Publications

@article{ghosh_improving_2023,

title = {Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis},

author = {Ghosh, Subhankar and Belkhouja, Taha and Yan, Yan and Doppa, Janardhan Rao},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/25936},

doi = {10.1609/aaai.v37i6.25936},

issn = {2374-3468},

year  = {2023},

date = {2023-06-01},

urldate = {2023-06-01},

journal = {Proceedings of the AAAI Conference on Artificial Intelligence},

volume = {37},

number = {6},

pages = {7722\textendash7730},

abstract = {Safe deployment of deep neural networks in high-stake real-world applications require theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled framework for uncertainty quantification of deep models in the form of prediction set for classification tasks with a user-specified coverage (i.e., true class label is contained with high probability). This paper proposes a novel algorithm referred to as Neighborhood Conformal Prediction (NCP) to improve the efficiency of uncertainty quantification from CP for deep classifiers (i.e., reduce prediction set size). The key idea behind NCP is to use the learned representation of the neural network to identify k nearest-neighbor calibration examples for a given testing input and assign them importance weights proportional to their distance to create adaptive prediction sets. We theoretically show that if the learned data representation of the neural network satisfies some mild conditions, NCP will produce smaller prediction sets than traditional CP algorithms. Our comprehensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using diverse deep neural networks strongly demonstrate that NCP leads to significant reduction in prediction set size over prior CP methods.},

note = {Number: 6},

keywords = {AI, Water},

pubstate = {published},

tppubtype = {article}

}

@article{abatzoglou_downscaled_2023,

title = {Downscaled subseasonal fire danger forecast skill across the contiguous United States},

author = {Abatzoglou, John T. and McEvoy, Daniel J. and Nauslar, Nicholas J. and Hegewisch, Katherine C. and Huntington, Justin L.},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/asl.1165},

doi = {10.1002/asl.1165},

issn = {1530-261X},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Atmospheric Science Letters},

volume = {24},

number = {8},

pages = {e1165},

abstract = {The increasing complexity and impacts of fire seasons in the United States have prompted efforts to improve early warning systems for wildland fire management. Outlooks of potential fire activity at lead-times of several weeks can help in wildland fire resource allocation as well as complement short-term meteorological forecasts for ongoing fire events. Here, we describe an experimental system for developing downscaled ensemble-based subseasonal forecasts for the contiguous US using NCEP's operational Climate Forecast System version 2 model. These forecasts are used to calculate forecasted fire danger indices from the United States (US) National Fire Danger Rating System in addition to forecasts of evaporative demand. We further illustrate the skill of subseasonal forecasts on weekly timescales using hindcasts from 2011 to 2021. Results show that while forecast skill degrades with time, statistically significant week 3 correlative skill was found for 76% and 30% of the contiguous US for Energy Release Component and evaporative demand, respectively. These results highlight the potential value of experimental subseasonal forecasts in complementing existing information streams in weekly-to-monthly fire business decision making for suppression-based decisions and geographic reallocation of resources during the fire season, as well for proactive fire management actions outside of the core fire season.},

note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/asl.1165},

keywords = {Fallow, Water},

pubstate = {published},

tppubtype = {article}

}

@article{kalyanaraman_special_2022,

title = {Special report: The AgAID AI institute for transforming workforce and decision support in agriculture},

author = {Kalyanaraman, Ananth and Burnett, Margaret and Fern, Alan and Khot, Lav and Viers, Joshua},

url = {https://www.sciencedirect.com/science/article/pii/S0168169922002617},

doi = {10.1016/j.compag.2022.106944},

issn = {0168-1699},

year  = {2022},

date = {2022-06-01},

urldate = {2022-08-16},

journal = {Computers and Electronics in Agriculture},

volume = {197},

pages = {106944},

abstract = {Tackling the grand challenges of 21st century agriculture (Ag) will require a fundamental shift in the way we envision the role of artificial intelligence (AI) technologies, and in the way we build agricultural AI systems. This shift is needed especially for complex, high-value agricultural ecosystems such as those in the Western U.S., where 300+ crops are grown. Farmers and policy makers in this region face variable profitability, major crop loss and poor crop quality owing to several challenges, including increased labor costs and shortages of skilled workers, weather and management uncertainties, and water scarcity. While AI is expected to be a significant tool for addressing these challenges, AI capabilities must be expanded and will need to account for human input and human behavior \textendash calling for a strong AI-Ag coalition that also creates new opportunities to achieve sustained innovation. Accomplishing this goal goes well beyond the scope of any specific research project or disciplinary silo and requires a more holistic transdisciplinary effort in research, development, and training. To respond to this need, we initiated the AgAID Institute, a multi-institution, transdisciplinary National AI Research Institute that will build new public-private partnerships involving a diverse range of stakeholders in both agriculture and AI. The institute focuses its efforts on providing AI solutions to specialty crop agriculture where the challenges pertaining to water availability, climate variability and extreme weather, and labor shortages, are all significantly pronounced. Our approach to all AgAID Institute activities is being guided by three cross-cutting principles: (i) adoption as a first principle in AI design; (ii) adaptability to changing environments and scales, and (iii) amplification of human skills and machine efficiency. The AgAID Institute is conducting a range of activities including: using agricultural AI applications as testbeds for developing innovative AI technologies and workflows; laying the technological foundations for climate-smart agriculture; serving as a nexus for culturally inclusive collaborative and transdisciplinary learning and knowledge co-production; preparing the next generation workforce for careers at the intersection of Ag and AI technology; and facilitating technology adoption and transfer.},

keywords = {AI, Education, Farm Ops, Humans, Labor, Water},

pubstate = {published},

tppubtype = {article}

}