John S. Schreck

According to our database, John S. Schreck authored at least 12 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Bayesian Deep Learning for Convective Initiation Nowcasting Uncertainty Estimation.

Da Fan, David John Gagne II, Steven J. Greybush, Eugene E. Clothiaux, John S. Schreck, Chaopeng Shen

CoRR, July, 2025

Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control.

Junjie Yu, John S. Schreck, David John Gagne, Keith W. Oleson, Jie Li, Yongtu Liang, Qi Liao, Mingfei Sun, David O. Topping, Zhonghua Zheng

CoRR, May, 2025

Investigating the contribution of terrain-following coordinates and conservation schemes in AI-driven precipitation forecasts.

Yingkai Sha, John S. Schreck, William E. Chapman, David John Gagne II

CoRR, March, 2025

Uncertainty Quantification of Wind Gust Predictions in the Northeast US: An Evidential Neural Network and Explainable Artificial Intelligence Approach.

Israt Jahan, John S. Schreck, David John Gagne, Charlie Becker, Marina Astitha

CoRR, February, 2025

Uncertainty quantification of wind gust predictions in the northeast United States: An evidential neural network and explainable artificial intelligence approach.

Israt Jahan, John S. Schreck, David John Gagne, Charlie Becker, Marina Astitha

Environ. Model. Softw., 2025

2024

Community Research Earth Digital Intelligence Twin (CREDIT).

John S. Schreck, Yingkai Sha, William E. Chapman, Dhamma Kimpara, Judith Berner, Seth McGinnis, Arnold Kazadi, Negin Sobhani, Ben Kirk, David John Gagne II

CoRR, 2024

2023

Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management.

John S. Schreck, William Petzke, Pedro A. Jimenez, Thomas Brummet, Jason C. Knievel, Eric James, Branko Kosovic, David John Gagne

Remote. Sens., July, 2023

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications.

John S. Schreck, David John Gagne II, Charlie Becker, William E. Chapman, Kim Elmore, Gabrielle Gantos, Eliot Kim, Dhamma Kimpara, Thomas Martin, Maria J. Molina, Vanessa M. Pryzbylo, Jacob Radford, Belen Saavedra, Justin Willson, Christopher D. Wirz

CoRR, 2023

Mimicking non-ideal instrument behavior for hologram processing using neural style translation.

John S. Schreck, Matthew Hayman, Gabrielle Gantos, Aaron Bansemer, David John Gagne

CoRR, 2023

2022

Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection.

Andrea Di Gioacchino, Jonah Procyk, Marco Molari, John S. Schreck, Yu Zhou, Yan Liu, Rémi Monasson, Simona Cocco, Petr Sulc

PLoS Comput. Biol., September, 2022

Neural network processing of holographic images.

John S. Schreck, Gabrielle Gantos, Matthew Hayman, Aaron Bansemer, David John Gagne

CoRR, 2022

2019

Learning retrosynthetic planning through self-play.

John S. Schreck, Connor W. Coley, Kyle J. M. Bishop

CoRR, 2019