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Submodular maximization under various constraints is a fundamental problem studied continuously, in both computer science and operations research, since the late 1970’s. A central technique in this field is to approximately optimize the multilinear extension of the submodular objective, and then round the solution. The use of this technique requires a solver able to approximately maximize multilinear extensions. Following a long line of work, Buchbinder and Feldman(2019) described such a solver guaranteeing 0.385-approximation for down-closed constraints, while Oveis Gharan and Vondrak (2011) showed that no solver can guarantee better than 0.478-approximation. In this talk, I will present a new solver guaranteeing 0.401-approximation, which significantly reduces the gap between the best known solver and the inapproximability result. The design and analysis of the new solver are based on a novel bound that we have proved for DR-submodular functions, and might be of independent interest.

Based on a joint work with Niv Buchbinder.

In recent years, chemistry education has increasingly emphasized the integration of the Sustainable Development Goals (SDGs) into curricula, aiming to foster responsible global citizenship. In this lecture, we will explore two educational programs designed to enhance critical thinking, digital literacy, and student engagement with environmental issues. I will present findings on how these initiatives foster students’ critical thinking, encourage data-driven activism, and strengthen their sustainability agency. By showcasing the transformative potential of Education for Sustainable Development (ESD) in the chemistry classroom, I will highlight the role of science education in empowering students to drive meaningful change.

This talk presents two wavelet-based innovations that significantly improve convolutional neural networks (CNNs) in terms of scalability, computational efficiency, and robustness. I’ll begin with WTConv, a novel convolutional layer that leverages multilevel Haar wavelet decomposition to expand receptive fields efficiently. WTConv scales logarithmically with kernel size, enabling nearly global receptive fields without the parameter bloat typical of large kernels. Beyond improving classification accuracy, WTConv boosts shape bias and robustness to corruptions, all while remaining a lightweight, drop-in replacement for depthwise convolutions.

Next, I’ll introduce Wavelet Compressed Convolution (WCC), a method for compressing high-resolution activation maps in image-to-image tasks. By applying joint wavelet-domain shrinkage across channels and executing 1×1 convolutions directly on the compressed representation, WCC substantially reduces memory bandwidth and compute cost. Unlike aggressive quantization—which often causes severe degradation—WCC maintains high accuracy across tasks like segmentation, super-resolution, and depth estimation, even under extreme compression. Together, these methods show how wavelet transforms can serve as a powerful, hardware-friendly toolset for designing scalable and efficient CNNs.

Bio:
Shahaf Finder is a PhD candidate in Computer Science, supervised by Prof. Oren Freifeld and Prof. Eran Treister, researching efficient neural networks with a focus on convolutional neural networks (CNNs) and graph neural networks (GNNs). He is also a Co-Founder of LimitlessCNC, a startup focused on integrating AI into CNC programming, where he leads the development of the core algorithmic technology.