Wilson Silva is an Assistant Professor in the Departments of Information and Computing Sciences and Biology at Utrecht University, as well as a Guest AI Researcher in the Department of Radiology at the Netherlands Cancer Institute. His research focuses on trustworthy artificial intelligence for life sciences, with particular emphasis on explainable AI, privacy-preserving machine learning, and enhancing generalizability in cancer research.
Before moving to the Netherlands, Wilson completed his PhD at the University of Porto/INESC TEC, where he conducted research within the Visual Computing and Machine Intelligence group under the guidance of Prof. Jaime Cardoso. During his PhD, he also spent time as a visiting researcher in Prof. Mauricio Reyes’ lab at the University of Bern in Switzerland. Wilson holds a master’s degree in Electrical and Computer Engineering from the University of Porto.

TBA

Yongshuo Zong is a final-year PhD student at the University of Edinburgh where he works with Prof. Timothy Hospedales. His research focus on multimodal learning, especially large vision-language models, with an emphasis on areas such as safety, robustness, in-context learning, and multi-image understanding. He also works in the area of AI for healthcare where he develops MEDFAIR, one of the most popular benchmarks for medical imaging fairness.

Large language models (LLMs) have become integral to daily life, powering numerous applications. Building on the foundation of LLMs, large vision-language models (VLMs) have been developed to seamlessly integrate visual and textual data, enabling advanced multimodal interactions with humans. In this talk, I will first provide an overview of the background of large language models and multimodal learning. I will then introduce key research challenges in advancing LLMs and VLMs, focusing on topics such as in-context learning, multi-image understanding, and long-context processing. Finally, I will discuss the challenges of deploying large vision-language models safely, discussing potential alignment strategies to ensure reliability and ethical use. This talk aims to give the audience an overview of the foundations, advancements, and current challenges in vision-language models, highlighting the latest research driving this field forward.

Dr. Fadi Boutros is a scientific researcher at the Fraunhofer IGD and a principal investigator at the National Research Center for Applied Cybersecurity ATHENE, Germany. Fadi received his Ph.D. in computer science from TU Darmstadt (2022) and a master's degree in "Distributed Software Systems" from TU Darmstadt (2019). Also, he is participating in the Software Campus program, a management program of the German Federal Ministry of Education and Research (BMBF). He authored and co-authored several conference and journal papers. His main research interests lie in the fields of biometrics, machine learning, and efficient deep learning. For his scientific work, he received several awards, including the CAST-Förderpreis 2019 award, the IJCB 2022 Qualcomm Audience Choice Award, and the 2022 EAB Biometrics Industry Award from the European Association for Biometrics (EAB) for his Ph.D. dissertation.

In this lecture on knowledge distillation (KD), we delve into its critical role in enhancing the performance of compact machine learning models, using face recognition as a prime example application. KD is a model compression technique that involves transferring knowledge from a large, complex teacher model to a smaller, more efficient student model. In the context of face recognition, KD has emerged as an effective approach to enhance the performance of lightweight models, ensuring they perform well in resource-constrained environments. This lecture will explore the principles of knowledge distillation and its application to face recognition tasks. We will discuss various distillation strategies, including traditional soft-label techniques and more advanced methods such as feature-based distillation. The lecture will also address the challenges specific to face recognition, such as handling large-scale datasets, managing intra-class variability and performing KD within a privacy friendly framework. The lecture further addresses the data requirements for effective distillation, highlighting the importance of high-quality labeled datasets and discussing techniques to simulate diverse and representative data distributions. Real-world examples from face recognition systems, like those used in security and authentication, will demonstrate how knowledge distillation bridges the gap between model efficiency and accuracy, enabling scalable deployment. Finally, the lecture will provide insights into the future of KD in face recognition, including integration with deep learning architectures and the potential for improving real-time performance on edge devices.

Dr Giulia holds a Bachelor's Degree in Biomedical Engineering, followed by a Master's Degree in Neuroengineering with honours from the University of Genoa and a Master's Thesis in Computer Vision on neuromorphic visual algorithms at King's College London. After a year as a software engineer for computer vision at the Italian Institute of Technology (IIT), Giulia completed her PhD at The University of Manchester, receiving the President's Doctoral Scholar Award, as a result of her studies focused on bioinspired algorithms for visual attention, event-driven sensing and neuromorphic algorithms.
After her work as a postdoctoral researcher at the IIT, Dr Giulia has recently been awarded the Marie Skłodowska-Curie Postdoctoral Fellowship, to fund her research project at the Faculty of Electrical Engineering, Czech Technical University in Prague. The ENDEAVOR project, an Event-driven Active Vision for Object Perception, focuses on leveraging end-to-end spiking-based architecture for online robotic applications, revolutionizing object recognition techniques.
Her ongoing research revolves around neuromorphic algorithms and computing, to demonstrate their suitability for real-time robotics applications characterised by minimal power consumption and low latency.

TBA

Ph.D. Luís Rosado is a Senior Scientist at Fraunhofer Portugal AICOS. He received a Ph.D. in Biomedical Engineering (2018) from the Faculty of Engineering of the University of Porto, and a M.Sc. in Biomedical Engineering (2009) from Instituto Superior Técnico of the Technical University of Lisbon. His research focuses on applying computer vision and machine learning to various industry sectors, being currently engaged in developing solutions for health, viticulture, and the automotive industry. Current areas of interest include Generative AI, Data-centric ML, Explainable AI, and Industrial Visual Inspection.

This session will allow a case study demonstration of controllable strategies for generating realistic and representative synthetic retinal fundus images with diffusion models. With a hands-on component focused on enhancing data auditing of real and synthetic retinal fundus image datasets using the recently open-sourced pyMDMA library. This will motivate a discussion about the practical implications of synthetic health data, including its benefits, risks, and ethical considerations.