Alan Said

Based in Gothenburg, Sweden

Bio:

Alan Said is an Associate Professor of Computer Science at the Department of Applied Information Technology, University of Gothenburg, Sweden. Said leads research in human-centered artificial intelligence, recommender systems, user modeling, and the sustainability of AI systems. His work includes theoretical advancements in machine learning, practical applications in health and personalized technology, and interdisciplinary research on fairness, transparency, and the environmental impact of AI.

Said received his Ph.D. in Computer Science from Technische Universität Berlin, Germany, focusing on the evaluation of recommender systems. He has held roles in academia and industry, including Marie Curie Postdoctoral Fellowships at Centrum Wiskunde & Informatica (CWI) and Delft University of Technology in the Netherlands, and positions in applied machine learning in industry.

Said has authored over 100 peer-reviewed publications in venues such as ACM RecSys, CSCW, IUI, UMAP, TORS, and TIST. His contributions have been recognized through awards including the Springer Best Paper Award at UMAP.

He collaborates internationally with academic institutions and industry partners. His service in ACM includes General Co-chair for RecSys 2019 and UMAP 2026, Chair of the ACM RecSys Steering Committee, and editorial roles for ACM Transactions on Recommender Systems and ACM Transactions on Intelligent Systems and Technology.

url: www.alansaid.com

Available Lectures

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Designing Ethical and Responsible Human-Centered AI
Artificial Intelligence increasingly shapes various aspects of daily life, from healthcare to social media interactions. Ethical design and responsible deployment of AI require a human-centered...
From Netnews to Neural Nets: A History of Recommender Systems
Recommender systems have evolved dramatically over the past few decades, transitioning from early Netnews experiments to today’s neural network-driven and generative models. This talk...
Recommender Systems at the Cost of the Planet
Recommender systems shape what billions of people see, buy, and believe — but at what cost? As these systems grow in scale and influence, so too does their environmental and societal...
Recommender Systems for a Fair and Sustainable Future
Recommender systems have become central to digital decision-making, shaping what we see, buy, and engage with. But as their influence grows, so too do the societal and environmental consequences...
Transparent and Accountable Recommender Systems
Recommender systems increasingly mediate interactions across digital platforms, from streaming services to e-commerce. As their use expands, ensuring these systems are transparent and accountable...

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