Diana Benavides-Prado, PhD.

About Me

I am a Lecturer (Assistant Professor) in Machine Learning and AI at the School of Electronic Engineering and Computer Science (EECS), Queen Mary University of London, where I am a member of the MInDS research group and the Centre for Multimodal AI.

My career spans over 15 years of academic and research leadership across the UK, New Zealand, and Colombia. I earned my Ph.D. in Computer Science from the University of Auckland (2019) and have previously held faculty and senior research positions at the University of Auckland (UoA) and Auckland University of Technology (AUT).

Research Vision

My research is primarily situated in foundational Machine Learning, where I investigate the mechanisms that allow artificial systems to learn, retain, and transfer knowledge over time.

My core research focuses on Continual Learning, Transfer Learning, and Deep Neural Networks. I am specifically interested in the algorithmic challenges of self-improving systems—developing models that can autonomously enhance their capabilities through continuous interaction with data without the catastrophic forgetting of prior knowledge.

While my work is rooted in fundamental theory, I apply these insights to solve critical real-world problems. I have a long-standing commitment to AI for Social Good, leading the design and deployment of machine learning tools that support high-stakes decision-making. Historically, this has involved safeguarding human well-being through child maltreatment screening (in the US, Chile, and NZ).

Academic Leadership & Community

I am an active contributor to the international AI research community. I serve as an arXiv Moderator (Machine Learning category) and on the editorial boards of Data Science and Engineering and Neural Computing and Applications (Springer). I am a frequent Program Committee member/Senior Program Committee Member for top-tier venues including NeurIPS, ICML, ICLR, AAAI, and IJCAI.

With a deep commitment to pedagogical research, I deliver undergraduate and postgraduate AI courses that bridge theoretical foundations with hands-on, research-led practice.

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