上海财经大学 > 教师主页 > 教师

姓  名:邱怡轩
职  称:副教授
研究方向:统计计算,深度学习
教授课程:深度学习,分布式计算,贝叶斯统计,计算统计

E - mailqiuyixuan@sufe.edu.cn

电话:65901035


研究项目


研究领域

统计计算,大规模数据分析,深度学习模型

教育经历

2012-2018 普渡大学统计系,博士

2010-2012 中国人民大学统计学院,硕士

2006-2010 中国人民大学统计学院,学士

工作经历

2020至今 上海财经大学统计与管理学院,副教授

2018-2020 卡内基梅隆大学统计与数据科学系,博士后研究员

研究成果

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. and Wang, X. (2023+). Efficient Multimodal Sampling via Tempered Distribution Flow. Journal of the American Statistical Association, accepted.

 

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.


 


奖励,荣誉

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