| Properties and numerical evaluation of the Rosenblatt distribution MS Veillette, MS Taqqu | 99 | 2013 |
| Sevir: A storm event imagery dataset for deep learning applications in radar and satellite meteorology M Veillette, S Samsi, C Mattioli Advances in Neural Information Processing Systems 33, 22009-22019, 2020 | 74 | 2020 |
| STBL: Alpha stable distributions for MATLAB M Veillette Matlab Central File Exchange, retreived October 10, 2012, 2012 | 61* | 2012 |
| Numerical computation of first-passage times of increasing Lévy processes M Veillette, MS Taqqu Methodology and Computing in Applied Probability 12 (4), 695-729, 2010 | 41 | 2010 |
| Using differential equations to obtain joint moments of first-passage times of increasing Lévy processes M Veillette, MS Taqqu Statistics & probability letters 80 (7-8), 697-705, 2010 | 39 | 2010 |
| Creating synthetic radar imagery using convolutional neural networks MS Veillette, EP Hassey, CJ Mattioli, H Iskenderian, PM Lamey Journal of Atmospheric and Oceanic Technology 35 (12), 2323-2338, 2018 | 29 | 2018 |
| Heterogeneous convective weather forecast translation into airspace permeability with prediction intervals MP Matthews, MS Veillette, JC Venuti, RA DeLaura, JK Kuchar Journal of Air Transportation 24 (2), 41-54, 2016 | 26 | 2016 |
| Distributed deep learning for precipitation nowcasting S Samsi, CJ Mattioli, MS Veillette 2019 IEEE High Performance Extreme Computing Conference (HPEC), 1-7, 2019 | 25 | 2019 |
| Graphical display of radar and radar-like meteorological data MS Veillette, MM Wolfson, H Iskenderian, C Mattioli, ER Williams US Patent App. 14/290,308, 2014 | 24 | 2014 |
| A technique for computing the PDFs and CDFs of nonnegative infinitely divisible random variables MS Veillette, MS Taqqu Journal of applied probability 48 (1), 217-237, 2011 | 19 | 2011 |
| Pce-pinns: Physics-informed neural networks for uncertainty propagation in ocean modeling B Lütjens, CH Crawford, M Veillette, D Newman arXiv preprint arXiv:2105.02939, 2021 | 12 | 2021 |
| Polarimetric observations of chaff using the WSR-88D network JM Kurdzo, ER Williams, DJ Smalley, BJ Bennett, DC Patterson, ... Journal of Applied Meteorology and Climatology 57 (5), 1063-1081, 2018 | 11 | 2018 |
| WSR-88D chaff detection and characterization using an optimized hydrometeor classification algorithm JM Kurdzo, BJ Bennett, MS Veillette, DJ Smalley, ER Williams, ... AMS Conference on Aviation, Range, and Aerospace Meteorology, 2017 | 8 | 2017 |
| Airspace Flow Rate Forecast Algorithms, Validation, and Implementation M Matthews, R DeLaura, M Veillette, J Venuti, J Kuchar Project report atc-428, MIT Lincoln Laboratory, Lexington, MA, 2015 | 8 | 2015 |
| Adapting deep learning models to new meteorological contexts using transfer learning P Khorrami, O Simek, B Cheung, M Veillette, R Dangovski, I Rugina, ... 2021 IEEE International Conference on Big Data (Big Data), 4169-4177, 2021 | 6 | 2021 |
| Analysis of factors affecting air travel demand during the COVID-19 pandemic R DeLaura, M Veillette, T Reynolds AIAA AVIATION 2021 FORUM, 2342, 2021 | 6 | 2021 |
| Translating Convective Weather Forecasts into Strategic Traffic Management Decision Aids M Matthews, M Veillette, J Venuti, R DeLaura, J Kuchar 12th USA/Europe Air Traffic Management Research and Development Seminar, 2017 | 6 | 2017 |
| The offshore precipitation capability M Veillette, H Iskenderian, M Wolfson, C Mattioli, E Hassey, P Lamey Project Report ATC-430, MIT Lincoln Laboratory, Lexington, MA, 2016 | 6 | 2016 |
| Convective initiation forecasts through the use of machine learning methods MS Veillette, H Iskenderian, PM Lamey, LJ Bickmeier 93rd American Meteorological Society Annual Meeting: 16th Conference on …, 2013 | 6 | 2013 |
| A deep learning–based velocity dealiasing algorithm derived from the WSR-88D open radar product generator MS Veillette, JM Kurdzo, PM Stepanian, J McDonald, S Samsi, JYN Cho Artificial Intelligence for the Earth Systems 2 (3), e220084, 2023 | 4 | 2023 |
