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., Zhang, L., and Wang, X., Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models. 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. 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 with extensions to online learning
    The 11th ICSA International Conference (incoming), 2019.
  • 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

  • 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.

Applied Predictive Modeling ggplot2 The Art of R Programming R Graphics Cookbook