Organizing Committee
Tirthankar Ghosal
Oak Ridge National Laboratory, USA
Tirthankar Ghosal is a scientist at ORNL. His main research interests are NLP/ML for Scientific Discourse Processing and Peer Reviews, Text/Dialogue Summarization, Argumentation Mining. He is currently the co-editor of SIGIR Forum. Beside SDP, Tirthankar is associated in organizing AutoMin @ Interspeech 2021, SummDial @ SIGDial 2021, WiNLP 2021, etc.
Philipp Mayr
GESIS – Leibniz Institute for the Social Sciences, Germany
Philipp Mayr is a team leader at the GESIS -- Leibniz-Institute for the Social Sciences department Knowledge Technologies for the Social Sciences. Philipp Mayr received his PhD in applied informetrics and information retrieval from Humboldt University Berlin in 2009. His research group focuses on methods and techniques for interactive information and data retrieval. Philipp was the main organizer of the BIR workshops at ECIR 2014-2020 and the BIRNDL workshops at JCDL and SIGIR.
Anita de Waard
Elsevier, USA
Anita is VP of Research Collaborations, where her work focuses on working with academic and industry partners on projects pertaining to progressing modes and frameworks for scholarly communication. She works on bridging the gap between science publishing and computational and information technologies. De Waard has worked on discourse analysis of scientific narratives, with an emphasis on finding key epistemic components in biological text, and helped establish the TAC BiomedSumm task in 2014.
Aakanksha Naik
Allen Institute for AI, USA
Aakanksha Naik is a research scientist on the Semantic Scholar team at AI2, where her work focuses on information extraction from scholarly literature and leveraging scientific knowledge to assist human decision-making. She received her PhD in language technologies from Carnegie Mellon University. She was a co-organizer of the PAN-DL workshop at COLING 2022 and organized mentorship programs at NAACL 2019, ACL 2019, ACL 2020 and ACL 2022.
Shannon Shen
Massachussets Institute of Technology, USA
Shannon Shen is a PhD student in the Computer Science & Artificial Intelligence Laboratory at the Massachusetts Institute of Technology, advised by David Sontag. His research focuses on NLP and Human AI collaboration, especially for expert domains like science and healthcare. Previously, Shannon also worked as researcher on the Semantic Scholar team, and developed methods and tools for processing scholarly document PDFs that are published in TACL and ACL.
Amanpreet Singh
Allen Institute for AI, USA
Amanpreet is a Research Engineer in the Semantic Scholar team at AI2, with a focus on applying NLP to create tools and resources like SciRepEval for tasks in the scientific literature domain. He holds a Master's degree in Computer Science with Data Science specialization from Stony Brook University.
Orion Weller
Johns Hopkins University, USA
Orion Weller is a PhD student at the Center for Language and Speech Processing at Johns Hopkins University. Advised by Benjamin Van Durme and Dawn Lawrie, he focuses on the intersection of natural language processing and information retrieval. Orion is supported by a National Science Foundation Graduate Research Fellowship.
Yanxia Qin
National University of Singapore, Singapore
Yanxia Qin is a postdoctoral research fellow at the School of Computing at the National University of Singapore. She obtained her Ph.D. degree from Harbin Institute of Technology, China. Her research interests include scientific document processing, domain specialization of large language models, and text generation techniques.
Yoonjoo Lee
Korea Advanced Institute of Science & Technology, South Korea
Yoonjoo Lee is a PhD student at the School of Computing at Korea Advanced Institute of Science & Technology, advised by Juho Kim. Her research interests lie in the intersection of human-computer interaction (HCI) and natural language processing (NLP). She aims to support people to learn from and make sense of dense information (e.g., content in lecture videos, scientific articles) by creating diverse scaffoldings (e.g., QAs, dialogues, explanations) using AI models.
