Andrea Soltoggio

Dr. Andrea Soltoggio is a Reader in Artificial Intelligence (Associate Professor) and Research Coordinator in the Computer Science Department at Loughborough University's School of Science. He holds a combined BSc and MSc in Computer Science from the Norwegian University of Science and Technology and Politecnico di Milano, and a PhD from the University of Birmingham. His academic journey includes research positions at EPFL, the University of Central Florida, and Bielefeld University where he served as Technical Coordinator for the FP7 European project AMARSi.

His research focuses on creating AI systems that learn continuously like biological brains, with particular emphasis on lifelong learning, collaborative knowledge sharing, and energy-efficient AI. Dr. Soltoggio has made significant contributions to the development of algorithms that enable AI systems to share knowledge without centralized control, as demonstrated in his MOSAIC framework published in 2025 and his Nature Machine Intelligence paper on collective AI systems.

His recent publications reveal a strong trend toward sustainable AI development, with multiple projects focused on reducing the energy footprint of machine learning systems. His work bridges neuroscience and AI, exploring how biological learning mechanisms can inform more adaptive artificial systems. The research spans from theoretical frameworks to practical applications in robotics, healthcare diagnostics, and edge computing.

• Fellow of the Higher Education Academy (FHEA)
• Published in Nature Machine Intelligence (2024)
• Lead researcher on multiple funded projects including FP7 AMARSi
• Regular media contributor on AI topics (BBC, The Conversation)

Dr. Soltoggio actively supervises PhD students and postdoctoral researchers in the Computer Science Department, with current projects focusing on lifelong learning, neuromorphic computing, and sustainable AI. His research group collaborates with Loughborough Business School on digital decarbonization initiatives and maintains connections with international AI laboratories. The team has access to advanced computational resources including Nvidia A100 GPUs and various robotic platforms for experimental validation of their theories.