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.
Science of Science, AI for Science, Computational Research Integrity, Bias in AI
CSCI 5434 - Probability for Computer Science
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.
CSCI 7000 - Current Topics in Computer Science
Fall 2022 / Spring 2023
Covers research topics of current interest in computer science that do not fall into a standard subarea. May be repeated up to 8 total credit hours.