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Earth's landscapes are shaped by competition between tectonic plates that push bedrock upward and river networks that remove mass. Transport of countless rock fragments is a fundamental aspect of this action, resonating with many other near-surface processes across the hydrosphere, biosphere, and geosphere. Identifying how efficiently rock fragments are transported away, considering their properties and ecohydrological feedbacks during weather events, has remained a persistent scientific challenge since the dawn of computational geomorphology. With recent advances in terrain remote sensing and analysis techniques, hydroclimate observations/models, and computational methods for describing dynamic topography, a research frontier is emerging, paving the way for a promising new era in the science of surface processes and topographic forms. The seminar first presents a new application of a theory for heterogeneous sediment transport in mountainous gravel-bed rivers. A set of numerical experiments discovered process-form relations that emerge from sediment grains' lithological heterogeneity. Then, the talk will present a first-of-its-kind Earthcasting approach that integrates high-resolution event-scale rainfall forcing into a Holocene-scale landscape evolution research framework. Within that timescale, the importance of the interaction between soil grains and ecohydrological processes in shaping fast-evolving landforms is highlighted. Lastly, paleo-rainfall regimes capable of triggering erosion-deposition cycles and possible future transitions to a unique climate-erosion state by the 21st century will be demonstrated. The findings have the potential to shift paradigms in the interpretation of sediment records and landscape forms. The newly developed methodologies enable unprecedented quantification of surface processes with respect to material properties and climate forcings, which open opportunities toward a transformational understanding of landscape evolution. 

Let \theta and \mu be two Borel probability measures on a topological group G such that the subsemigroup generated by the support of \theta is contained in the subsemigroup generated by the support of \mu. We show that if the total variation distance of \theta\mu^n and \mu^n\theta, where the multiplication is understood to be convolution, goes to 0 as n tends to infinity, then every bounded \mu-harmonic function on G is also \theta-harmonic. Among other things, this result gives elegant alternative proofs of several known theorems, for example that for any probability measure \nu on G, the centre of G acts trivially on the Poisson boundary of (G,\nu). Joint work with Yair Hartman and Omer Segev.

Lior will relate her PhD study on the global biomass of birds. The thrushes are a passerine bird family, Turdidae, with a worldwide distribution. The family was once much larger before biologists reclassified the former subfamily Saxicolinae, which includes the chats and European robins, as Old World flycatchers. Thrushes are small to medium-sized ground living birds that feed on insects, other invertebrates, and fruit. Some unrelated species around the world have been named after thrushes due to their similarity to birds in this family.

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Over the past decade, deep learning algorithms have achieved—and in some cases surpassed—human-level performance in face recognition. This remarkable success raises a fundamental question: to what extent do these artificial systems capture the mechanisms that underlie human face recognition? In this talk, I will explore the convergences and divergences between deep learning models and the human face recognition system. I will first show how deep convolutional neural networks (DCNNs)
reproduce key phenomena observed in human face perception. Yet, despite these similarities, important differences remain in how humans and deep learning algorithms learn and represent faces. To bridge these gaps, we employ models that learn continually and integrate visual and language-based models, to capture both perceptual and conceptual aspects of face recognition. Together, these findings demonstrate how deep learning algorithms can advance our understanding of human face recognition.

BIO:
Galit Yovel is a professor in the School of Psychological Sciences and Sagol School of Neuroscience at Tel Aviv University. She earned her PhD in Psychology from the University of Chicago and completed her Post-Doctoral studies in the Department of Brain and Cognitive Sciences at MIT. In her research she combines methods from experimental psychology, neuroimaging and AI to unravel the neural and cognitive mechanisms of human face recognition. Her work extends beyond faces to examine how the body, voice, motion, and semantic information contribute to person recognition. She was the head of Strauss MRI Center at Tel Aviv University (2015-2017), the head of the School of Psychological Sciences (2017-2021) and the head of the AI and Data Science major for students in life science/social sciences and law (2022-2025). She is the recipient of the Bruno award (2017), and a six-time recipient
of the Tel Aviv University Rector award for excellence in teaching.