| Three-dimensional printing with sacrificial materials for soft matter manufacturing CS O’Bryan, T Bhattacharjee, SR Niemi, S Balachandar, N Baldwin, ... MRS bulletin 42 (8), 571-577, 2017 | 165 | 2017 |
| Atom3d: Tasks on molecules in three dimensions RJL Townshend, M Vögele, P Suriana, A Derry, A Powers, Y Laloudakis, ... arXiv preprint arXiv:2012.04035, 2020 | 130 | 2020 |
| Atomic-scale strain manipulation of a charge density wave S Gao, F Flicker, R Sankar, H Zhao, Z Ren, B Rachmilowitz, ... Proceedings of the National Academy of Sciences 115 (27), 6986-6990, 2018 | 73 | 2018 |
| Topics, Authors, and Institutions in Large Language Model Research: Trends from 17K arXiv Papers R Movva, S Balachandar, K Peng, G Agostini, N Garg, E Pierson Proceedings of the 2024 Conference of the North American Chapter of the …, 2024 | 6 | 2024 |
| Domain constraints improve risk prediction when outcome data is missing S Balachandar, N Garg, E Pierson arXiv preprint arXiv:2312.03878, 2023 | 5 | 2023 |
| Large language models shape and are shaped by society: A survey of arXiv publication patterns R Movva, S Balachandar, K Peng, G Agostini, N Garg, E Pierson arXiv preprint arXiv:2307.10700, 2023 | 5 | 2023 |
| Breaking the symmetry: Resolving symmetry ambiguities in equivariant neural networks S Balachandar, A Poulenard, C Deng, L Guibas arXiv preprint arXiv:2210.16646, 2022 | 1 | 2022 |
| Using GNNs to Model Biased Crowdsourced Data for Urban Applications S Balachandar, S Sadhuka, B Berger, E Pierson, N Garg | | |
| Learning Graph Neural Networks from Biased Outcome Data S Balachandar, S Sadhuka, B Berger, E Pierson, N Garg ICML 2024 Workshop on Structured Probabilistic Inference {\&} Generative …, 0 | | |