Sussex AI Day 2026
Chichester 1, Brighton, BN1 9RH
Monday 19th January 2026
Let's share and celebrate the exciting Data Science and Artificial Intelligence related research that is being conducted across the University.
We’re looking forward to hosting our annual research symposium - Sussex AI Day on Monday 19th January 2026.
Please register using your Sussex email address.
We are excited to announce our keynote speaker is prof. Eleni Vasilaki - Head of the Machine Learning research group, Member of the Complex Systems Modelling research group at the University of Sheffield.
Title:
Tackling Reliability and Scalability in Neuromorphic Computing via Noise-aware Learning
Neuromorphic computing has evolved into a broad label covering technologies ranging from biologically inspired systems to machine-learning-style architectures. Computing with materials is often promoted as a potentially energy-efficient alternative to conventional hardware, but some of these claims lack robust empirical support. In this talk I will outline key challenges in the field, with a focus on variability and scalability. I will present concrete examples from my recent work showing how noise-aware learning in systems with stochastic, device-level behaviour can help mitigate variability and improve robustness. These results suggest that while variability poses real constraints, it can be addressed through appropriate learning strategies.
Please note that photographs and footage will be taken throughout this event. These will be used by the University for marketing and publicity in our publications, on our website and in social media or in any third party publication. Please contact the event organiser ([email protected]) if you have any concerns or if you wish to be exempted from this activity.
We’re looking forward to hosting our annual research symposium - Sussex AI Day on Monday 19th January 2026.
Please register using your Sussex email address.
We are excited to announce our keynote speaker is prof. Eleni Vasilaki - Head of the Machine Learning research group, Member of the Complex Systems Modelling research group at the University of Sheffield.
Title:
Tackling Reliability and Scalability in Neuromorphic Computing via Noise-aware Learning
Neuromorphic computing has evolved into a broad label covering technologies ranging from biologically inspired systems to machine-learning-style architectures. Computing with materials is often promoted as a potentially energy-efficient alternative to conventional hardware, but some of these claims lack robust empirical support. In this talk I will outline key challenges in the field, with a focus on variability and scalability. I will present concrete examples from my recent work showing how noise-aware learning in systems with stochastic, device-level behaviour can help mitigate variability and improve robustness. These results suggest that while variability poses real constraints, it can be addressed through appropriate learning strategies.
Please note that photographs and footage will be taken throughout this event. These will be used by the University for marketing and publicity in our publications, on our website and in social media or in any third party publication. Please contact the event organiser ([email protected]) if you have any concerns or if you wish to be exempted from this activity.
