Follow
Ron Sarafian
Title
Cited by
Cited by
Year
Gaussian markov random fields versus linear mixed models for satellite-based PM2. 5 assessment: evidence from the northeastern USA
R Sarafian, I Kloog, AC Just, JD Rosenblatt
Atmospheric environment 205, 30-35, 2019
192019
Gaussian Markov random fields improve ensemble predictions of daily 1 km PM2. 5 and PM10 across France
I Hough, R Sarafian, A Shtein, B Zhou, J Lepeule, I Kloog
Atmospheric Environment 264, 118693, 2021
152021
A domain adaptation approach for performance estimation of spatial predictions
R Sarafian, I Kloog, E Sarafian, I Hough, JD Rosenblatt
IEEE Transactions on Geoscience and Remote Sensing 59 (6), 5197-5205, 2020
72020
Six types of dust events in Eastern Mediterranean identified using unsupervised machine-learning classification
D Nissenbaum, R Sarafian, Y Rudich, S Raveh-Rubin
Atmospheric Environment 309, 119902, 2023
32023
Deep multi-task learning for early warnings of dust events implemented for the Middle East
R Sarafian, D Nissenbaum, S Raveh-Rubin, V Agrawal, Y Rudich
NPJ climate and atmospheric science 6 (1), 23, 2023
22023
Optimal-design domain-adaptation for exposure prediction in two-stage epidemiological studies
R Sarafian, I Kloog, JD Rosenblatt
Journal of Exposure Science & Environmental Epidemiology 33 (6), 963-970, 2023
12023
Domain Adaptation Approaches for Exposure Models
R Sarafian
https://github.com/ronsarafian/DA_for_EXposure_Models/blob/main …, 2020
2020
Gaussian Markov Random Fields for Big-scale Spatio-Temporal Data
R Sarafian
https://github.com/ronsarafian/GMRF_Big_Data/blob/main/GMRF_Bid_Data.pdf, 2019
2019
The system can't perform the operation now. Try again later.
Articles 1–8