Richard Oliveira

Hello! My name is Richard, and I'm a Marie Skłodowska-Curie doctoral fellow and first year Phd candidate at Politecnico di Milano, Italy, in the department of Electronics, Information Technology, and Bioengineering (DEIB) and the department of Mathematics (DMAT), under the supervision of Dr. Stefano Savazzi and Prof. Monica Nicoli. Furthermore, I am also affiliated with the Institute of Electronics, Computer and Telecommunication Engineering (IEIIT) at the Consiglio Nazionale delle Ricerche.

My background can be broadly defined as that of a Mathematical Engineer with a focus on developing mathematical models for how information is acquired, communicated and processed in engineered systems, with applications in control, computation and Machine Learning. My research explores the mathematical underpinnings of machine learning through the lens of optimization theory, focusing on how algorithms learn in distributed settings. I'm particularly drawn to understanding regularization in learning dynamics, especially how it emerges implicitly when agents collaborate over graphs. A central question in my work is how network structure, consensus processes, and the spectral properties of communication graphs shape the stability, convergence, and generalization of gradient-based methods.

I received my MSc. in Electrical Engineering, Signal and Image Processing and a BSc. in Electrical Engineering, Machine Learning and Controls from the Electrical and Computer Engineering Department at the University of California, San Diego, La Jolla, USA. During my MSc. studies I have worked on the theory of Discrete Pearson-Rayleigh Random Walks under the supervision of Prof. Massimo Franceschetti.

I have a google scholar citation profile.