About the project
Objective
This research aims to create a strong framework for Online Continual Learning (OCL) that will help AI models learn gradually from streaming data. It will tackle the catastrophic forgetting phenomenon and maintain long-term flexibility, with a focus on high-stakes, changing environments where models need to adjust quickly without retraining. This is especially important in healthcare and other real-world areas where data changes constantly and unpredictably.
Background
Traditional AI models, especially in Deep Learning (DL), are usually trained on fixed datasets and used as set systems, making them fragile in changing situations. When new tasks or data come up, these models either need expensive retraining or risk forgetting what they previously learned. This inflexibility limits their growth and usefulness in real-world scenarios
Online continual learning changes this by allowing models to update continuously as new data comes in, much like how humans learn over time. However, this approach brings complex challenges, such as finding a balance between stability (keeping old knowledge) and plasticity (adjusting to new information).
Finding this balance in high-dimensional areas is particularly hard, as updates need to happen in real-time while following strict memory and processing limits. Take healthcare as an example: patient profiles and medical knowledge change every day. AI systems must keep up without sacrificing safety or accuracy. The implications go beyond healthcare. They also include autonomous systems that must navigate changing environments and adaptive vision models that need to stay relevant amid ever-changing data streams.
About the Digital Futures Postdoc Fellow
Romeo Lanzino is a researcher in Artificial Intelligence. He focuses on Computer Vision, Continual Learning, and , bioinformatics . He is currently a postdoctoral researcher at KTH Royal Institute of Technology, where he’s researching adaptive AI systems that can learn continuously from changing data streams. He earned a PhD in Artificial Intelligence at Sapienza University of Rome (Italy) under the Italian National PhD AI program. His doctoral research looked closely at how Deep Learning is used for analyzing physiological signals, questioning common beliefs about how well neural networks perform in Electroencephalography studies. He has a background in computer science from Sapienza, where he received both his BSc and MSc with honors. Romeo is active in the academic community as a reviewer for top venues like ICCV, NeurIPS, and IEEE Transactions on Multimedia, and he also co-organizes related workshops at major conferences.
Main supervisor
Atsuko Maki, KTH
Co-supervisor
Josephine Sullivan, KTH
