معرفی
Daniel Acuña serves as an Associate Professor in the Department of Computer Science at the University of Colorado at Boulder, where he leads the Science of Science and Computational Discovery Lab. Currently on leave for Academic Year 2025-26, he remains an active faculty member focused on accelerating scientific discovery through computational methods.
Dr. Acuña's research centers on the science of science, a specialized field within computational social science. His work applies machine learning and artificial intelligence to analyze vast datasets of publications and citations, uncovering patterns that optimize knowledge production. He investigates historical relationships in scientific discovery, collaboration mechanisms, and funding decision processes. Recently, his research has expanded to address biases in artificial intelligence systems and develop detection methodologies. His lab creates practical web-based software tools designed to enhance literature search capabilities, improve peer review processes, and detect scientific fraud.
Dr. Acuña has secured significant research funding from prestigious organizations including the National Science Foundation (NSF), Department of Health and Human Services (DDHS), Sloan Foundation, and Defense Advanced Research Projects Agency (DARPA) through the SCORE project. His influential work has received notable media coverage in high-impact outlets such as Nature News, Nature Podcast, The Chronicle of Higher Education, NPR, and The Scientist. He actively seeks new students to join his research laboratory, indicating ongoing commitment to mentoring despite his upcoming leave period.
The Science of Science and Computational Discovery Lab represents his primary research environment, where interdisciplinary approaches bridge computer science with social science methodologies to fundamentally understand and improve the scientific process itself.
