| Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study with the PathML toolkit for computational pathology J Rosenthal, R Carelli, M Omar, D Brundage, E Halbert, J Nyman, SN Hari, ... Molecular Cancer Research 20 (2), 202-206, 2022 | 27 | 2022 |
| Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states J Nyman, T Denize, Z Bakouny, C Labaki, BM Titchen, K Bi, SN Hari, ... Cell Reports Medicine 4 (9), 2023 | 2 | 2023 |
| Tryage: Real-time, intelligent routing of user prompts to large language model SN Hari, M Thomson arXiv preprint arXiv:2308.11601, 2023 | 2 | 2023 |
| Engineering flexible machine learning systems by traversing functionally invariant paths in weight space G Raghavan, M Thomson arXiv preprint arXiv:2205.00334, 2022 | 2 | 2022 |
| Examining Batch Effect in Histopathology as a Distributionally Robust Optimization Problem SN Hari, J Nyman, N Mehta, H Elmarakeby, B Jiang, F Dietlein, ... bioRxiv, 2021.09. 14.460365, 2021 | 1 | 2021 |
| Herd: Using multiple, smaller LLMs to match the performances of proprietary, large LLMs via an intelligent composer SN Hari, M Thomson arXiv preprint arXiv:2310.19902, 2023 | | 2023 |
| Using Distributionally Robust Optimization to improve robustness in cancer pathology SN Hari, E Van Allen, J Nyman, N Mehta, B Jiang, H Elmarakeby, ... NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and …, 2021 | | 2021 |
| Ensemble-Learning for Sustainable NLP E Berman, SN Hari | | |
| Liver Lesion Localization using Deep Convolutional Neural Networks S Aluko, SN Hari, VP Yakubenko | | |
Eliezer Van AllenDana-Farber Cancer InstituteVerified email at dfci.harvard.edu
