Bio

Daniel Acuna is an Associate Professor in the School of Information Studies at Syracuse University, Syracuse, NY. His current research aims to understand decision-making in science—from helping hiring committees to predict future academic success to removing the potential biases that scientists and funding agencies commit during peer review. To achieve these tasks, Dr. Acuna harnesses vast datasets about scientific activities and applies Machine Learning and A.I. to uncover rules that make publication, collaboration, and funding decisions more successful. Recently, he has been interested in biases in artificial intelligence and in developing methods for detecting it. Simultaneously, he has created tools to improve literature search, peer review, and detect scientific fraud. He has grants from NSF, DDHS, Sloan Foundation, and DARPA, and his work has been featured in Nature News, Nature Podcast, The Chronicle of Higher Education, NPR, and The Scientist. Before joining Syracuse University, Acuna studied a Ph.D. in Computer Science at the University of Minnesota - Twin Cities and was a postdoctoral researcher at Northwestern University and the Rehabilitation Institute of Chicago. During his graduate studies, he received an NIH Neuro-physical-computational Sciences (NPCS) Graduate Training Fellowship, NIPS Travel Award, and a CONICYT-World Bank Fellowship