2025
1.
Bhupinderjeet Singh; Mingliang Liu; John T. Abatzoglou; Jennifer C. Adam; Kirti Rajagopalan
In: Journal of Hydrology, vol. 662, pp. 133833, 2025, ISSN: 0022-1694.
Abstract | Links | BibTeX | Tags: AI, Water
@article{singh_incorporating_2025,
title = {Incorporating relative humidity in precipitation phase partitioning reduces model bias for some snow and streamflow metrics across the Northwest US},
author = {Bhupinderjeet Singh and Mingliang Liu and John T. Abatzoglou and Jennifer C. Adam and Kirti Rajagopalan},
url = {https://www.sciencedirect.com/science/article/pii/S0022169425011710},
doi = {10.1016/j.jhydrol.2025.133833},
issn = {0022-1694},
year = {2025},
date = {2025-12-01},
urldate = {2025-12-01},
journal = {Journal of Hydrology},
volume = {662},
pages = {133833},
abstract = {While the importance of bivariate precipitation phase partitioning\textemdashthat incorporates both surface air temperature and relative humidity\textemdashhas been established for accurately estimating rain versus snow, hydrology models often rely on a simpler approach that uses only surface-temperature. We evaluate model bias changes for a suite of snow and streamflow metrics between temperature-based rain-snow partitioning (T-RSP) and temperature-relative-humidity-based rain-snow partitioning (TRH-RSP). We used the VIC-CropSyst coupled crop-hydrology model across the Pacific Northwest US as a case study. We found that transition to the TRH-RSP method resulted in a better match between modeled and observed (a) peak snow water equivalent (SWE) magnitude and timing (∼50% reduction in mean absolute bias), (b) daily SWE in winter months (reduction of relative bias from −30% to −4%), and (c) snow-start dates (mean reduction in bias from 7 days to 0 days) for the majority of the observational snow telemetry stations considered. Depending on the metric, 75\textendash88% of stations showed improvements. Most improvements are in the mid elevation stations. We also find improvements in estimates of basin-level streamflow and the ratio of peak SWE over streamflow. Elevation, temperature exposure, and meteorological bias partly explain the variability in performance improvements across stations. We did see a degradation in bias for snow-off dates. This is likely because meteorological bias and the modeled snowmelt dynamics\textemdashboth of which cannot be resolved by changing the precipitation partitioning\textemdashbecome important in the shoulder months at the end of the cold season. Overall, biases in SWE due to precipitation phase partitioning account for a substantial portion of the overall SWE bias\textemdashat least as much as, if not more than known precipitation biases. Transitioning from T-RSP to TRH-RSP can help us better understand model behavior, improve model accuracies, and better support management decision support for water resources, and prioritize improvements in melt dynamics to improve timing simulations.},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {article}
}
While the importance of bivariate precipitation phase partitioning—that incorporates both surface air temperature and relative humidity—has been established for accurately estimating rain versus snow, hydrology models often rely on a simpler approach that uses only surface-temperature. We evaluate model bias changes for a suite of snow and streamflow metrics between temperature-based rain-snow partitioning (T-RSP) and temperature-relative-humidity-based rain-snow partitioning (TRH-RSP). We used the VIC-CropSyst coupled crop-hydrology model across the Pacific Northwest US as a case study. We found that transition to the TRH-RSP method resulted in a better match between modeled and observed (a) peak snow water equivalent (SWE) magnitude and timing (∼50% reduction in mean absolute bias), (b) daily SWE in winter months (reduction of relative bias from −30% to −4%), and (c) snow-start dates (mean reduction in bias from 7 days to 0 days) for the majority of the observational snow telemetry stations considered. Depending on the metric, 75–88% of stations showed improvements. Most improvements are in the mid elevation stations. We also find improvements in estimates of basin-level streamflow and the ratio of peak SWE over streamflow. Elevation, temperature exposure, and meteorological bias partly explain the variability in performance improvements across stations. We did see a degradation in bias for snow-off dates. This is likely because meteorological bias and the modeled snowmelt dynamics—both of which cannot be resolved by changing the precipitation partitioning—become important in the shoulder months at the end of the cold season. Overall, biases in SWE due to precipitation phase partitioning account for a substantial portion of the overall SWE bias—at least as much as, if not more than known precipitation biases. Transitioning from T-RSP to TRH-RSP can help us better understand model behavior, improve model accuracies, and better support management decision support for water resources, and prioritize improvements in melt dynamics to improve timing simulations.
2.
Basavaraj R. Amogi; Lav R. Khot; Bernardita V. Sallato
Impact of summer heat and mitigation strategies on apple (Cosmic Crisp®) fruit color dynamics quantified using crop physiology sensing system Journal Article
In: Journal of Agriculture and Food Research, vol. 23, pp. 102163, 2025, ISSN: 2666-1543.
Abstract | Links | BibTeX | Tags: AI, Farm Ops
@article{amogi_impact_2025,
title = {Impact of summer heat and mitigation strategies on apple (Cosmic Crisp®) fruit color dynamics quantified using crop physiology sensing system},
author = {Basavaraj R. Amogi and Lav R. Khot and Bernardita V. Sallato},
url = {https://www.sciencedirect.com/science/article/pii/S2666154325005344},
doi = {10.1016/j.jafr.2025.102163},
issn = {2666-1543},
year = {2025},
date = {2025-10-01},
urldate = {2025-10-01},
journal = {Journal of Agriculture and Food Research},
volume = {23},
pages = {102163},
abstract = {Frequent summer heat waves significantly challenge global fruit production, including apples (Malus domestica Borkh.) grown in Washington State, USA. While growers employ heat mitigation strategies like evaporative cooling with overhead sprinklers, foggers, shade/drape netting, and protective sprays, these techniques can inadvertently compromise fruit coloration, a key quality attribute influencing harvest timing, marketability, and consumer acceptance. Thus, this study investigated whether continuous, in-orchard monitoring of fruit color and microclimatic conditions could help optimize mitigation practices without compromising fruit quality. Using a Crop Physiology Sensing System (CPSS), apple (Cosmic Crisp®) fruit color progression and ambient weather conditions were monitored at 5-min intervals throughout the 2022 growing season under fogging, netting, and untreated control treatments. CPSS with integrated RGB imaging data were contrasted with ambient air temperature (Tair) within each treatment using a custom developed algorithm. The algorithm allowed automated and daily quantification of fruit color metrics, including hue angle (h°), color transition from green to red (a∗), and chroma. Results suggest that prolonged daytime Tair exceeding 35 °C could cause significant degradation of red pigmentation (increasing h° and declining a∗). Netting caused overnight heat retention and delayed color recovery, whereas fogging effectively moderated the microclimate, preserving red coloration. Crucially, a nighttime drop in Tair to approximately 12 °C facilitated the reappearance of red coloration. To our knowledge, this is the first study to document both the degradation and subsequent reappearance of apple fruit coloration under field conditions. These findings suggest that continuous apple fruit color and ambient air temperature monitoring could be useful to effectively employ heat mitigation techniques, thereby improving fruit quality and market value at harvest.},
keywords = {AI, Farm Ops},
pubstate = {published},
tppubtype = {article}
}
Frequent summer heat waves significantly challenge global fruit production, including apples (Malus domestica Borkh.) grown in Washington State, USA. While growers employ heat mitigation strategies like evaporative cooling with overhead sprinklers, foggers, shade/drape netting, and protective sprays, these techniques can inadvertently compromise fruit coloration, a key quality attribute influencing harvest timing, marketability, and consumer acceptance. Thus, this study investigated whether continuous, in-orchard monitoring of fruit color and microclimatic conditions could help optimize mitigation practices without compromising fruit quality. Using a Crop Physiology Sensing System (CPSS), apple (Cosmic Crisp®) fruit color progression and ambient weather conditions were monitored at 5-min intervals throughout the 2022 growing season under fogging, netting, and untreated control treatments. CPSS with integrated RGB imaging data were contrasted with ambient air temperature (Tair) within each treatment using a custom developed algorithm. The algorithm allowed automated and daily quantification of fruit color metrics, including hue angle (h°), color transition from green to red (a∗), and chroma. Results suggest that prolonged daytime Tair exceeding 35 °C could cause significant degradation of red pigmentation (increasing h° and declining a∗). Netting caused overnight heat retention and delayed color recovery, whereas fogging effectively moderated the microclimate, preserving red coloration. Crucially, a nighttime drop in Tair to approximately 12 °C facilitated the reappearance of red coloration. To our knowledge, this is the first study to document both the degradation and subsequent reappearance of apple fruit coloration under field conditions. These findings suggest that continuous apple fruit color and ambient air temperature monitoring could be useful to effectively employ heat mitigation techniques, thereby improving fruit quality and market value at harvest.
3.
Supriya Savalkar; Michael Pumphrey; Kimberly Campbell; Fabio Scarpare; Tanvir Ferdousi; Samarth Swarup; Claudio Stöckle; Kirti Rajagopalan
Earlier planting fails to replicate historical production conditions for US spring wheat under future climates Journal Article
In: communications earth & environment, vol. 6, iss. 1, pp. 708, 2025, ISBN: 2662-4435, (ISSN: 2693-5015).
Abstract | Links | BibTeX | Tags:
@article{savalkar_earlier_2024,
title = {Earlier planting fails to replicate historical production conditions for US spring wheat under future climates},
author = {Supriya Savalkar and Michael Pumphrey and Kimberly Campbell and Fabio Scarpare and Tanvir Ferdousi and Samarth Swarup and Claudio St\"{o}ckle and Kirti Rajagopalan},
url = {https://www.nature.com/articles/s43247-025-02716-0},
doi = {10.1038/s43247-025-02716-0},
isbn = {2662-4435},
year = {2025},
date = {2025-08-27},
urldate = {2024-10-01},
journal = {communications earth \& environment},
volume = {6},
issue = {1},
pages = {708},
publisher = {Research Square},
abstract = {Global warming and heat stress can adversely affect crop yields and quality. Earlier planting that shifts the growing season to cooler periods is a widely considered adaptation strategy in climate change literature. We ask: How effective is earlier planting in reducing high-temperature-exposure across growth stages? What are the associated temperature-exposure tradeoffs, and can historical conditions be matched? With US spring wheat as a case study, growth-stage-specific temperature exposure signatures are developed to estimate tradeoffs from earlier planting. While earlier planting does reduce exposure to critical and lethal high temperatures during reproductive stages, it fails to replicate historical production conditions. The Pacific Northwest is an exception, although tail-end growth stages may require management. Historically-equivalent planting windows narrow presenting logistical challenges. Therefore, while many climate-change assessments list earlier planting as an effective adaptation strategy, it may not be as effective when tradeoffs are considered, and consideration of other strategies will be important.},
note = {ISSN: 2693-5015},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Global warming and heat stress can adversely affect crop yields and quality. Earlier planting that shifts the growing season to cooler periods is a widely considered adaptation strategy in climate change literature. We ask: How effective is earlier planting in reducing high-temperature-exposure across growth stages? What are the associated temperature-exposure tradeoffs, and can historical conditions be matched? With US spring wheat as a case study, growth-stage-specific temperature exposure signatures are developed to estimate tradeoffs from earlier planting. While earlier planting does reduce exposure to critical and lethal high temperatures during reproductive stages, it fails to replicate historical production conditions. The Pacific Northwest is an exception, although tail-end growth stages may require management. Historically-equivalent planting windows narrow presenting logistical challenges. Therefore, while many climate-change assessments list earlier planting as an effective adaptation strategy, it may not be as effective when tradeoffs are considered, and consideration of other strategies will be important.
4.
Oishee Bintey Hoque, Nibir Chandra Mandal, Abhijin Adiga, Samarth Swarup, Sayjro Kossi Nouwakpo, Amanda Wilson, Madhav Marathe
Knowledge-Informed Deep Learning for Irrigation Type Mapping from Remote Sensing Proceedings Article
In: International Joint Conferences on Artificial Intelligence 2025.
Abstract | Links | BibTeX | Tags: Deep learning, Irrigation, Mapping, Remote Sensing
@inproceedings{nokey,
title = {Knowledge-Informed Deep Learning for Irrigation Type Mapping from Remote Sensing},
author = {Oishee Bintey Hoque, Nibir Chandra Mandal, Abhijin Adiga, Samarth Swarup, Sayjro Kossi Nouwakpo, Amanda Wilson, Madhav Marathe},
doi = { https://doi.org/10.48550/arXiv.2505.08302},
year = {2025},
date = {2025-08-22},
urldate = {2025-08-22},
organization = {International Joint Conferences on Artificial Intelligence},
abstract = {Accurate mapping of irrigation methods is crucial for sustainable agricultural practices and food systems. However, existing models that rely solely on spectral features from satellite imagery are ineffective due to the complexity of agricultural landscapes and limited training data, making this a challenging problem. We present Knowledge-Informed Irrigation Mapping (KIIM), a novel Swin-Transformer based approach that uses (i) a specialized projection matrix to encode crop to irrigation probability, (ii) a spatial attention map to identify agricultural lands from non-agricultural
lands, (iii) bi-directional cross-attention to focus complementary information from different modalities, and (iv) a weighted ensemble for combining predictions from images and crop information. Our experimentation on five states in the US shows up to 22.9% (IoU) improvement over baseline with a 71.4% (IoU) improvement for hard-to-classify drip irrigation. In addition, we propose a two-phase transfer learning approach to enhance cross-state irrigation mapping, achieving a 51% IoU boost in a state with limited labeled data. The ability to achieve baseline performance with only 40% of the training data highlights its efficiency, reducing the dependency on extensive manual labeling efforts and making large-scale, automated irrigation mapping more feasible and cost-effective. Code: https://github.com/Nibir088/KIIM},
keywords = {Deep learning, Irrigation, Mapping, Remote Sensing},
pubstate = {published},
tppubtype = {inproceedings}
}
Accurate mapping of irrigation methods is crucial for sustainable agricultural practices and food systems. However, existing models that rely solely on spectral features from satellite imagery are ineffective due to the complexity of agricultural landscapes and limited training data, making this a challenging problem. We present Knowledge-Informed Irrigation Mapping (KIIM), a novel Swin-Transformer based approach that uses (i) a specialized projection matrix to encode crop to irrigation probability, (ii) a spatial attention map to identify agricultural lands from non-agricultural
lands, (iii) bi-directional cross-attention to focus complementary information from different modalities, and (iv) a weighted ensemble for combining predictions from images and crop information. Our experimentation on five states in the US shows up to 22.9% (IoU) improvement over baseline with a 71.4% (IoU) improvement for hard-to-classify drip irrigation. In addition, we propose a two-phase transfer learning approach to enhance cross-state irrigation mapping, achieving a 51% IoU boost in a state with limited labeled data. The ability to achieve baseline performance with only 40% of the training data highlights its efficiency, reducing the dependency on extensive manual labeling efforts and making large-scale, automated irrigation mapping more feasible and cost-effective. Code: https://github.com/Nibir088/KIIM
lands, (iii) bi-directional cross-attention to focus complementary information from different modalities, and (iv) a weighted ensemble for combining predictions from images and crop information. Our experimentation on five states in the US shows up to 22.9% (IoU) improvement over baseline with a 71.4% (IoU) improvement for hard-to-classify drip irrigation. In addition, we propose a two-phase transfer learning approach to enhance cross-state irrigation mapping, achieving a 51% IoU boost in a state with limited labeled data. The ability to achieve baseline performance with only 40% of the training data highlights its efficiency, reducing the dependency on extensive manual labeling efforts and making large-scale, automated irrigation mapping more feasible and cost-effective. Code: https://github.com/Nibir088/KIIM
5.
Tiegiao Wang, Abhinav Jain, Liqiang He, Cindy Grimm, Sinisa Todorovic
A Dataset for Semantic and Instance Segmentation of Modern Fruit Orchards Unpublished
2025.
@unpublished{nokey,
title = {A Dataset for Semantic and Instance Segmentation of Modern Fruit Orchards},
author = {Tiegiao Wang, Abhinav Jain, Liqiang He, Cindy Grimm, Sinisa Todorovic},
editor = {Oregon State University},
url = {https://web.engr.oregonstate.edu/~sinisa/research/publications/cvpr25.pdf},
year = {2025},
date = {2025-08-20},
keywords = {},
pubstate = {published},
tppubtype = {unpublished}
}
6.
Oishee Bintey Hoque, Abhijin Adiga, Aniruddha Adiga, Siddharth Chaudhary, Madhav V. Marathe, S. S. Ravi, Kirti Rajagopalan, Amanda Wilson,; Samarth Swarup
IGraSS: Learning to Identify Infrastructure Networks from Satellite Imagery by Iterative Graph-constrained Semantic Segmentation Proceedings Article
In: 2025 IEEE International Geoscience and Remote Sensing Symposium 2025.
Abstract | Links | BibTeX | Tags: Infrastructure Networks, Satellite Imagery, Semantic Segmentation
@inproceedings{nokey,
title = {IGraSS: Learning to Identify Infrastructure Networks from Satellite Imagery by Iterative Graph-constrained Semantic Segmentation},
author = {Oishee Bintey Hoque, Abhijin Adiga, Aniruddha Adiga, Siddharth Chaudhary, Madhav V. Marathe, S. S. Ravi, Kirti Rajagopalan, Amanda Wilson, and Samarth Swarup},
url = {https://arxiv.org/abs/2506.08137},
doi = { https://doi.org/10.48550/arXiv.2506.08137},
year = {2025},
date = {2025-08-08},
urldate = {2025-08-08},
organization = {2025 IEEE International Geoscience and Remote Sensing Symposium},
abstract = {Accurate canal network mapping is essential for water management, including irrigation planning and infrastructure maintenance. State-of-the-art semantic segmentation models for infrastructure mapping, such as roads, rely on large, wellannotated remote sensing datasets. However, incomplete or inadequate ground truth can hinder these learning approaches. Many infrastructure networks have graph-level properties such as reachability to a source (like canals) or connectivity (roads) that can be leveraged to improve these existing ground truth. This paper develops a novel iterative framework IGraSS, combining a semantic segmentation module\textemdashincorporating RGB and additional modalities (NDWI, DEM)\textemdashwith a graph-based ground-truth refinement module. The segmentation module processes satellite imagery patches, while the refinement module operates on the entire data viewing the infrastructure network as a graph. Experiments show that IGraSS reduces unreachable canal segments from 18% to 3%, and training with refined ground truth significantly improves canal identification. IGraSS serves as a robust framework for both refining noisy ground truth and mapping canal networks from remote sensing imagery. We also demonstrate the effectiveness and generalizability of IGraSS using road networks as an example, applying a different graph-theoretic constraint to complete road networks.},
keywords = {Infrastructure Networks, Satellite Imagery, Semantic Segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
Accurate canal network mapping is essential for water management, including irrigation planning and infrastructure maintenance. State-of-the-art semantic segmentation models for infrastructure mapping, such as roads, rely on large, wellannotated remote sensing datasets. However, incomplete or inadequate ground truth can hinder these learning approaches. Many infrastructure networks have graph-level properties such as reachability to a source (like canals) or connectivity (roads) that can be leveraged to improve these existing ground truth. This paper develops a novel iterative framework IGraSS, combining a semantic segmentation module—incorporating RGB and additional modalities (NDWI, DEM)—with a graph-based ground-truth refinement module. The segmentation module processes satellite imagery patches, while the refinement module operates on the entire data viewing the infrastructure network as a graph. Experiments show that IGraSS reduces unreachable canal segments from 18% to 3%, and training with refined ground truth significantly improves canal identification. IGraSS serves as a robust framework for both refining noisy ground truth and mapping canal networks from remote sensing imagery. We also demonstrate the effectiveness and generalizability of IGraSS using road networks as an example, applying a different graph-theoretic constraint to complete road networks.
7.
Nibir Chandra Mandal, Oishee Bintey Hoque, Abhijin Adiga, Samarth Swarup, Mandy Wilson, Lu Feng, Yangfeng Ji, Miaomiao Zhang, Geoffrey Fox, Madhav Marathe
IrrMap: A Large-Scale Comprehensive Dataset for Irrigation Method Mapping Proceedings Article
In: 2025 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2025.
Abstract | Links | BibTeX | Tags: AI, Irrigation, Mapping
@inproceedings{nokey,
title = {IrrMap: A Large-Scale Comprehensive Dataset for Irrigation Method Mapping},
author = {Nibir Chandra Mandal, Oishee Bintey Hoque, Abhijin Adiga, Samarth Swarup, Mandy Wilson, Lu Feng, Yangfeng Ji, Miaomiao Zhang, Geoffrey Fox, Madhav Marathe},
doi = { https://doi.org/10.48550/arXiv.2505.08273},
year = {2025},
date = {2025-08-07},
urldate = {2025-08-07},
organization = {2025 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
abstract = {We introduce IrrMap, the first large-scale dataset (1.1 million patches) for irrigation method mapping across regions. IrrMap consists of multi-resolution satellite imagery from LandSat and Sentinel, along with key auxiliary data such as crop type, land use, and vegetation indices. The dataset spans 1,687,899 farms and 14,117,330 acres across multiple western U.S. states from 2013 to 2023, providing a rich and diverse foundation for irrigation analysis and ensuring geospatial alignment and quality control. The dataset is ML-ready, with standardized 224×224 GeoTIFF patches, the multiple input data layers, carefully chosen train-test-split data, and accompanying dataloaders for seamless deep learning model training and benchmarking in irrigation mapping. The dataset is also accompanied by a complete pipeline for dataset generation, enabling researchers to extend IrrMap to new regions for irrigation data collection or adapt it with minimal effort for other similar applications in agricultural and geospatial analysis. We also analyze the irrigation method distribution across crop groups, spatial irrigation patterns (using Shannon diversity indices), and irrigated area variations for both LandSat and Sentinel, providing insights into regional and resolution-based differences. To promote further exploration, we openly release IrrMap, along with the derived datasets, benchmark models, and pipeline code, through a GitHub repository: https:// github.com/ Nibir088/IrrMap and Data repository: https:// huggingface.co/ Nibir/IrrMap, providing comprehensive documentation and implementation details.},
keywords = {AI, Irrigation, Mapping},
pubstate = {published},
tppubtype = {inproceedings}
}
We introduce IrrMap, the first large-scale dataset (1.1 million patches) for irrigation method mapping across regions. IrrMap consists of multi-resolution satellite imagery from LandSat and Sentinel, along with key auxiliary data such as crop type, land use, and vegetation indices. The dataset spans 1,687,899 farms and 14,117,330 acres across multiple western U.S. states from 2013 to 2023, providing a rich and diverse foundation for irrigation analysis and ensuring geospatial alignment and quality control. The dataset is ML-ready, with standardized 224×224 GeoTIFF patches, the multiple input data layers, carefully chosen train-test-split data, and accompanying dataloaders for seamless deep learning model training and benchmarking in irrigation mapping. The dataset is also accompanied by a complete pipeline for dataset generation, enabling researchers to extend IrrMap to new regions for irrigation data collection or adapt it with minimal effort for other similar applications in agricultural and geospatial analysis. We also analyze the irrigation method distribution across crop groups, spatial irrigation patterns (using Shannon diversity indices), and irrigated area variations for both LandSat and Sentinel, providing insights into regional and resolution-based differences. To promote further exploration, we openly release IrrMap, along with the derived datasets, benchmark models, and pipeline code, through a GitHub repository: https:// github.com/ Nibir088/IrrMap and Data repository: https:// huggingface.co/ Nibir/IrrMap, providing comprehensive documentation and implementation details.
8.
Hooman Shahrokhi; Devjeet Raj Roy; Yan Yan; Venera Arnaoudova; Jana Doppa
Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression Proceedings Article
In: Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, pp. 3718–3748, PMLR, 2025, (ISSN: 2640-3498).
Abstract | Links | BibTeX | Tags: AI
@inproceedings{shahrokhi_conformal_2025,
title = {Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression},
author = {Hooman Shahrokhi and Devjeet Raj Roy and Yan Yan and Venera Arnaoudova and Jana Doppa},
url = {https://proceedings.mlr.press/v286/shahrokhi25a.html},
year = {2025},
date = {2025-07-01},
urldate = {2025-07-01},
booktitle = {Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence},
pages = {3718\textendash3748},
publisher = {PMLR},
abstract = {We consider the problem of generating valid and small prediction sets by sampling outputs (e.g., software code and natural language text) from a black-box deep generative model for a given input (e.g., textual prompt). The validity of a prediction set is determined by a user-defined binary admissibility function depending on the target application. For example, requiring at least one program in the set to pass all test cases in code generation application. To address this problem, we develop a simple and effective conformal inference algorithm referred to as textbackslashem Generative Prediction Sets (GPS). Given a set of calibration examples and black-box access to a deep generative model, GPS can generate prediction sets with provable guarantees. The key insight behind GPS is to exploit the inherent structure within the distribution over the minimum number of samples needed to obtain an admissible output to develop a simple conformal regression approach over the minimum number of samples. Unlike prior work , the sets generated by GPS do not require iterative sampling at test time, while maintaining strict marginal coverage guarantees. Experiments on multiple datasets for code and math word problems using different large language models demonstrate the efficacy of GPS over state-of-the-art methods.},
note = {ISSN: 2640-3498},
keywords = {AI},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the problem of generating valid and small prediction sets by sampling outputs (e.g., software code and natural language text) from a black-box deep generative model for a given input (e.g., textual prompt). The validity of a prediction set is determined by a user-defined binary admissibility function depending on the target application. For example, requiring at least one program in the set to pass all test cases in code generation application. To address this problem, we develop a simple and effective conformal inference algorithm referred to as textbackslashem Generative Prediction Sets (GPS). Given a set of calibration examples and black-box access to a deep generative model, GPS can generate prediction sets with provable guarantees. The key insight behind GPS is to exploit the inherent structure within the distribution over the minimum number of samples needed to obtain an admissible output to develop a simple conformal regression approach over the minimum number of samples. Unlike prior work , the sets generated by GPS do not require iterative sampling at test time, while maintaining strict marginal coverage guarantees. Experiments on multiple datasets for code and math word problems using different large language models demonstrate the efficacy of GPS over state-of-the-art methods.
9.
John T. Abatzoglou; Charles A. Young; Vishal K. Mehta; Joshua H. Viers; Katherine C. Hegewisch
Predicting California Water-Year Types Using Seasonal Climate Forecasts Journal Article
In: 2025, (Section: Journal of Applied Meteorology and Climatology).
Abstract | Links | BibTeX | Tags: AI, Water
@article{abatzoglou_predicting_2025,
title = {Predicting California Water-Year Types Using Seasonal Climate Forecasts},
author = {John T. Abatzoglou and Charles A. Young and Vishal K. Mehta and Joshua H. Viers and Katherine C. Hegewisch},
url = {https://journals.ametsoc.org/view/journals/apme/64/8/JAMC-D-24-0244.1.xml},
doi = {10.1175/JAMC-D-24-0244.1},
year = {2025},
date = {2025-07-01},
urldate = {2025-07-01},
abstract = {California’s water management is confounded by large interannual variability in water availability inherent in Mediterranean climates and competing water demands in the state. Hydrologic outlooks during late winter and spring are an important consideration that water managers use in water allocation decisions. While hydrologic outlooks are informed by initial conditions, particularly in snowmelt-dominated systems such as California’s Sierra Nevada, there is potential for seasonal climate forecasts to improve such outlooks. California uses a standard water-year typology based on indices of runoff for the Sacramento and San Joaquin Rivers which form the basis of state surface water allocation decisions. We present a simple intuitive model that uses November\textendashMarch watershed average precipitation and temperature in each river basin that explains approximately 90% of the variance in water-year type indices. We then evaluate the utility of these models to forecast water-year types by appending observational temperature and precipitation data with downscaled seasonal climate forecasts from the North American Multimodel Ensemble (NMME). We demonstrate that seasonal climate forecasts augment forecasts based exclusively on initial conditions (e.g., Sierra snowpack). For example, NMME-informed forecasts in January for critical or dry water-year types had an accuracy of 50%\textemdashcomparable to the skill of forecasts based solely on initial conditions 1\textendash2 months later. While California’s hydroclimate is renowned for its unpredictability, results suggest that NMME-informed forecasts may provide additional lead time for water managers to proactively implement water management strategies in anticipation of dry conditions. Significance Statement Water resources in California are wildly variable from year to year resulting in challenges for water managers and users. Improved hydroclimate forecasts can enable proactive measures to secure water for multiple uses. Seasonal forecasts of winter precipitation are notoriously poor in California’s Sierra Nevada, which provides much of the state’s water resources. However, here we show that incorporating seasonal temperature and precipitation forecasts alongside observations adds skill to water-year type outlooks in the Sacramento and San Joaquin River basins. Such forecasts can better inform surface water allocation decisions to minimize the economic and ecological impacts of the state’s variable hydroclimate.},
note = {Section: Journal of Applied Meteorology and Climatology},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {article}
}
California’s water management is confounded by large interannual variability in water availability inherent in Mediterranean climates and competing water demands in the state. Hydrologic outlooks during late winter and spring are an important consideration that water managers use in water allocation decisions. While hydrologic outlooks are informed by initial conditions, particularly in snowmelt-dominated systems such as California’s Sierra Nevada, there is potential for seasonal climate forecasts to improve such outlooks. California uses a standard water-year typology based on indices of runoff for the Sacramento and San Joaquin Rivers which form the basis of state surface water allocation decisions. We present a simple intuitive model that uses November–March watershed average precipitation and temperature in each river basin that explains approximately 90% of the variance in water-year type indices. We then evaluate the utility of these models to forecast water-year types by appending observational temperature and precipitation data with downscaled seasonal climate forecasts from the North American Multimodel Ensemble (NMME). We demonstrate that seasonal climate forecasts augment forecasts based exclusively on initial conditions (e.g., Sierra snowpack). For example, NMME-informed forecasts in January for critical or dry water-year types had an accuracy of 50%—comparable to the skill of forecasts based solely on initial conditions 1–2 months later. While California’s hydroclimate is renowned for its unpredictability, results suggest that NMME-informed forecasts may provide additional lead time for water managers to proactively implement water management strategies in anticipation of dry conditions. Significance Statement Water resources in California are wildly variable from year to year resulting in challenges for water managers and users. Improved hydroclimate forecasts can enable proactive measures to secure water for multiple uses. Seasonal forecasts of winter precipitation are notoriously poor in California’s Sierra Nevada, which provides much of the state’s water resources. However, here we show that incorporating seasonal temperature and precipitation forecasts alongside observations adds skill to water-year type outlooks in the Sacramento and San Joaquin River basins. Such forecasts can better inform surface water allocation decisions to minimize the economic and ecological impacts of the state’s variable hydroclimate.
10.
Soumiki Chattopadhyay
Systematizing inclusive design in MOSIP : an experience report Technical Report
2025, (Publisher: Oregon State University).
Links | BibTeX | Tags: AI, Humans
@techreport{chattopadhyay_systematizing_nodateb,
title = {Systematizing inclusive design in MOSIP : an experience report},
author = {Soumiki Chattopadhyay},
url = {https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/0g354q07x},
year = {2025},
date = {2025-06-10},
note = {Publisher: Oregon State University},
keywords = {AI, Humans},
pubstate = {published},
tppubtype = {techreport}
}
