approximate Gradient Descent via the Langevin Algorithm. Accepted by AAAI Conference on Artificial Intelligence (AAAI 2020), 2019+.
• 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.
• Qiu, Y., Zhang, L., and Liu, C., Exact and Efficient Inference for Partial Bayes Problems. Electronic Journal of Statistics, 2018.
• 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.
• Abraham, G., Qiu, Y., and Inouye, M., FlashPCA2: Principal Component Analysis of Biobank-scale Genotype Datasets. Bioinformatics, 2017.
• Qiu, Y., Wang, X. et al., Web Usage Cluster Analysis Based on Prediction Strength. International Conference on Instrumentation, Measurement, Circuits and Systems, 2011.

Invited Conference Talks

• 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.

Other Talks and Posters

• Gradient-based Spase PCA with Extensions to Online Learning
  Joint Statistical Meetings, 2019.
• Stochastic Approximate Gradient Descent via the Underdamped Langevin Algorithm
  SAMSI Deep Learning Workshop, 2019. Poster
• Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion
  Conference of the Science of Deep Learning, 2019. Poster
• Exact and Efficient Inference for Partial Bayes Problems
  Fifth Bayesian, Fiducial, and Frequentist (BFF5) Conference, 2018. Poster
• Beyond Bayes: What We Can Do with a Partial Prior
  Purdue Statistics Graduate Student Seminar, 2017. Slides
• How To Make Your Code Faster
  Purdue Statistics Graduate Student Seminar, 2016. Slides
• Generalized p-Value for Two-Sample Functional Data Comparison
  Joint Statistical Meetings, 2014.
• Dynamic Document with knitr
  Purdue Statistics Graduate Student Seminar, 2014. Slides

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.