Osvaldo Simeone

Osvaldo Simeone is an information engineer whose research connects machine learning with wireless communications and information theory. After an MSc in 2001 and a PhD in 2005 from Politecnico di Milano, he spent 2006 to 2017 at the New Jersey Institute of Technology, where he became a full professor and an IEEE Fellow in 2016, and 2017 to 2025 at King's College London. His work there included the 2017 monograph A Brief Introduction to Machine Learning for Engineers, a 2019 introduction to probabilistic spiking neural networks, and the 2022 Cambridge textbook Machine Learning for Engineers; in 2025 he won a European Research Council Advanced Grant for CONTRACT, a project on calibrating AI models in wireless systems. He is a Professor of Information Engineering at Northeastern University London, where he co-directs the Intelligent Networked Systems Institute, and he published Classical and Quantum Information Theory in 2026.

Now at: Northeastern University London
Born: 1977
Works: 729
Citations: 20,193
h-index: 59

Years link to their sources. Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

2001–2005

Degrees from Politecnico di Milano

He earns an MSc with honours in 2001 and a PhD in 2005, both in information engineering, at Politecnico di Milano in Italy.
2006–2017

Professor at NJIT

He joins the Electrical and Computer Engineering Department at the New Jersey Institute of Technology in 2006. In 2016, NJIT promotes him to full professor, and he is named a Fellow of the IEEE.
2017–2025

Years at King's College London

He is with the Department of Engineering at King's College London from 2017 to 2025. In 2022, he receives an Open Fellowship from the EPSRC and chairs the Signal Processing for Communications and Networking Technical Committee of the IEEE Signal Processing Society.
2017–2018

A machine learning monograph

He posts the monograph A Brief Introduction to Machine Learning for Engineers on arXiv in September 2017 and revises it in May 2018. It introduces key concepts, algorithms and theoretical results in machine learning, with a focus on probabilistic models for supervised and unsupervised learning.
2019

Probabilistic spiking neural networks

In 2019, he coauthors an introduction to probabilistic spiking neural networks with Hyeryung Jang, Brian Gardner and André Grüning. The article derives supervised and unsupervised learning rules for networked spiking neurons from first principles via variational inference.
2022

A Cambridge textbook for engineers

Cambridge University Press publishes his textbook Machine Learning for Engineers in 2022. The book derives modern methods, such as generative adversarial networks, from first principles and ties machine learning to estimation, detection, information theory and optimization.
2025

An ERC Advanced Grant

In June 2025, King's College London announces his European Research Council Advanced Grant for CONTRACT. The project proposes a new statistical foundation for reliably calibrating and monitoring AI models in wireless systems.
As of October 2026

Northeastern University London

He is Professor of Information Engineering at Northeastern University London, where he co-directs the Intelligent Networked Systems Institute (INSI). With Bipin Rajendran he leads the institute's London hub, whose inaugural event takes place in January 2026 and whose two missions are developing reliable AI and emerging computing. In February 2026, Northeastern's College of Engineering announces his textbook Classical and Quantum Information Theory, published by Cambridge University Press.