@article{union,
title = "A Fast Algorithm to Delineate Watershed Boundaries for Simple Geometries",
journal = "Environmental Modelling & Software",
pages = "104842",
year = "2020",
issn = "1364-8152",
doi = "https://doi.org/10.1016/j.envsoft.2020.104842",
url = "http:https://www.sciencedirect.com/science/article/pii/S1364815220308999",
author = "Scott Haag and Daniel Schwartz and Bahareh Shakibajahromi and Michael Campagna and Ali Shokoufandeh",
keywords = "Watershed boundary, marching algorithm, modified nested set algorithm, constrained watershed boundary",
abstract = "In this manuscript, we describe a computational model to delineate watershed boundaries for simple geometries (points, lines or polygons) where maximum water flow distance can be constrained by a user-defined variable. This method generalizes earlier research to delineate watershed boundaries using a marching algorithm. Our proposed method allows users to delineate watersheds for a number of use-cases including evaluating best management practices (BMP’s), measuring water impacts to municipal, city and parcel boundaries and others. We describe computational complexity and space costs and compare them experimentally to standard techniques."
}

@inproceedings{icons,
author = {Carter, John and Rego, Jocelyn and Schwartz, Daniel and Bhandawat, Vikas and Kim, Edward},
title = {Learning Spiking Neural Network Models of Drosophila Olfaction},
year = {2020},
isbn = {9781450388511},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3407197.3407214},
doi = {10.1145/3407197.3407214},
booktitle = {International Conference on Neuromorphic Systems 2020},
articleno = {20},
numpages = {5},
keywords = {neuro-inspired artificial intelligence, machine learning, olfaction, neuromorphic computing, spiking neural networks},
location = {Oak Ridge, TN, USA},
series = {ICONS 2020}
}