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אפריל 25, 2016
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Date:11חמישידצמבר 2025הרצאה
Vision and AI
More information שעה 12:15 - 13:15כותרת Who Said Neural Networks Aren't Linear?מיקום בניין יעקב זיסקינד
Lecture Hall - Room 1 - אולם הרצאות חדר 1מרצה Assaf Shocher
Technionמארגן המחלקה למדעי המחשב ומתמטיקה שימושיתצרו קשר תקציר Show full text abstract about NeNeural networks are famously nonlinear. However, linearity...» NeNeural networks are famously nonlinear. However, linearity is defined relative to a pair of vector spaces, f:X→Y. Is it possible to identify a pair of non-standard vector spaces for which a conventionally nonlinear function is, in fact, linear? This paper introduces a method that makes such vector spaces explicit by construction. We find that if we sandwich a linear operator between two invertible neural networks, then the corresponding vector spaces are induced by newly defined operations. This framework makes the entire arsenal of linear algebra applicable to nonlinear mappings. We demonstrate this by collapsing diffusion model sampling into a single step, enforcing global idempotency for projective generative models, and enabling modular style transfer.
Bio:
Assaf is an Assistant Professor at the Technion in the Faculty of Data and Decision Sciences. Previously, he was a Research Scientist at NVIDIA, a Postdoc at UC Berkeley with Alyosha Efros, and a Visiting Scholar at Google DeepMind. I received my PhD from the Weizmann Institute of Science, advised by Michal Irani. I have two Bachelor degrees from Ben-Gurion University in Physics and Electrical-Engineering. Assaf’s research focuses on Deep Neural Networks for computer vision, guided by two core principles: a pursuit of elegant, foundational ideas that offer fundamentally new perspectives, and a focus on dynamic and adaptive learning for real-world scenarios like unannotated data streams and distribution shifts. -
Date:11חמישידצמבר 2025הרצאה
Geometric Functional Analysis and Probability Seminar
More information שעה 13:30 - 14:30כותרת Generalized Hodge theory for geometric boundary-value problemsמיקום בניין יעקב זיסקינד
Room 155 - חדר 155מרצה Roee Leder
HUJIמארגן הפקולטה למתמטיקה ומדעי המחשבצרו קשר תקציר Show full text abstract about A fundamental theorem states that a two-dimensional Riemanni...» A fundamental theorem states that a two-dimensional Riemannian manifold with boundary, equipped with a symmetric tensor field, is locally isometrically embedded in Euclidean space if and only if the symmetric tensor field satisfies the Gauss-Mainardi-Codazzi equations—in which case, the tensor field is the second fundamental form.
When the intrinsic metric is Euclidean, it is a classical result that such tensor fields are Hessians of functions satisfying the Monge-Ampère equation. I shall present a version of this result to arbitrary Riemannian metrics, using a generalized Hodge theory I developed for a broader class of geometric boundary-value problems. I will discuss this theory, its main features, and perhaps give a glimpse of more complicated examples it addresses. -
Date:11חמישידצמבר 2025הרצאה
Lung cancer – advances in recent years and the role of B-cells in immune response
More information שעה 14:00 - 15:00מיקום Candiotty
Auditoriumמרצה Prof. Jair Bar מארגן המכון לחקר הטיפול בסרטן עש דואק -
Date:14ראשוןדצמבר 2025הרצאה
The Clore Center for Biological Physics
More information שעה 13:15 - 14:30כותרת Self-organized hyperuniformity in population dynamicsמיקום ספרית הפיסיקה על שם נלה וליאון בנוזיומרצה Dr. Tal Agranov
Lunch at 12:45צרו קשר תקציר Show full text abstract about Living systems often operate at critical states – poised on ...» Living systems often operate at critical states – poised on the border between two distinct dynamical behaviours, where unique functionality emerges [1]. A striking example is the ear’s sensory hair cells, which amplify faint sounds by operating on the verge of spontaneous oscillations [2]. How such finely tuned states are maintained, and what statistical signatures characterise them, remain major open questions.In this talk, I will present a generic mechanism for critical tuning in population dynamics [3]. In our model, the consumption of a shared resource drives the population towards a critical steady state characterised by prolonged individual lifetimes. Remarkably, we find that in its spatially extended form, the model exhibits hyperuniform density correlations. In contrast to previously studied hyperuniform systems, our model lacks conservation laws even arbitrarily close to criticality. Through explicit coarse-graining, we derive a hydrodynamic theory that clarifies the underlying mechanism for this striking statistical behaviour. I will highlight several biological contexts in which this mechanism is expected to operate, including biomolecular complex assembly in the developing C. elegans embryo. Here, together with experimental collaborators, we identify signatures of critical tuning that may arise from resource competition.More broadly, our framework motivates future work on how living systems harness resource-mediated interactions to regulate their dynamical states.[1] T. Mora, W. Bialek, J Stat Phys (2011)[2] S. Camalet, T. Duke, F. Jülicher and J. Prost, PNAS (1999)[3] T Agranov, N. Wiegenfeld,O. Karin and B. D. Simons arXiv:2509.08077 (2025)FOR THE LATEST UPDATES AND CONTENT ON SOFT MATTER AND BIOLOGICAL PHYSICS AT THE WEIZMANN, VISIT OUR WEBSITE: https://www.bio -
Date:14ראשוןדצמבר 202515שנידצמבר 2025כנסים
Symposium in honor of Rafi Malach - The Mind's Eye: A Quest from Vision to Consciousness
More information שעה 14:00 - 19:00כותרת Symposium in honor of Rafi Malach - The Mind's Eye: A Quest from Vision to Consciousnessמיקום מרכז כנסים על-שם דויד לופאטייושב ראש Michal Ramotדף בית צרו קשר -
Date:16שלישידצמבר 2025הרצאה
Recent Advances in Understanding Arenaviral Cell Entry and Immune Recognition
More information שעה 11:15 - 12:15מיקום אולם הרצאות ע"ש גרהרד שמידטמרצה Prof. Ron Diskin מארגן המחלקה לביולוגיה מבנית וכימית -
Date:16שלישידצמבר 2025הרצאה
Mathematics Colloquium
More information שעה 11:15 - 12:30כותרת A perspective on Stationary Gaussian processesמיקום בניין יעקב זיסקינד
Room 1 - 1 חדרמרצה Naomi Feldheim
Bar Ilan Universityמארגן המחלקה למדעי המחשב ומתמטיקה שימושיתצרו קשר תקציר Show full text abstract about Real stochastic processes are random real-valued functions o...» Real stochastic processes are random real-valued functions on an underlying space (in this talk, Z^d or R^d). Gaussianity occurs when a process is obtained as a sum of many infinitesimal independent contributions, and stationarity occurs when the phenomenon in question is invariant under translations in time or in space. This makes stationary Gaussian processes (SGPs) an excellent model for noise and random signals, placing them amongst the most well studied stochastic processes.
Persistence of a stochastic process is the event of remaining above a fixed level on a large ball of radius T. For a Stationary Gaussian process, we ask two basic questions:
1. What is the asymptotic behavior of the persistence probability, as T grows?
2. Conditioned on the persistence event, what is the typical shape of the process (if there is one)?
These questions, posed by physicists and applied mathematicians decades ago, have been successfully addressed only in the last few years, by exploiting strong relations with harmonic analysis.
In this talk, we will describe old and new results, the main tools and ideas used to achieve them, and many open questions that remain. -
Date:18חמישידצמבר 2025סימפוזיונים
Physics Colloquium
More information שעה 11:15 - 12:30כותרת The quest for the Nonlinear Breit-Wheeler Pair Production Measurementמיקום Weissman Auditoriumמרצה Dr. Noam Tal-Hod מארגן המחלקה לפיזיקה של מערכות מורכבותצרו קשר תקציר Show full text abstract about The nonlinear Breit-Wheeler process — electron-positron pair...» The nonlinear Breit-Wheeler process — electron-positron pair creation from high-energy photons in an intense electromagnetic field — is one of the most fundamental yet experimentally elusive predictions of strong-field quantum electrodynamics. Reaching the regime where this process becomes measurable requires not only extreme light-matter interaction conditions, but also detecting technologies capable of resolving rare signatures amid complex backgrounds. Beyond its intrinsic importance for testing quantum electrodynamics in the strongest fields accessible on Earth, this process is also relevant for understanding environments such as magnetars, where similarly intense fields and abundant pair production naturally occur. I will present the ongoing international effort to realize a definitive measurement of the process and highlight how advanced particle-tracking methods, commonly used in High-Energy Physics experiments, are contributing to this goal. I will discuss the running E320 experiment at SLAC, where our tracking detector is used to characterize collisions of 10 GeV electrons and 10 TW laser pulses in unprecedented detail, and give an outlook on the upcoming LUXE experiment at DESY, which aims to operate at the intensity frontier. I will also describe new opportunities at high-power multi-PW laser facilities — including our recent all-laser campaigns at ELI-NP and APOLLON — that open complementary routes to probe strong-field physics in complementary parameter spaces. Together, these efforts bring accelerator-based, laser-based and particle physics approaches closer to a definitive measurement of the nonlinear Breit-Wheeler process. -
Date:18חמישידצמבר 2025הרצאה
Vision and AI
More information שעה 12:15 - 13:15כותרת Bridging Generative Models and Physical Priors for 3D Reconstructionמיקום בניין יעקב זיסקינד
Lecture Hall - Room 1 - אולם הרצאות חדר 1מרצה Dor Verbin
Google DeepMindמארגן המחלקה למדעי המחשב ומתמטיקה שימושיתצרו קשר תקציר Show full text abstract about Recent years have brought remarkable progress in 3D vision p...» Recent years have brought remarkable progress in 3D vision problems like view synthesis and inverse rendering. Despite these advancements, substantial challenges remain in material and lighting decomposition, geometry estimation, and view synthesis—particularly when handling a wide range of materials. In this talk, I will outline a few of these problems and present solutions that combine the principled structure and efficiency of physics-based rendering with the strong priors encoded in generative image and video models.
Bio:
Dor Verbin is a research scientist at Google DeepMind in San Francisco, where he works on computer vision, computer graphics, and machine learning. He received his Ph.D. in computer science from Harvard University. Previously, he received a double B.Sc. in physics and in electrical engineering from Tel Aviv University, after which he worked as a researcher at Camerai, developing real-time computer vision algorithms for mobile devices. He received the Best Student Paper Honorable Mention award at CVPR 2022. -
Date:18חמישידצמבר 2025הרצאה
Geometric Functional Analysis and Probability Seminar
More information שעה 13:30 - 14:30כותרת Metric smoothnessמיקום בניין יעקב זיסקינד
Room 155 - חדר 155מרצה Assaf Naor
Princetonמארגן המחלקה למדעי המחשב ומתמטיקה שימושיתצרו קשר תקציר Show full text abstract about A foremost longstanding open problem in the Ribe program is ...» A foremost longstanding open problem in the Ribe program is to find a purely metric reformulation of the Banach space property of having an equivalent norm whose modulus of uniform smoothness has a given power type. In this talk we will present a solution of this problem. All of the relevant background and concepts will be explained, and no prerequisites will be assumed beyond rudimentary undergraduate functional analysis and probability. Based on joint work with Alexandros Eskenazis. -
Date:21ראשוןדצמבר 202522שנידצמבר 2025אירועים אקדמיים
Hanukkah STAR - workshop 2025
More information שעה כל היוםמיקום בניין יעקב זיסקינד
Room 1דף בית -
Date:22שנידצמבר 2025הרצאה
Seminar for PhD thesis Defense by Yahel Cohen
More information שעה 11:00 - 12:00כותרת “miRNA isoforms as biomarkers for amyotrophic lateral sclerosis prognosis”מיקום Benoziyo Biochemistry auditorium room 191c-new -
Date:22שנידצמבר 2025הרצאה
Foundations of Computer Science Seminar
More information שעה 11:15 - 12:15כותרת Corners and Communication Complexityמיקום בניין יעקב זיסקינד
Lecture Hall - Room 1 - אולם הרצאות חדר 1מרצה Shachar Lovett
UCSDמארגן המחלקה למדעי המחשב ומתמטיקה שימושיתצרו קשר תקציר Show full text abstract about The corners problem is a classical problem in additive combi...» The corners problem is a classical problem in additive combinatorics. A corner is a triple of points (x,y), (x+d,y), (x,y+d). It can be viewed as a 2-dimensional analog of a (one-dimensional) 3-term arithmetic progression. An old question of Ajtai and Szemeredi is: how many points can there be in the n x n integer grid without containing a corner? They proved a qualitative bound of o(n^2), but no effective quantitative bounds.
This question has an equivalent description in the language of communication complexity. Given 3 players with inputs x,y,z which are integers in the range 1 to n, what is the most efficient Number-On-Forehead (NOF) deterministic protocol to check if they sum to n. This connection was first observed in the seminal paper of Chandra, Furst and Lipton that introduced the NOF model back in 1983.
In the language of communication complexity, the trivial protocol sends log(n) bits, but there is a better NOF protocol (based on constructions in additive combinatorics) which only sends (log n)^{1/2} bits. However, the best lower bound until our work was double exponentially far off - of the order of log log log n. In this work, we close this gap, and prove a lower bound of (log n)^c for some absolute constant c.
The work is based on combining the high-level approach of Shkredov, who obtained the previous lower bound, which was based on Fourier analysis; with the recent breakthrough of Kelley and Meka on the 3-term arithmetic progression problem, and the ensuing developments. The main message is that "spreadness" based techniques (a notion that I will explain in the talk) give significantly better quantitative bounds compared to classical Fourier analysis.
Joint work with Michael Jaber, Yang P. Liu, Anthony Ostuni and Mehtaab Sawhney
Paper will appear in FOCS 2025
https://arxiv.org/abs/2504.07006 -
Date:23שלישידצמבר 2025הרצאה
Climate modeling in the era of AI
More information שעה 11:30 - 12:30מיקום אולם הרצאות ע"ש גרהרד שמידטמרצה Laure Zanna מארגן המחלקה למדעי כדור הארץ וכוכבי הלכתתקציר Show full text abstract about While AI has been disrupting conventional weatherforecasting...» While AI has been disrupting conventional weatherforecasting, we are only beginning to witness theimpact of AI on long-term climate simulations. Thefidelity and reliability of climate models have beenlimited by computing capabilities. These limitationslead to inaccurate representations of key processessuch as convection, cloud, or mixing or restrict theensemble size of climate predictions. Therefore, theseissues are a significant hurdle in enhancing climatesimulations and their predictions.Here, I will discuss a new generation of climatemodels with AI representations of unresolved oceanphysics, learned from high-fidelity simulations, andtheir impact on reducing biases in climatesimulations. The simulations are performed withoperational ocean model components. I will furtherdemonstrate the potential of AI to accelerate climatepredictions and increase their reliability through thegeneration of fully AI-driven emulators, which canreproduce decades of climate model output in secondswith high accuracy -
Date:23שלישידצמבר 2025הרצאה
Hidden genetic complexities and phenotypic consequences of evolving plant stem cell networks across nature and agriculture
More information שעה 11:45 - 12:00כותרת Prof. Zachary Lippman HHMI, School of Biological Sciences, CSHLמיקום בניין לביוכימיה על שם נלה וליאון בנוזיו למדעי הצמח
Auditoriumמרצה Prof. Zachary Lippman- HHMI, School of Biological Sciences, CSHL צרו קשר -
Date:23שלישידצמבר 2025הרצאה
Olfactory perception in Mice: identity, intensity, and natural correlates
More information שעה 12:30 - 13:30מיקום אולם הרצאות ע"ש גרהרד שמידטמרצה Prof. Dmitry Rinberg מארגן המחלקה למדעי המוחצרו קשר תקציר Show full text abstract about Olfactory perception raises two fundamental questions:&n...» Olfactory perception raises two fundamental questions: ‘What is an odor?’ — its identity—and ‘How strong is an odor?’ — its intensity. While significant progress has been made in characterizing these perceptual variables in humans in relation to odor composition and concentration, limitations in human neuroscience methods restrict our ability to probe their neural correlates with high temporal and spatial resolution. To address this gap, we developed a novel behavioral paradigm in mice to study the neural computations underlying odor perception. First, we trained mice to report perceptual distances between odors, enabling us to construct a mouse odor perceptual space that links perceptual odor identity to neural representations. Second, we trained mice to match the perceived intensities of different odors, creating a framework to probe the neural coding of odor intensity. This approach offers a path to connecting perceptual judgments to neural activity and may be extensible to other sensory systems. -
Date:24רביעידצמבר 2025הרצאה
Machine Learning and Statistics Seminar
More information שעה 11:15 - 12:15כותרת Two Lenses on Deep Learning: Data Reconstruction and Transformer Structureמיקום בניין יעקב זיסקינד
Room 1 - 1 חדרמרצה Gilad Yehudai
New York Universityמארגן המחלקה למדעי המחשב ומתמטיקה שימושיתצרו קשר תקציר Show full text abstract about Despite the remarkable success of modern deep learning, our ...» Despite the remarkable success of modern deep learning, our theoretical understanding remains limited. Many fundamental questions about how these models learn, what they memorize, and what their architectures can express are still largely open. In this talk, I focus on two such questions that offer complementary perspectives on the behavior of modern networks.
First, I examine how standard training procedures implicitly encode aspects of the training data in the learned parameters, enabling reconstruction across a wide range of architectures and loss functions. Second, I turn to transformers and analyze how architectural choices, such as the number of heads, rank, and depth, shape their expressive capabilities, revealing both strengths and inherent limitations of low-rank attention.
Together, these perspectives highlight recurring principles that shape the behavior of deep models, bringing us closer to a theoretical framework that can explain and predict the phenomena observed in practice.
Bio:
Gilad is a postdoctoral research associate at the Courant Institute of Mathematical Sciences at New York University, hosted by Prof. Joan Bruna. His research focuses on the theory of deep learning models, with a recent emphasis on transformers. He also works on attacks on neural networks, particularly data reconstruction attacks and adversarial attacks. Previously, he completed his Ph.D. at the Weizmann Institute of Science under the supervision of Ohad Shamir. He has held research internships at NVIDIA, where he worked on graph neural networks, and at Google Research, where he worked on large-scale optimization. He holds a B.Sc. and M.Sc. in mathematics from Tel Aviv University. -
Date:24רביעידצמבר 2025הרצאה
2025-2026 Spotlight on Science Seminar Series - Dr. Jacques Pienaar (Department of Physics Core Facilities)
More information שעה 12:30 - 14:00כותרת Illuminating the Dark: The Search for Dark Matterמיקום אולם הרצאות ע"ש גרהרד שמידטמרצה Jacques Pienaar צרו קשר תקציר Show full text abstract about Cosmological observations suggest that about 85% of the univ...» Cosmological observations suggest that about 85% of the universe’s mass is made up of matter that neither emits nor absorbs light. The existence of this mysterious component—dark matter—is inferred from its gravitational effects and is theorized to interact only very weakly with ordinary matter. The XENON detector, located deep underground in Italy’s Gran Sasso Laboratory, employs a large reservoir of ultrapure liquid xenon to search for the faint signals produced when a dark matter particle collides with a xenon atom. By suppressing background radiation and using highly sensitive sensors, the experiment strives to observe these extremely rare events. Although dark matter remains undetected, XENON continues to search while also shaping future searches. -
Date:25חמישידצמבר 2025סימפוזיונים
Special Physics Colloquium
More information שעה 12:30 - 14:00כותרת Is there turbulence in the deep ocean?מיקום Physics Weissman Auditoriumתקציר Show full text abstract about Short answer: Yes. One might imagine the deep ocean as a dar...» Short answer: Yes. One might imagine the deep ocean as a dark, silent world, largely untouched by the restless motion seen at the surface, where winds raise waves and storms stir the sea. However, just as surface waves exist along the sharp density interface between the ocean and the atmosphere, internal waves are supported by smooth vertical gradients in density far beneath the ocean's surface. The turbulence of these waves plays a central role in ocean mixing and circulation.I will introduce surface and internal waves as examples of dispersive wave systems, and explain how their long-time dynamics can be described using the theory of weak wave turbulence. I will then present our recent work, which addresses a long-standing problem in geophysical fluid dynamics: deriving the observed broadband oceanic spectrum of internal waves, known as the Garrett-Munk spectrum, directly from the governing equations.The central message of the talk is that the weak-rotation limit is singular, and that it is precisely this singular limit that allows the oceanic spectrum to emerge from first principles.No background in geophysical fluid dynamics will be assumed. -
Date:25חמישידצמבר 2025הרצאה
Geometric Functional Analysis and Probability Seminar
More information שעה 13:30 - 14:30כותרת Statistical properties of Markov shiftsמיקום בניין יעקב זיסקינד
Room 155 - חדר 155מרצה Yeor Hafouta
Floridaמארגן המחלקה למדעי המחשב ומתמטיקה שימושיתצרו קשר תקציר Show full text abstract about The central limit theorem (CLT) and related results for stat...» The central limit theorem (CLT) and related results for stationary weakly dependent sequences of random variables have been extensively studied in the past century, starting from a pioneering work of Berenstien (1927). However, in many physical phenomena there are external forces, measurement errors and unknown variables (e.g. storms, the observer effect, the uncertainty principle etc.). This means that the local laws of physics depend on time, and it leads us to studying non-stationary sequences.
The asymptotic behaviour of non-stationary sequences have been studied extensively in the past decades, but it is still developing compared with the theory of stationary processes. In this talk we will focus on inhomogeneous Markov chains. For sufficiently well contracting Markov chains the CLT was first proven by Dobrushin (1956). Since then many results were proven for stationary chains. In 2021 Dolgopyat and Sarig proved local central limit theorems (LCLT) for inhomogeneous Markov chains. In 2022 Dolgopyat and H proved optimal CLT rates in Dobrusin's CLT. These results closed a big gap in literature concerning the non-stationary case.
An open problem raised by Dolgopyat and Sarig in their 2021 book concerns limit theorems for Markov shifts, that is when the underlying sequence of functions that forms the partial sums depend on the entire path of the chain. Two circumstances where such dependence arises are products of random matrices and random iterated functions, and there are many other instances when the functionals depend on the entire path.
In this talk we will present our solution to the above problem. More precisely, we prove CLT, optimal CLT rates and LCLT for a wide class of sufficiently well mixing Markov chains and functionals with infinite memory. Even though the inhomogeneous case is more complicated, our results seem to be new already for stationary chains.