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
CSCA 5622 - Introduction to Machine Learning: Supervised Learning
Primary Instructor
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Spring 2026
This course introduces various supervised ML algorithms and prediction tasks applied to different data. Specific topics include linear and logistic regression, KNN, Decision trees, ensemble methods such as Random Forest and Boosting, and kernel methods such as SVM. Formerly offered as a special topics course. Same as DTSA 5509.
CSCA 5632 - Unsupervised Algorithms in Machine Learning
Primary Instructor
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Spring 2026
Students will learn selected unsupervised learning methods for dimensionality reduction, clustering, finding latent features, and application cases such as recommender systems with hands-on examples of product recommendation algorithms. Formerly offered as a special topics course. Same as DTSA 5510.
CSCA 5642 - Introduction to Deep Learning
Primary Instructor
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Spring 2026
Course will cover the basics of deep learning, such as multilayer perceptron, convolutional neural network, recurrent neural network, how to build and train neural network models, optimization methods, and application examples. Formerly offered as a special topics course. Same as DTSA 5511.
CSCI 5434 - Probability for Computer Science
Primary Instructor
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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
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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
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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.
DTSA 5509 - Introduction to Machine Learning: Supervised Learning
Primary Instructor
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Spring 2026
This course introduces various supervised ML algorithms and prediction tasks applied to different data. Specific topics include linear and logistic regression, KNN, Decision trees, ensemble methods such as Random Forest and Boosting, and kernel methods such as SVM. Formerly offered as a special topics course. Same as CSCA 5622.
DTSA 5510 - Unsupervised Algorithms in Machine Learning
Primary Instructor
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Spring 2026
Students will learn selected unsupervised learning methods for dimensionality reduction, clustering, finding latent features, and application cases such as recommender systems with hands-on examples of product recommendation algorithms. Formerly offered as a special topics course. Same as CSCA 5632.
DTSA 5511 - Introduction to Deep Learning
Primary Instructor
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Spring 2026
Course will cover the basics of deep learning, such as multilayer perceptron, convolutional neural network, recurrent neural network, how to build and train neural network models, optimization methods, and application examples. Formerly offered as a special topics course. Same as CSCA 5642.
