| Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning R Dandekar, G Barbastathis medRxiv, 2020 | 180* | 2020 |
| Universal rim thickness in unsteady sheet fragmentation Y Wang, R Dandekar, N Bustos, S Poulain, L Bourouiba Physical review letters 120 (20), 204503, 2018 | 93 | 2018 |
| Bayesian neural ordinary differential equations R Dandekar, K Chung, V Dixit, M Tarek, A Garcia-Valadez, KV Vemula, ... arXiv preprint arXiv:2012.07244, 2020 | 76 | 2020 |
| A machine learning aided global diagnostic and comparative tool to assess effect of quarantine control in Covid-19 spread R Dandekar, C Rackauckas, G Barbastathis Patterns, 100145, 2020 | 69 | 2020 |
| Neural Network aided quarantine control model estimation of COVID spread in Wuhan, China R Dandekar, G Barbastathis arXiv preprint arXiv:2003.09403, 2020 | 31 | 2020 |
| Film spreading from a miscible drop on a deep liquid layer R Dandekar, A Pant, BA Puthenveettil Journal of Fluid Mechanics 829, 304-327, 2017 | 16 | 2017 |
| Neural Network aided quarantine control model estimation of COVID spread in Wuhan R Dandekar, G Barbastathis China. arXiv 2003, 2020 | 14 | 2020 |
| Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning. medRxiv R Dandekar, G Barbastathis Preprint posted online April 6 (2020), 10.1101, 2020 | 12 | 2020 |
| Safe blues: A method for estimation and control in the fight against COVID-19 R Dandekar, SG Henderson, M Jansen, S Moka, Y Nazarathy, ... medRxiv, 2020.05. 04.20090258, 2020 | 10 | 2020 |
| Quantifying the Effect of Quarantine Control in COVID-19 Infectious Spread Using Machine Learning. 2020 R Dandekar, G Barbastathis Preprint at https://doi. org/10.1101/2020.04 3, 0 | 10 | |
| Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning E Nieves, R Dandekar, C Rackauckas Frontiers in Systems Biology 4, 1338518, 2024 | 7 | 2024 |
| Safe Blues: The case for virtual safe virus spread in the long-term fight against epidemics R Dandekar, SG Henderson, HM Jansen, J McDonald, S Moka, ... Patterns 2 (3), 2021 | 7 | 2021 |
| Bayesian neural ordinary differential equations (2020) R Dandekar, K Chung, V Dixit, M Tarek, A Garcia-Valadez, KV Vemula, ... arXiv preprint arXiv:2012.07244, 2012 | 7 | 2012 |
| Neural Network aided quarantine control model estimation of global Covid-19 spread. arXiv 2020 R Dandekar, G Barbastathis arXiv preprint arXiv:2004.02752, 0 | 7 | |
| Neural Network aided quarantine control model estimation of global Covid-19 spread (2020) R Dandekar, G Barbastathis arXiv preprint arXiv:2004.02752, 2020 | 5 | 2020 |
| Implications of delayed reopening in controlling the COVID-19 surge in Southern and West-Central USA R Dandekar, E Wang, G Barbastathis, C Rackauckas Health Data Science 2021, 9798302, 2021 | 4 | 2021 |
| Evaluating cultural awareness of llms for yoruba, malayalam, and english F Dawson, Z Mosunmola, S Pocker, RA Dandekar, R Dandekar, S Panat arXiv preprint arXiv:2410.01811, 2024 | 3 | 2024 |
| Model-form epistemic uncertainty quantification for modeling with differential equations: Application to epidemiology E Acquesta, T Portone, R Dandekar, C Rackauckas, R Bandy, G Huerta Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | 3 | 2022 |
| HULLMI: Human vs LLM identification with explainability PD Joshi, S Pocker, RA Dandekar, R Dandekar, S Panat arXiv preprint arXiv:2409.04808, 2024 | 1 | 2024 |
| NanoVLMs: How small can we go and still make coherent Vision Language Models? M Agarwalla, H Kumar, R Dandekar, R Dandekar, S Panat arXiv preprint arXiv:2502.07838, 2025 | | 2025 |
Christopher Vincent RackauckasMassachusetts Institute of TechnologyVerificeret mail på mit.edu