Eleonora Giunchiglia is an Assistant Professor at Imperial College London in the Department of Electrical and Electronic Engineering and a member of Imperial-X (I-X). She completed her DPhil at the University of Oxford in 2022, followed by a postdoctoral position at TU Wien before joining Imperial in 2024. Her research lies at the intersection of machine learning and formal reasoning, with a focus on neuro-symbolic AI — developing methods that integrate logical constraints and background knowledge into neural networks to make them safer, more reliable, and trustworthy, with applications ranging from constrained generative models to theorem proving and verifiable LLM reasoning.

Luca Andolfi is a Research Fellow at Imperial College London working at the intersection of formal logic, knowledge representation, and machine learning. His research focuses on neuro-symbolic AI, with particular interest in methods for learning semantically meaningful concepts that support intermediate reasoning processes within neuro-symbolic architectures. By developing approaches that make reasoning more interpretable and robust, his work contributes to the design of transparent and trustworthy AI systems.

Mihaela Cătălina Stoian is a postdoctoral researcher at Imperial College London working on constraining LLMs for theorem proving. She recently completed her PhD at the University of Oxford, where her work focused on developing neuro-symbolic methods that integrate background knowledge constraints into neural networks for real-world applications. Her contributions to the field have been recognised with several awards, including the Oxford PhD Runner-up Prize awarded by G-Research. Previously, she worked on detecting reflective symmetries in 3D models at Five AI and completed her Master’s in speech-to-text machine translation at the University of Edinburgh.

Joshua is a first-year PhD student at Imperial College London and a visiting researcher at the University of Edinburgh and Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Prior to his PhD, he published several papers at top-tier conferences, including ACL, EMNLP, NAACL, and ICLR. His research interests lie within LLM Reasoning, with a particular emphasis on theorem proving to improve the faithfulness and verifiability of LLM outputs. He is currently working at the intersection of diffusion models and theorem proving.

Lohith is a first-year PhD student in the DUCK Lab at Imperial College London, supervised by Dr. Eleonora Giunchiglia. He holds an MEng in Information & Computer Engineering from the University of Cambridge and, prior to his PhD studies, worked on low-latency speech-language models for speech synthesis at a venture-backed startup. His current research focuses on the application of Neurosymbolic AI in Financial Markets, including constrained reinforcement learning for dynamic portfolio construction and efficient constraint enforcement over categorical variables with applications in foundation models for automated trading.

Dylan is a first-year Chemistry PhD student funded by the AIChemy Hub for AI in Chemistry and co-supervised by Prof. Kim Jelfs, Dr. Alex Ganose, and Dr. Eleonora Giunchiglia. His research focuses on neurosymbolic AI as a means of embedding prior chemistry knowledge into generative models for materials discovery, with the aim of improving the synthetic accessibility of generated structures. He holds a first-class MChem from the University of Oxford, where his Masters research, supervised by Prof. Fernanda Duarte, centred on the computational design of serine beta-lactamase inhibitors to combat antimicrobial resistance.