Daniel Acuña is an Associate Professor in the Department of Computer Science at the University of Colorado at Boulder. He leads the Science of Science and Computational Discovery Lab. He works in science of science, a subfield of computational social science, and A.I. for science. He writes papers and builds web-based software tools to accelerate knowledge discovery. Daniel’s research aims to understand historical relationships, mechanisms, and optimization opportunities of knowledge production. Daniel harnesses vast datasets about publications and citations 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 developing methods for detecting them. In addition, he has created tools to improve literature search, peer review, and detect scientific fraud. He has been funded by NSF, DDHS, Sloan Foundation, and DARPA through the SCORE project, and his work has been featured in Nature News, Nature Podcast, The Chronicle of Higher Education, NPR, and the Scientist.
