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Acuna, Daniel E

Associate Professor

Positions

Research Areas research areas

Research

research overview

  • My 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. 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), the Computational Research Integrity (CRI-CONF) conference, and the Computational Research Integrity competitions. 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.

keywords

  • Science of Science, AI for Science, Computational Research Integrity, Bias in AI

Publications

selected publications

Teaching

courses taught

  • CSCI 5434 - Probability for Computer Science
    Primary Instructor - Spring 2023 / Spring 2024
    This course will introduce computer science students to topics in probability and statistics that will be useful in other computer science courses. Basic concepts in probability will be taught from an algorithmic and computational point of view, with examples drawn from computer science. Recommended prerequisite courses of APPM 1360 or MATH 2300 and CSCI 2824 or MATH 2001 or ECEN 2703 (all minimum grade B).
  • CSCI 5622 - Machine Learning
    Primary Instructor - Fall 2023
    Trains students to build computer systems that learn from experience. Includes the three main subfields: supervised learning, reinforcement learning and unsupervised learning. Emphasizes practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines, Q-learning). Covers connections to data mining and statistical modeling. A strong foundation in probability, statistics, multivariate calculus, and linear algebra is highly recommended.
  • CSCI 7000 - Current Topics in Computer Science
    Primary Instructor - Fall 2022 / Spring 2023 / Spring 2024
    Covers research topics of current interest in computer science that do not fall into a standard subarea. May be repeated up to 18 total credit hours.

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