About Me

Hello and welcome to my homepage!

My name is Diego Fregolent Mendes de Oliveira is an Assistant Professor at the School of Electrical Engineering & Computer Science, University of North Dakota. He is interested in problems originating from the interplay between people and computing systems, the determinants of information quality in cyberspace, and how information propagates across social networks, with applications to the integrity of information in cyberspace and the trustworthiness and reliability of social computing systems. Another direction of his research focuses on studying Diversity, Equity, and Inclusion (DEI) in different fields of science. By leveraging publication data along with grants and prize information, he seeks to uncover gender disparities in academia. His expertise extends to complex network systems, involving data analysis, machine learning, agent-based modeling, and computational social science, as well as chaos and dynamical systems, with a focus on nonlinear dynamics, chaotic attractors, and bifurcations.

Before joining the University of Maryland, I held several positions that includes a Postdoctoral Research Associate positing at The Statistical and Applied Mathematical Sciences Institute and the University North Carolina woring with Prof. Dr. Peter Mucha, Prof. Dr. M. Gregory Forest and Prof. David Banks. I was also a Research Scientist at the Rensselaer Polytechnic Institute and the Army Research Lab working in collaboration with Prof. Dr. Boleslaw K. Szymanski and Dr. Kevin S. Chan.

Before joining RPI/ARL, I was a Postdoctoral Fellow at Northwestern University working in collaboration with Prof. Luis Amaral and Prof. Brian Uzzi from the Department of Chemical and Biological Engineering in the McCormick School of Engineering and Applied Science and the Northwestern Institute on Complex Systems (NICO).

Here you will find some information about my work with complex networks and time-dependent systems.

Research Interests:

Complex Networks 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 of dynamical processes on networks, online social media.

Chaos and Dynamical Systems, acting mainly on the following themes: nonlinear dynamics, dynamical systems, closed and open systems, scaling law, discrete maps, chaotic dynamics, conservative and dissipative systems, time-dependent systems, Fermi acceleration, billiards, kicked systems, chaotic and periodic attractors, bifurcations, boundary crisis.