Diana Benavides Prado, PhD.

I am currently a Lecturer in Machine Learning and AI at the School of Electronic Engineering and Computer Science (EECS), Queen Mary University of London, where I am also part of the MInDS research group. I have previously held academic and research positions in New Zealand and Colombia. I was a Lecturer in Computer Science (AI & ML) at The University of Auckland (2023–2025), a Senior Research Fellow at the NAO Institute, The University of Auckland (2021–2023), and a Senior Research Fellow at the Centre for Social Data Analytics, Auckland University of Technology (2016–2021). Prior to that, I was Research Lead at the Centre for Informatics Research, University of Los Andes, Colombia (2013–2016), and a Research Assistant at the same Centre (2010-2012).

I earned my Ph.D. in Computer Science from The University of Auckland, New Zealand (2019), after completing a Master’s degree in Systems Engineering and Computing at University of Los Andes, Colombia (2012) and a Bachelor’s degree in Systems Engineering and Computing at San Martin University, Colombia (2010).

I have contributed to numerous projects in data science, machine learning, and artificial intelligence, collaborating with researchers and practitioners in Colombia, Chile, the United States, New Zealand, Australia, and, more recently, the UK. I have extensive experience leading and working with teams of researchers, data scientists, and machine learning engineers on both fundamental and applied projects across a wide range of domains.

My fundamental research interests include transfer learning, continual machine learning, and deep neural networks, with a particular focus on enabling knowledge transfer in increasingly knowledgeable and self-improving continual learning systems. On the applied side, my work focuses on machine learning for social good: I have led the design and implementation of tools that support high-stakes decision-making to safeguard human well-being, and more recently has expanded her interests to applications that advance the protection of animals.

In terms of teaching, I have extensive experience delivering undergraduate and postgraduate courses in machine learning and artificial intelligence. My teaching portfolio includes both theoretical foundations and practical applications, where I integrate research-led content with hands-on projects. I have supervised numerous student research projects and technical staff in areas related to AI and machine learning.

Beyond research and teaching, I am an active contributor to the international AI community. I have served on program committees for top conferences such as AAAI, IJCAI, NeurIPS, ICML, and ICLR, and as a reviewer for journals including Transactions of Machine Learning Research. I am also a moderator for arXiv in the Machine Learning category. In addition, I have served in leadership and editorial roles, including Program Chair for the Australasian Conference on Data Science and Machine Learning (AusDM) and editorial board member for journals such as Data Science and Machine Learning (Springer), Data Science and Engineering (Springer), and Neural Computing and Applications (Springer).

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