• 338 Citations
  • 9 h-Index

Research output per year

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Personal profile

Professional Information

Dr. Acuna studied a Ph.D. in Computer Science at the University of Minnesota, Twin Cities. During his graduate studies, he was part of the Center for Cognitive Sciences in the Department of Psychology and received a NIH Neuro-physical-computational Sciences (NPCS) Graduate Training Fellowship from the Department of Neuroscience. He additionally received the support of a CONICYT-World Bank fellowship and a travel award from the Neural Information Processing Systems (NIPS) 2008 conference. During his postdoctoral studies at the Rehabilitation Institute of Chicago and Northwestern University, Dr. Acuna gave multiple invited plenary talks and was interviewed by Nature Podcast, The Chronicle of Higher Education, NPR Science Friday, and The Scientist. Amazon AWS and Microsoft Azure have generously supported his big data analytics work with three academic computational credit awards.

Research interests

Since his Bachelor studies in Computer Science at the University of Santiago, Chile, Dr. Acuna has had a long interest in understanding human decision making and mimicking human semi-optimal strategies with algorithms. His long-term goal is to teach computers to learn from humans and enhance human decision making through the use of Machine Learning and Artificial Intelligence. As a postdoctoral researcher at the Rehabilitation Institute of Chicago and Northwestern University, Dr. Acuna studied machine learning, statistical decision theory, and the neural basis of learning.

The goal of his current research is 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. Simultaneously, he has created tools to improve literature search (https://scholarfy.net, https://eileen.io), peer review, and modeling of scientific expertise. Dr. Acuna imagines a future in which humans and A.I. agents seamlessly cooperate to make science more agile and accurate.

Daniel enjoys making contributions to the open source Data Science community, often creating his own packages and tools (https://github.com/daniel-acuna). For example, he recently gave a talk to the Chicago Python User Group, where he shared his views with over 80 professional developers on how science and industry face similar challenges. He is also looking to license multiple technologies co-invented by him.

Research Interests

  • Science of Science
  • Research Policy
  • Computational Research Integrity
  • Artificial Intelligence
  • Recommendation systems
  • Deep learning

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Research Output

  • 338 Citations
  • 9 h-Index
  • 19 Article
  • 5 Conference contribution
  • 1 Comment/debate

Artificial mental phenomena: Psychophysics as a framework to detect perception biases in AI models

Liang, L. & Acuna, D. E., Jan 27 2020, FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, Inc, p. 403-412 10 p. (FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Assigning credit to scientific datasets using article citation networks

    Zeng, T., Wu, L., Bratt, S. & Acuna, D. E., May 2020, In : Journal of Informetrics. 14, 2, 101013.

    Research output: Contribution to journalArticle

  • Estimating a Null Model of Scientific Image Reuse to Support Research Integrity Investigations

    Acuna, D. E. & Xiang, Z., Feb 22 2020, In : Arxiv.

    Research output: Contribution to journalArticle

  • Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable models

    Zeng, T. & Acuna, D. E., Jul 1 2020, In : Scientometrics. 124, 1, p. 399-428 30 p.

    Research output: Contribution to journalArticle

  • Scientific Image Tampering Detection Based On Noise Inconsistencies: A Method And Datasets

    Xiang, Z. & Acuna, D. E., Jan 21 2020, In : Arxiv.

    Research output: Contribution to journalArticle

    Press and Media