As part of the lab activities, students attend focused lectures primarily delivered by Prof. Moshe Guy and visitors. The lectures offer a valuable combination of academic depth and practical relevance, helping students connect classroom knowledge with real-world applications:
Speaker: Prof. Moshe Guy
Image denoising, a fundamental task in computer vision and image processing, has undergone a dramatic transformation with the rise of deep learning. This lecture explores the new era of image denoising, highlighting the profound impact of deep neural networks and examining what lies beyond current state-of-the-art techniques.
We will trace the evolution from classical algorithms to cutting-edge deep learning models, including convolutional neural networks (CNNs), autoencoders, and diffusion models. The lecture will also cover emerging trends that go beyond standard supervised learning, such as self-supervised methods, real-noise datasets, and physics-informed approaches.
Key topics include:
See talk video
Speaker: Prof. Eitan Zipelman
Quantum entanglement holds great promise for enabling ultra-secure communication. However, in real-world scenarios, entanglement is often imperfect, subject to loss, noise, and potential interception. This lecture explores the challenges and strategies for achieving secure communication when entanglement assistance is unreliable.
We will examine how quantum communication protocols can be adapted to function securely even in the presence of degraded or compromised entanglement. Topics include the impact of entanglement loss, eavesdropping risks, and error mitigation techniques, as well as recent theoretical and practical advances in quantum key distribution (QKD) under such constraints.
This talk is aimed at researchers and students in quantum information, cybersecurity, and communication theory who are interested in the intersection of quantum physics and secure communication under non-ideal conditions.
Speaker: Prof. Dana Raphaeli-Chen
Deep learning has revolutionized numerous fields, from computer vision and natural language processing to robotics and biomedical analysis. This lecture provides a comprehensive introduction to the design principles and methodologies behind modern deep learning architectures.
We will explore how neural network models are structured, optimized, and adapted to solve complex tasks. Topics include convolutional and recurrent networks, attention mechanisms, transformer models, architectural innovations such as ResNet and UNet, and strategies for tailoring architectures to specific applications.
Speaker: Prof. Moshe Guy
In an era where classical coordination strategies in communication networks are approaching their fundamental limits, quantum technologies offer promising avenues to overcome existing constraints. This lecture explores the emerging field of quantum coordination in multi-user networks, where quantum entanglement and quantum information protocols enable novel forms of collaboration and data exchange among multiple users.
Participants will gain insights into:
The lecture is intended for researchers, engineers, and students interested in quantum information science, communication networks, and distributed systems.
