# 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 Behrensâ€“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.