Alessandra Russo

Alessandra Russo is an Imperial College professor whose research develops symbolic and neuro-symbolic AI systems for interpretable learning. She earned a computer science degree at Bari in 1990, joined Imperial as a PhD student in 1991, became a Research Associate in 1995, and completed her PhD in 1996. She leads SPIKE, founded in 2010, and became Professor of Applied Computational Logic in 2016. From 2018, her research has brought machine learning together with probabilistic inference and symbolic learning; in 2020, she coauthored a paper on ILASP and received Imperial's President Award for Research Supervision. In 2025, she chaired an Imperial panel on responsible AI and coauthored papers on interpretable policies and neural DNF models; as of October 2026, she is Director of the Thomson Reuters–Imperial Frontier AI Research Lab and Convening Co-Director of Imperial's School of Convergence Science.

Now at: Imperial College London and Thomson Reuters–Imperial Frontier AI Research Lab
Works: 396
Citations: 7,259
h-index: 45
Badges: Loyal

Publication numbers from OpenAlex, as of 18 October 2026.

Timeline

1990

A computer science degree

She earns a Laurea in Computer Science from the University of Bari in 1990.
1991–1996

From student to researcher

She joins Imperial as a PhD student in 1991, becomes a Research Associate in 1995, and completes her PhD in 1996.
2010–2016

SPIKE and applied logic

The SPIKE research group is founded in 2010 and led by Russo; she is promoted to Professor of Applied Computational Logic in 2016.
2018

Neuro-symbolic methods

From 2018, she develops neuro-symbolic systems that combine machine learning with probabilistic inference and symbolic learning to support interpretable, explainable models.
2020

ILASP and a research award

In 2020, she coauthors an overview of ILASP, a system that learns Answer Set Programs. She also receives Imperial's President Award for Research Supervision in 2020.
2025

Interpretable policy models

In June 2025, she chairs Imperial's alumni panel on responsible AI as Head of the Department of Computing and Co-Director of its School for Convergence Science in Human and Artificial Intelligence. At AAMAS 2025, she coauthors Neural DNF-MT, a neuro-symbolic method that translates trained models into interpretable logic programs. In 2025, she coauthors work that produces compact, interpretable logical representations from neural DNF models.
As of October 2026

Directing a Frontier AI lab

In October 2026, Russo is Professor in Applied Computational Logic, Director of the Thomson Reuters–Imperial Frontier AI Research Lab, and Convening Co-Director of Imperial's School of Convergence Science.