| 2025 | AIED | Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems. | Steven Coyne, Diana Galvn-Sosa, Ryan Spring, Camlia Guerraoui, Michael Zock, Keisuke Sakaguchi, Kentaro Inui |
| 2020 | EMNLP | Training Question Answering Models From Synthetic Data. | Raul Puri, Ryan Spring, Mohammad Shoeybi, Mostofa Patwary, Bryan Catanzaro |
| 2020 | IJCAI | Mutual Information Estimation using LSH Sampling. | Ryan Spring, Anshumali Shrivastava |
| 2019 | ICML | Compressing Gradient Optimizers via Count-Sketches. | Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava |
| 2018 | ICLR | Scalable Estimation via LSH Samplers (LSS). | Ryan Spring, Anshumali Shrivastava |
| 2018 | ICML | MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. | Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk |
| 2018 | PACLIC | The Effect of L2 Onset on L2 and L3 learning: The Case of Native Speakers of Burkinabe languages. | Alain Hien, Ryan Spring |
| 2017 | KDD | Scalable and Sustainable Deep Learning via Randomized Hashing. | Ryan Spring, Anshumali Shrivastava |
| 2010 | PACLIC | A Look into the Acquisition of English Motion Event Conflation by Native Speakers of Chinese and Japanese. | Ryan Spring |