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Hello and welcome to my homepage!
I’m Diego Fregolent Mendes de Oliveira, an Assistant Professor at the School of Electrical Engineering & Computer Science at the University of North Dakota. My research explores the intersection between people and computing systems, with a focus on how information flows through social networks and how its quality and integrity are shaped in cyberspace. I am particularly interested in ensuring the trustworthiness and reliability of social computing systems.
Another important area of my research is Diversity, Equity, and Inclusion (DEI) across scientific disciplines. By analyzing publication records, funding data, and awards, I work to uncover and understand gender disparities in academia.
My expertise spans complex network systems, including data analysis, machine learning, agent-based modeling, and computational social science. I also have a strong background in chaos and dynamical systems, studying nonlinear dynamics, chaotic attractors, and bifurcation phenomena.
Before joining the University of North Dakota, I held several research positions. I was a Postdoctoral Research Associate at the Statistical and Applied Mathematical Sciences Institute (SAMSI) and the University of North Carolina, where I collaborated with Prof. Peter Mucha, Prof. M. Gregory Forest, and Prof. David Banks. I also served as a Research Scientist at Rensselaer Polytechnic Institute and the Army Research Lab, working with Prof. Boleslaw K. Szymanski and Dr. Kevin S. Chan.
Prior to that, I was a Postdoctoral Fellow at Northwestern University, collaborating with Prof. Luís Amaral and Prof. Brian Uzzi in the Department of Chemical and Biological Engineering and the Northwestern Institute on Complex Systems (NICO).
Here, you can find more about my work on complex networks and time-dependent systems.
Research Interests
Complex Network Systems: Data analysis, data visualization, big data, data mining, machine learning, optimization, social network analysis, sentiment analysis, agent-based modeling, computational social science, information diffusion, modeling dynamical processes on networks, online social media.
Chaos and Dynamical Systems: Nonlinear dynamics, closed and open systems, scaling laws, discrete maps, chaotic dynamics, conservative and dissipative systems, time-dependent systems, Fermi acceleration, billiards, kicked systems, chaotic and periodic attractors, bifurcations, and boundary crises.
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Lightning talk (3 slides) at the NEH ODH Project Directors’ Meeting
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Keynote Presentation at WADL 2018
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Invited presentation to the Web Archiving Team at the Library of Congress
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Presentation of ACM TOIS paper at SIGIR 2019 Read more
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Course lecture slides from asynchronous CS 432/532, first used in Fall 2020
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Project intro presentation for students in the 2022 ODU-CS REU Site on Disinformation Detection and Analytics
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Workshop presentation for students in the 2022 ODU-CS REU Site on Disinformation Detection and Analytics
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Workshop presentation for students in the 2022 ODU-CS REU Site on Disinformation Detection and Analytics
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Telling the story of Hurricane Katrina using mementos of CNN.com from the Wayback Machine
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Invited talk as part of the 2022 NLM History Talks series Read more
Catalog Description: Provides an overview of the World Wide Web and associated decentralized information structures, focusing mainly on the computing aspects of the Web: how it works, how it is used, and how it can be analyzed. Students will examine a number of topics including: web architecture, web characterization and analysis, web archiving, Web 2.0, social networks, collective intelligence, search engines, web mining, information diffusion on the web, and the Semantic Web. Prerequisites: A grade of C or better in CS 361 and CS 330. Read more
Catalog Description: This course covers the theory and application of data visualization. This includes issues in data cleaning to prepare data for visualization, theory behind mapping data to appropriate visual representations, introduction to visual analytics, and tools used for data analysis and visualization. Modern visualization software and tools will be used to analyze and visualize real-world datasets to reinforce the concepts covered in the course. Read more
Catalog Description: This course covers the theory and application of information visualization and of visual analytics, the science of combining interactive visual interfaces and information visualization techniques with automatic algorithms to support analytical reasoning through human-computer interaction. Research on visual perception, cognition, interactive visual interfaces, and visual analytics will be covered. Practical techniques for the display of complex multivariate data will be addressed. Course projects will require the development of interactive web-based interfaces to analyze and visualize real-world datasets. Prerequisite: CS 625 (Data Visualization) Read more
Catalog Description: Introduction to research methods in computer science. Topics include academic publishing, academic writing, literature reviews, responsible conduct of research, and presenting research results. Research faculty will present overviews of their research and how research is conducted in their labs. Read more
Catalog Description: This course covers the theory and application of information visualization and of visual analytics, the science of combining interactive visual interfaces and information visualization techniques with automatic algorithms to support analytical reasoning through human-computer interaction. Research on visual perception, cognition, interactive visual interfaces, and visual analytics will be covered. Practical techniques for the display of complex multivariate data will be addressed. Course projects will require the development of interactive web-based interfaces to analyze and visualize real-world datasets. Prerequisite: CS 625 (Data Visualization) Read more