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. He is looking for students to join his lab.
His current 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.
In addition to his research, Daniel enjoys building communities around science of science and research integrity. He co-organizes the Science of Science Summer School (S4) (2021, 2022), the Computational Research Integrity (CRI-CONF) conference (2021), and the Computational Research Integrity competitions (2022). In addition, he is part of the ACM's Diversity, Equity, and Inclusion (DEI) council, contributing to the social justice initiative on publications, awards, and peer review.
His main research areas and representative publications are:
- Science of science
- Ke, Q., Liang, L., Ding, Y., David, S. V., & Acuna, D. E. (2022). A dataset of mentorship in bioscience with semantic and demographic estimations. Scientific data, 9(1), 1-12. [Link]
- Liénard, J. F., Achakulvisut, T., Acuna, D. E., & David, S. V. (2018). Intellectual synthesis in mentorship determines success in academic careers. Nature communications, 9(1), 1-13. [Link]
- Acuna, D. E., Allesina, S., & Kording, K. P. (2012). Predicting scientific success. Nature, 489(7415), 201-202. [Link]
- AI for science
- Zeng, T., & Acuna, D. E. (2020). Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable models. Scientometrics, 124(1), 399-428. [Link]
- Zeng, T., Wu, L., Bratt, S., & Acuna, D. E. (2020). Assigning credit to scientific datasets using article citation networks. Journal of Informetrics, 14(2), 101013. [Link]
- Achakulvisut, T., Acuna, D. E., Ruangrong, T., & Kording, K. (2016). Science Concierge: A fast content-based recommendation system for scientific publications. PloS one, 11(7), e0158423. [Link]
- Fairness in AI
- Acuna, D. E., & Liang, L. (2021). Are AI ethics conferences different and more diverse compared to traditional computer science conferences? AIES '21: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society [Link]
- Liang, L., & Acuna, D. E. (2020). Artificial mental phenomena: Psychophysics as a framework to detect perception biases in AI models. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 403-412) (FAT* 2020) [Link]
- Computational Research Integrity
- Zhuang, H., Huang, T. Y., & Acuna, D. E. (2021). Graphical integrity issues in open access publications: Detection and patterns of proportional ink violations. PLoS computational biology, 17(12), e1009650. [Link]
- Acuna, D. E., Brookes, P. S., & Kording, K. P. (2018). Bioscience-scale automated detection of figure element reuse. BioRxiv, 269415. [Link]
- Xiang, Z., & Acuna, D. E. (2020). Scientific image tampering detection based on noise inconsistencies: A method and datasets. arXiv preprint arXiv:2001.07799. [Link]
Daniel drove 28 hours to Boulder from beautiful Central New York, where he was an Associate Professor in the School of Information Studies at Syracuse University. Before then, he was a postdoctoral researcher at Northwestern University and the Rehabilitation Institute of Chicago. He obtained his Ph.D. in Computer Science at the University of Minnesota - Twin Cities, receiving an NIH Neuro-physical-computational Sciences (NPCS) Graduate Training Fellowship, a CONICYT-World Bank Fellowship, and a NeurIPS Travel Award. Daniel was born in Santiago, Chile, where he attended the University of Santiago, Chile (obviously.)