Daniel
Acuña.

Associate Professor of Computer Science

I study the production and evaluation of scientific knowledge using machine learning and computational social science.

Science of Science and
Computational Discovery Lab

Research.

My research examines peer review, research integrity, scientific careers, and collaboration. I use scholarly data, statistical models, and machine learning to study these processes.

I lead the Science of Science and Computational Discovery Lab at CU Boulder. Our work includes empirical studies of scientific institutions and computational methods for analyzing scientific text, images, and citation networks.

Research overview

Research
areas.

Research integrity

Methods for detecting figure reuse, characterizing questionable journals, and evaluating the behavior of AI systems in research settings.

Science of science

Studying how funding, mentorship, collaboration, and recognition shape scientific work—and who gets the opportunity to contribute.

Read the research Dissecting the gender divide: authorship and acknowledgment in scientific publicationsScientometrics · 2026

AI for discovery

Building systems that help researchers find relevant work, identify unanswered questions, and evaluate scientific manuscripts.

Read the research REM-CTX: Automated Peer Review via Reinforcement Learning with Auxiliary ContextarXiv · 2026 · Preprint

Recent publications.

All publications

2026Journal article

Dissecting the gender divide: authorship and acknowledgment in scientific publications

Keigo Kusumegi; Daniel E. Acuna; Yukie Sano

Scientometrics

This study compares authorship and acknowledgment to examine how scientific contributions receive credit. Using PLOS articles and linked researcher records, it investigates differences associated with gender, contribution type, and citation standing.

Science of science

BibTeX

@article{kusumegi2026dissecting,
  title = {Dissecting the gender divide: authorship and acknowledgment in scientific publications},
  author = {Kusumegi, Keigo and Acuna, Daniel E. and Sano, Yukie},
  year = {2026},
  publication_date = {2026-08-08},
  journal = {Scientometrics},
  doi = {10.1007/s11192-026-05712-z},
  url = {https://doi.org/10.1007/s11192-026-05712-z}
}

2026Preprint / working paper

SciIntBench: Measuring LLM Compliance with Research Integrity Norms Under Adversarial Framing

Almene De Meran Meguimtsop; Maria Leonor Pacheco; Daniel E. Acuna

arXiv preprint arXiv:2605.29468

SciIntBench evaluates how language models respond to scientific requests framed as explicit misconduct, covert misconduct, or legitimate work. It measures both refusal of problematic requests and helpfulness on benign ones.

Research integrity Peer review & discovery

BibTeX

@article{meguimtsop2026sciintbench,
  title = {SciIntBench: Measuring LLM Compliance with Research Integrity Norms Under Adversarial Framing},
  author = {Meguimtsop, Almene De Meran and Pacheco, Maria Leonor and Acuna, Daniel E.},
  year = {2026},
  publication_date = {2026-05-28},
  journal = {arXiv preprint arXiv:2605.29468},
  doi = {10.48550/arXiv.2605.29468},
  url = {https://arxiv.org/abs/2605.29468}
}

2025Journal article

Estimating the predictability of questionable open-access journals

Han Zhuang; Lizhen Liang; Daniel E. Acuna

Science Advances, 11(35), eadt2792

This study evaluates whether journal websites and publication metadata can support large-scale screening for questionable open-access journals. It treats automated predictions as a way to focus expert investigation.

Research integrity Science of science

BibTeX

@article{zhuang2025estimating,
  title = {Estimating the predictability of questionable open-access journals},
  author = {Zhuang, Han and Liang, Lizhen and Acuna, Daniel E.},
  year = {2025},
  publication_date = {2025-08-27},
  journal = {Science Advances},
  volume = {11},
  number = {35},
  pages = {eadt2792},
  doi = {10.1126/sciadv.adt2792},
  url = {https://doi.org/10.1126/sciadv.adt2792}
}

2025Journal article

Predicting the longevity of resources shared in scientific publications

Daniel E. Acuna; Jian Jian; Tong Zeng; Lizhen Liang; Han Zhuang

Humanities and Social Sciences Communications, 12(1), 698

Code and data links can stop working long after a paper is published. This study examines which features of a resource, its host, and its associated publication help explain and predict its availability over time.

Research integrity Science of science

BibTeX

@article{acuna2022predicting,
  title = {Predicting the longevity of resources shared in scientific publications},
  author = {Acuna, Daniel E. and Jian, Jian and Zeng, Tong and Liang, Lizhen and Zhuang, Han},
  year = {2025},
  publication_date = {2025-05-22},
  journal = {Humanities and Social Sciences Communications},
  volume = {12},
  number = {1},
  pages = {698},
  doi = {10.1057/s41599-025-04716-z},
  url = {https://doi.org/10.1057/s41599-025-04716-z}
}

Teaching.

All teaching

Fall 2026

Machine Learning

University of Colorado Boulder

Coursera

Machine Learning: Theory and Hands-on Practice with Python

Three-course specialization · Supervised learning, unsupervised learning, and deep learning

Academic
background.

Full curriculum vitae

Before joining CU Boulder, I was a faculty member in Syracuse University’s School of Information Studies. I earned my Ph.D. in Computer Science at the University of Minnesota and was a postdoctoral researcher at Northwestern University and the Rehabilitation Institute of Chicago.

I was born in Santiago, Chile, and studied at the University of Santiago. I also contribute to the Science of Science Summer School and the Computational Research Integrity Conference.

DanielAcuña.

Research.

Researchareas.

Research integrity

Science of science

AI for discovery

Recent publications.

Dissecting the gender divide: authorship and acknowledgment in scientific publications

SciIntBench: Measuring LLM Compliance with Research Integrity Norms Under Adversarial Framing

Estimating the predictability of questionable open-access journals

Predicting the longevity of resources shared in scientific publications

Teaching.

Machine Learning

Machine Learning: Theory and Hands-on Practice with Python

Academicbackground.

Daniel
Acuña.

Research
areas.

Academic
background.