Speakers

Title: Suggesting New Research Directions via Large Knowledge Network and Machine Learning

Dr. Xuemei Gu is a Humboldt Postdoctoral Fellow at the Max Planck Institute for the Science of Light. She received her PhD in 2020 from Nanjing University, where she worked on computer science and quantum information. During 2017 to 2019, she conducted her PhD research in the group of Anton Zeilinger at IQOQI Vienna. From 2020 to 2022, she worked as a postdoctoral researcher at the University of Science and Technology of China, where she focused on the understanding of quantum foundation and implemented related experiments. Her research interests include quantum foundation, quantum networks, artificial intelligence, machine learning, and graph theory. Currently, she is exploring how AI can make new conceptual advances in physics and other scientific domains.

Title: Detecting Invisible Labor in Data-Intensive Scientific Communities

Dr. Sarah Bratt, PhD is an Assistant Professor at the University of Arizona School of Information (iSchool). She holds a B.S. in Philosophy from Ithaca College and M.S. in Library and Information Science with a Data Science certificate from Syracuse University. Her research lies at the intersection of scholarly communication, research data management, and science of science. The overarching goal of her research is to understand and design for long-term research data sustainability and actionable science policy. Her research has been published in Quantitative Science Studies (QSS), Journal of Informetrics, and Scientometrics. She was a Research Fellow at the Laboratory of Innovation Science at Harvard (LISH), a Teaching Fellow at the iSchool Inclusion Institute (i3), and has received several awards including the PhD prize in Information Science & Technology from Syracuse University and honorable mention as a 2022 Better Scientific Software (BSSw) Fellow.

Title: TBD

Dr. Michael Kopp