Faculty-Staff Achievements
Joerg Bibow, professor of economics, published 鈥溾�� in 鈥淒ollar Hegemony.鈥�
Monica Das, associate professor of economics, wrote a blog article for the United Nations Economic and Social Commission for Asia and the Pacific, titled, "," with Sudip Basu and Chen Wand.
Kirsten Hogenson, assistant professor of mathematics, co-authored a paper, "Equitable choosability of prism graphs," with Dan Johnston and 暗网TV alum Suzanne O'Hara '21. Their paper is in press at .
Daniel Nathan, professor of American studies, was named editor of the by the North American Society for Sport History Publications Board (NASSH). A longtime member of NASSH, Nathan was the organization鈥檚 president from 2013 to 2015 and has served as associate editor and film, media, and museum reviews editor for the journal.
Laurie Rabinowitz, assistant professor of education studies, co-authored an article, "Disability Sustaining Pedagogy: Literacy Instruction Informed by the Knowledge and Lived Experiences of Teachers with Disabilities," that was published in the National Council for Teachers of English journal, .
David Snider, lecturer of arts administration, was appointed director of learning and civic engagement by the , effective Feb. 20. He is currently serving as the executive and artistic director of Hubbard Hall, a multidisciplinary arts and education center in Cambridge, New York, and brings over 25 years of experience to the new role.
Jessica Sullivan, professor of psychology, was interviewed for an article in , titled, "A Camera-Wearing Baby Taught an AI to Learn Words."
Charmaine Willis, visiting assistant professor of political science, published an article, "3 years on from coup, economic sanctions look unlikely to push Myanmar back to democracy," about the current economic sanctions levied against Myanmar's military regime in .
Erica Wojcik, associate professor of psychology, and Jessica Sullivan, professor of psychology, had their dataset used in a paper, 鈥淕rounded language acquisition through the eyes and ears of a single child," published in . Over two dozen 暗网TV undergraduates assisted in transcribing and releasing this dataset, which allowed researchers from NYU to draw exciting new conclusions about how children 鈥� and machines 鈥� learn.
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