| Point-of-care colorimetric analysis through smartphone video B Coleman, C Coarsey, MA Kabir, W Asghar Sensors and Actuators B: Chemical 282, 225-231, 2019 | 53 | 2019 |
| Sub-linear race sketches for approximate kernel density estimation on streaming data B Coleman, A Shrivastava Proceedings of The Web Conference 2020, 1739-1749, 2020 | 50 | 2020 |
| Development of a flow-free magnetic actuation platform for an automated microfluidic ELISA C Coarsey, B Coleman, MA Kabir, M Sher, W Asghar RSC advances 9 (15), 8159-8168, 2019 | 49 | 2019 |
| Cell phone based colorimetric analysis for point-of-care settings B Coleman, C Coarsey, W Asghar Analyst 144 (6), 1935-1947, 2019 | 46 | 2019 |
| To petabytes and beyond: recent advances in probabilistic and signal processing algorithms and their application to metagenomics RAL Elworth, Q Wang, PK Kota, CJ Barberan, B Coleman, A Balaji, ... Nucleic acids research 48 (10), 5217-5234, 2020 | 39 | 2020 |
| Sub-linear memory sketches for near neighbor search on streaming data B Coleman, R Baraniuk, A Shrivastava International Conference on Machine Learning, 2089-2099, 2020 | 30* | 2020 |
| Fast processing and querying of 170tb of genomics data via a repeated and merged bloom filter (rambo) G Gupta, M Yan, B Coleman, B Kille, RAL Elworth, T Medini, T Treangen, ... Proceedings of the 2021 International Conference on Management of Data, 2226 …, 2021 | 28* | 2021 |
| A one-pass distributed and private sketch for kernel sums with applications to machine learning at scale B Coleman, A Shrivastava Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications …, 2021 | 17* | 2021 |
| One-pass diversified sampling with application to terabyte-scale genomic sequence streams B Coleman, B Geordie, L Chou, RAL Elworth, T Treangen, A Shrivastava International Conference on Machine Learning, 4202-4218, 2022 | 15* | 2022 |
| Practical near neighbor search via group testing J Engels, B Coleman, A Shrivastava Advances in Neural Information Processing Systems 34, 9950-9962, 2021 | 15 | 2021 |
| H Chi, James Caverlee, Julian McAuley, and Derek Zhiyuan Cheng. How to train data-efficient llms N Sachdeva, B Coleman, WC Kang, J Ni, L Hong arXiv preprint arXiv:2402.09668, 2024 | 14 | 2024 |
| Graph reordering for cache-efficient near neighbor search B Coleman, S Segarra, AJ Smola, A Shrivastava Advances in Neural Information Processing Systems 35, 38488-38500, 2022 | 14 | 2022 |
| Development of a point-of-care assay for HIV-1 viral load using higher refractive index antibody-coated microbeads M Sher, B Coleman, M Caputi, W Asghar Sensors 21 (5), 1819, 2021 | 11 | 2021 |
| Unified Embedding: Battle-tested feature representations for web-scale ML systems B Coleman, WC Kang, M Fahrbach, R Wang, L Hong, E Chi, D Cheng Advances in Neural Information Processing Systems 36, 2024 | 7 | 2024 |
| Modeling and analysis of power line communications for application in smart grid M Peck, G Alvarez, B Coleman, H Moradi, M Forest, V Aalo arXiv preprint arXiv:1709.06883, 2017 | 6 | 2017 |
| H Chi, James Caverlee, Julian McAuley, and Derek Zhiyuan Cheng. 2024. How to Train Data-Efficient LLMs N Sachdeva, B Coleman, WC Kang, J Ni, L Hong arXiv preprint arXiv:2402.09668, 2024 | 5 | 2024 |
| Revisiting consistent hashing with bounded loads J Chen, B Coleman, A Shrivastava Proceedings of the AAAI Conference on Artificial Intelligence 35 (5), 3976-3983, 2021 | 5 | 2021 |
| Bloom origami assays: Practical group testing L Abraham, G Becigneul, B Coleman, B Scholkopf, A Shrivastava, ... arXiv preprint arXiv:2008.02641, 2020 | 5 | 2020 |
| How to Train Data-Efficient LLMs N Sachdeva, B Coleman, WC Kang, J Ni, L Hong, EH Chi, J Caverlee, ... arXiv preprint arXiv:2402.09668, 2024 | 4 | 2024 |
| One-pass distribution sketch for measuring data heterogeneity in federated learning Z Liu, Z Xu, B Coleman, A Shrivastava Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
Anshumali ShrivastavaRice University, ThirdAI Corp.Verified email at rice.edu
