Browsing by Author "Buler, Jeffrey"
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Item Proceedings of the 2023 Delaware Data Science Symposium(Data Science Institute of the University of Delaware, 2023-09-22) Bagozzi, Benjamin E.; Abou Ali, Hanan; Blaustein, Michael; Blinova, Daria; Buler, Jeffrey; Carney, Lynette; Chandrasekaran, Sunita; Davey, Adam; Fleischhacker, Adam; Ostovari, Mina; Peart, Daniel; Smith, Sam; Tawiah, Nii Adjetey; Wu, Cathy H.The 2023 Delaware Data Science Symposium was held on September 22nd with a primary focus on the role of data science in financial technology (FinTech) and health equity. The Symposium was organized by the University of Delaware’s (UD’s) Data Science Institute (DSI) with support from Tech Impact, Dupont, Kendal Corporation, Intellitec Solutions, UD’s Library, Museums, & Press, the UD Career Center, the UD Graduate College, the UD Master of Science in Data Science Program, UD’s Artificial Intelligence Center of Excellence (AICOE), and the DSI. It represented the fourth Delaware Data Science Symposium hosted at the University of Delaware, and the third such Symposium since the DSI’s inception. Altogether, the Symposium saw over 280 registered attendees from the University of Delaware and partner institutions across the Mid-Atlantic and beyond. The 2023 Delaware Data Science Symposium included multiple keynote speakers, a series of initiative & lightning talks, a poster session, a panel on data science-driven equity from healthcare, FinTech, community, and educational perspectives, and a session on UD’s summer 2023 Data Science (DS) + Artificial Intelligence (AI) Hackathon. Alongside these sessions, the Symposium also facilitated two associated satellite events. The first was a September 21st Data Science and Analytics Open House for UD graduate programs focused on data science and analytics. The second was a September 25th workshop on the use of MATLAB for low-code AI.Item Relating weather radar data to migrating waterfowl abundance in the Rainwater Basin of Nebraska(The Journal of Wildlife Management, 2023-06-01) Liu, Boyan; Kemink, Kaylan; Sieges, Mason; Smolinsky, Jaclyn; Varner, Dana; Buler, JeffreyWaterfowl migrations are large-scale events that involve millions of birds moving over broad geographic extents, which make them difficult to quantify and study. Historically, wildlife managers have relied mostly on field surveys, such as visual counts from the ground or air that sample at small spatial or temporal extents, or both. Combining field surveys with remote sensing data comprehensively collected over large spatial extents at high temporal frequency may improve the study of migrating waterfowl distributions. We tested the strength of the relationship between broad-scale weather surveillance radar data and fine-scale field surveys of waterfowl abundance at wetlands within the Rainwater Basin of Nebraska, USA, from 2017–2019. Radar reflectivity of waterfowl at the peak exodus of evening flights was positively correlated with diurnal waterfowl count data, although there was unexplained variation in the relationship. The association was also very similar across various time scales ranging from daily to monthly averages of waterfowl abundance. We suggest that human-based ground surveys can calibrate and leverage more comprehensive remote sensing data to get a broad understanding of waterfowl distributions during migration. Several confounding factors, such as spatial displacement between radar and survey observation, individual variation in orientation and body size, and identification of avian species sampled by radar, remain on the path to improving radar-based waterfowl estimates.