T-SNE-CUDA: GPU-Accelerated T-SNE and its Applications to Modern Data. - David M. Chan; Roshan Rao; Forrest Huang; John F. Canny (SBAC-PAD, 2018)

Evaluating Protein Transfer Learning with TAPE. - Roshan Rao; Nicholas Bhattacharya; Neil Thomas; Yan Duan; Xi Chen 022; John F. Canny; Pieter Abbeel; Yun S. Song (NeurIPS, 2019)

ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations. - Daniel Seita; David M. Chan; Roshan Rao; Chen Tang; Mandi Zhao; John F. Canny (CoRR, 2019)

GPU accelerated t-distributed stochastic neighbor embedding. - David M. Chan; Roshan Rao; Forrest Huang; John F. Canny (J. Parallel Distributed Comput., 2019)

Quality and Relevance Metrics for Selection of Multimodal Pretraining Data. - Roshan Rao; Sudha Rao; Elnaz Nouri; Debadeepta Dey; Asli Celikyilmaz; Bill Dolan (CVPR Workshops, 2020)

Transformer protein language models are unsupervised structure learners. - Roshan Rao; Joshua Meier; Tom Sercu; Sergey Ovchinnikov; Alexander Rives (ICLR, 2021)

MSA Transformer. - Roshan Rao; Jason Liu; Robert Verkuil; Joshua Meier; John F. Canny; Pieter Abbeel; Tom Sercu; Alexander Rives (ICML, 2021)

Language models enable zero-shot prediction of the effects of mutations on protein function. - Joshua Meier; Roshan Rao; Robert Verkuil; Jason Liu; Tom Sercu; Alexander Rives (NeurIPS, 2021)

Training, Evaluating, and Understanding Evolutionary Models for Protein Sequences - Roshan Rao (2021)

End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman. - Samantha Petti; Nicholas Bhattacharya; Roshan Rao; Justas Dauparas; Neil Thomas; Juannan Zhou; Alexander M. Rush; Peter K. Koo; Sergey Ovchinnikov 001 (Bioinform., 2023)