Browsing by Author "Bagozzi, Benjamin E."
Now showing 1 - 10 of 10
Results Per Page
Sort Options
Item DARWIN - A Resource for Computational and Data-intensive Research at the University of Delaware and in the Delaware Region(Data Science Institute [DSI], University of Delaware, Newark, DE, 2021) Eigenmann, Rudolf; Bagozzi, Benjamin E.; Jayaraman, Arthi; Totten, William; Wu, Cathy H.Item Human Rights Violations in Space: Assessing the External Validity of Machine-Geocoded versus Human-Geocoded Data(Political Analysis, 2021-12-15) Stundal, Logan; Bagozzi, Benjamin E.; Freeman, John R.; Holmes, Jennifer S.Political event data are widely used in studies of political violence. Recent years have seen notable advances in the automated coding of political event data from international news sources. Yet, the validity of machine-coded event data remains disputed, especially in the context of event geolocation. We analyze the frequencies of human- and machine-geocoded event data agreement in relation to an independent (ground truth) source. The events are human rights violations in Colombia. We perform our evaluation for a key, 8-year period of the Colombian conflict and in three 2-year subperiods as well as for a selected set of (non)journalistically remote municipalities. As a complement to this analysis, we estimate spatial probit models based on the three datasets. These models assume Gaussian Markov Random Field error processes; they are constructed using a stochastic partial differential equation and estimated with integrated nested Laplacian approximation. The estimated models tell us whether the three datasets produce comparable predictions, underreport events in relation to the same covariates, and have similar patterns of prediction error. Together the two analyses show that, for this subnational conflict, the machine- and human-geocoded datasets are comparable in terms of external validity but, according to the geostatistical models, produce prediction errors that differ in important respects.Item ‘Inspired to Action’: Immigrants’ Faith-Based Organizations’ Responses across Two Pandemics(Journal of Immigrant and Refugee Studies, 2022-02-12) Maduka-Ezeh, Awele; Bagozzi, Benjamin E.; Gardesey, Mawuna; Ezeh, Ikwesilotuto T.; Nibbs, Farrah; Nwegbu, Somawina; Mai, Ryan; Horney, Jennifer A.; Trainor, JosephSources of disaster resilience represent important (but understudied) dimensions of the interplay between immigrants and disasters, as do immigrants’ disaster response activities. Using key informant interviews, we examine immigrant faith-based organizations’ (FBO) responses to two contemporary pandemics. Additionally, we assess for the presence of disaster-relevant social capital in immigrant FBOs. FBOs were found to possess key components of social capital and to actively engage in pandemic response activities, including provision of health risk communication, education, leadership, infection control measures, cash and in-kind contributions, advocacy, and psychosocial support. For immigrant communities, FBO-based social capital contributes to effective disaster and pandemic responses.Item Proceedings of the 2020 DARWIN Computing Symposium(Data Science Institute of the University of Delaware, 2020-02-12) Jayraman, Arthi; Bagozzi, Benjamin E.; Eigenmann, Rudolf; Totten, William; Wu, Cathy H.The DARWIN Computing Symposium 2020—sponsored by the Data Science Institute of the University of Delaware—was held on February 12th, 2020. It represented the first event in a series of Symposia motivated by a National Science Foundation (NSF) MRI Award, also known as the Delaware Advanced Research Workforce and Innovation Network (DARWIN). As part of an NSF Major Research Instrumentation award (OAC-1919839), DARWIN has the goal of catalyzing "research and education at the University of Delaware (UD) and partners by acquiring a big data and high-performance computing system and making this instrument available to the community." This first DARWIN Computing Symposium introduced the machine—being ordered at the time— to the community, showcased computational and data-enabled research that will take advantage of the instrument, and provided opportunities for forming collaborations among future users at UD and regional partners. The Symposium included presentations detailing research involving UD faculty and members of DARWIN partner institutions that spanned the chemical and material sciences, engineering, the biological sciences, the environmental sciences, business, the social sciences, and education. Alongside this, an informational session on the DARWIN machine, a panel, and a student poster session provided an equally diverse range of additional viewpoints on computational- and data-intensive research, training, and education across the Delaware region and beyond. In addition to the NSF and Data Science Institute, the 2020 DARWIN Computing Symposium was sponsored by Atipa Technologies, DELL and CompassRed. Dr. Arthi Jayaraman, UD Professor and DARWIN Co-PI, served as chair of the 2020 DARWIN Computing Symposium.Item Proceedings of the 2021 DARWIN Computing Symposium(Data Science Institute of the University of Delaware, 2021-02-12) Bagozzi, Benjamin E.; Eigenmann, Rudolf; Jayaraman, Arthi; Totten, William; Wu, Cathy H.The DARWIN Computing Symposium 2021—sponsored by the Data Science Institute of the University of Delaware—was held on February 12, 2021. It represented the second event in a series of Symposia motivated by a National Science Foundation (NSF) MRI Award, also known as the Delaware Advanced Research Workforce and Innovation Network (DARWIN). As part of an NSF Major Research Instrumentation award (OAC-1919839), DARWIN has the goal of catalyzing "research and education at the University of Delaware (UD) and partners by acquiring a big data and high-performance computing system and making this instrument available to the community." This particular Symposium showcased recent computational and data-enabled research across the Delaware region, offered perspectives on broadening participation in computational and data-intensive research, and facilitated opportunities for forming collaborations among future DARWIN users at UD and regional partners. It also provided an overview of the newly operational DARWIN big data and high-performance computing machine via a panel on “early user mode” experiences. The 2021 DARWIN Computing Symposium was supported by the NSF and UD’s Data Science Institute. Dr. Benjamin E. Bagozzi, UD Associate Professor and DARWIN Co-PI, served as chair of the 2021 Symposium.Item Proceedings of the 2022 DARWIN Computing Symposium(Data Science Institute of the University of Delaware, 2022-03-24) Hadden-Perilla, Jodi A.; Perilla, Juan R.; Bagozzi, Benjamin E.; Eigenmann, Rudolf; Jayaraman, Arthi; Totten, William; Wu, Cathy H.The DARWIN Computing Symposium 2022—sponsored by the Data Science Institute of the University of Delaware—was held on March 24, 2022. It represented the third event in a series of Symposia motivated by a National Science Foundation (NSF) MRI Award, also known as the Delaware Advanced Research Workforce and Innovation Network (DARWIN). As part of an NSF Major Research Instrumentation award (OAC-1919839), DARWIN has the goal of catalyzing "research and education at the University of Delaware (UD) and partners by acquiring a big data and high-performance computing system and making this instrument available to the community." This third DARWIN Computing Symposium presented a wide variety of research enabled by the DARWIN machine to the Delaware community. Alongside this, it showcased additional computational and dataenabled research, provided details on accessing DARWIN for University of Delaware (UD) and partner institutions, and facilitated opportunities for forming collaborations among future users at UD and regional partners. In addition to the NSF and the Data Science Institute, the 2022 DARWIN Computing Symposium was sponsored by DELL and Nemours Children's Health. Drs. Jodi Haden-Perilla and Juan Perilla, both of the University of Delaware, served as co-chairs of the 2022 DARWIN Computing Symposium.Item Proceedings of the 2023 DARWIN Computing Symposium(Data Science Institute of the University of Delaware, 2023-02-23) Safronova, Marianna S.; Bagozzi, Benjamin E.; Eigenmann, Rudolf; Jayaraman, Arthi; Totten, William; Wu, Cathy H.The DARWIN Computing Symposium 2023—sponsored by the Data Science Institute of the University of Delaware—was held on February 23, 2023. It represented the fourth event in a series of Symposia motivated by a National Science Foundation (NSF) MRI Award, also known as the Delaware Advanced Research Workforce and Innovation Network (DARWIN). As part of an NSF Major Research Instrumentation award (OAC-1919839), DARWIN has the goal of catalyzing "research and education at the University of Delaware (UD) and partners by acquiring a big data and high-performance computing system and making this instrument available to the community." This fourth DARWIN Computing Symposium presented a wide variety of research enabled by the DARWIN machine to the Delaware community. It also showcased additional computational and data-enabled research, provided perspectives on broadening participation in computational and data-intensive research, and facilitated opportunities for forming collaborations among future users at UD and regional partners. In addition to the NSF and the Data Science Institute, the 2023 DARWIN Computing Symposium was sponsored by AMD, BioCurie, Chemours, and Tech Impact. Dr. Marianna Safronova, Professor of Physics at the Department of Physics and Astronomy, University of Delaware, served as chair of the 2023 DARWIN Computing Symposium.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 Proceedings of the 2024 DARWIN Computing Symposium(Data Science Institute of the University of Delaware, 2024-02-12) Hsu, Tian-Jian; Bagozzi, Benjamin E.; Eigenmann, Rudolf; Jayaraman, Arthi; Totten, William; Wu, Cathy H.; Blaustein, Michael; Blinova, Daria; Carney, Lynette; Huffman, John; Smith, Samantha; Zhang, JiayeThe DARWIN Computing Symposium 2024—sponsored by the Data Science Institute (DSI) of the University of Delaware—was held on February 12, 2024. It represented the fifth event in a series of Symposia motivated by a National Science Foundation (NSF) MRI Award, also known as the Delaware Advanced Research Workforce and Innovation Network (DARWIN). As part of an NSF Major Research Instrumentation award (OAC-1919839), DARWIN focuses on catalyzing "research and education at the University of Delaware (UD) and partners by acquiring a big data and high-performance computing system and making this instrument available to the community." In an effort to identify and advance future computing needs for artificial intelligence, to reduce the overhead for domain scientists utilizing HPC, and to develop regional partnerships, this fifth DARWIN Computing Symposium more specifically featured a panel and a keynote talk, as well as a series of research talks on DARWIN-enabled research, on computational and data-intensive (CDI) research/training needs, and on AI-focused CDI research more generally. These talks highlighted the use of AI in HPC to advance sciences and predictive capabilities with societal relevance across a wide range of domains. A panel discussion then facilitated interactions between research software engineers and domain scientists with an eye towards advancing scientific progress in different disciplines. In addition, 30 poster presentations by students and postdocs highlighted a number of relevant CDI research projects. Alongside the NSF and the Data Science Institute, the 2023 DARWIN Computing Symposium was sponsored by Tech Impact, UD’s Delaware Environmental Institute, UD’s Center for Applied Coastal Research, UD Information Technologies, and the University of Delaware Faculty Senate. Dr. Tian-Jian Hsu, University of Delaware Professor of Civil & Environmental Engineering and Director of the Center for Applied Coastal Research served as chair of the 2024 DARWIN Computing Symposium.Item Social media analysis reveals environmental injustices in Philadelphia urban parks(Scientific Reports, 2023-08-03) Walter, Matthew; Bagozzi, Benjamin E.; Ajibade, Idowu; Mondal, PinkiThe United Nations Sustainable Development Goal (SDG) target 11.7 calls for access to safe and inclusive green spaces for all communities. Yet, historical residential segregation in the USA has resulted in poor quality urban parks near neighborhoods with primarily disadvantaged socioeconomic status groups, and an extensive park system that addresses the needs of primarily White middle-class residents. Here we center the voices of historically marginalized urban residents by using Natural Language Processing and Geographic Information Science to analyze a large dataset (n = 143,913) of Google Map reviews from 2011 to 2022 across 285 parks in the City of Philadelphia, USA. We find that parks in neighborhoods with a high number of residents from historically disadvantaged demographic groups are likely to receive lower scores on Google Maps. Physical characteristics of these parks based on aerial and satellite images and ancillary data corroborate the public perception of park quality. Topic modeling of park reviews reveal that the diverse environmental justice needs of historically marginalized communities must be met to reduce the uneven park quality—a goal in line with achieving SDG 11 by 2030.