אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

הרצאה של חברת  STARKWARE - ההרצאה תעסוק בנושא Proof Systems  ותתמקד בגישור הפער שבין התיאוריה ליישום בעולם האמיתי (בהובלת ד"ר גיל בן שחר וסתיו בנו).  לינק להרשמה ...
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Machine learning relies on its ability to generalize from limited data, yet a principled theoretical understanding of generalization remains incomplete. While binary classification is well understood in the classical PAC framework, even its natural extension to multiclass learning is substantially more challenging. In this talk, I will present recent progress in multiclass learning that characterizes when generalization is possible and how much data is required, resolving a long-stand...
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Elections are a fundamental mechanism for collective decision-making. Yet in many real-world settings, the available information is often incomplete, inconsistent, or uncertain. This thesis addresses this challenge by developing computational frameworks for analyzing election outcomes under various forms of uncertainty. Our work builds upon the established paradigm of possible and necessary winners, where the goal is to determine which candidates could or must win given partially ordered vote...
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Modern machine learning systems operate in regimes that challenge classical learning-theoretic assumptions. Models are highly overparameterized, trained with simple optimization algorithms, and rely critically on how data is collected and curated. Understanding the limits of learning in these settings requires revisiting both the computational and statistical foundations of learning theory. A central question in learning theory asks which functions are tractably learnable. Classical c...
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We introduce a distributed computational model referred to as the \emph{uniform port} model.An algorithm operating in this model is defined by means of local automata associated with the ports (a.k.a. half-edges) of the input graph.The crux of the uniform port model is that the local automata are identical and admit a constant size description, making the model \emph{truly uniform}.Moreover, since the new model explicitly supports the assignment of (input and) output labels to the...
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Large Language Models (LLMs) have transformed what machines can do and how systems are designed to serve them. These models are both computationally and memory demanding, revealing the limits of traditional optimization methods that once sufficed for conventional systems. A central challenge in building LLM systems is improving system metrics while ensuring response quality. This talk presents approaches for reducing latency in LLM systems to support interactive applications, from sch...
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3D geometry powers design, perception, and simulation across various domains, including healthcare, transportation, manufacturing, and entertainment. Yet, traditional workflows for creating and editing 3D content are largely inaccessible, requiring expert knowledge and extensive manual effort. Recent advances in deep learning have enabled the generation of 3D assets through simple interfaces, such as natural language prompts. However, these AI models provide limited control, making it hard to...
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"טכניון על הבר" חודש ינואר - והפעם: למידת מכונה בעולם אנושי מערכות לומדות כבר נמצאות בכל מקום ומשפיעות על חיי היום-יום שלנו אבל האם הן באמת מותאמות לאנשים? בהרצאה מרתקת ייקח אותנו ד"ר ניר רוזנפלד אל האתגרים, ההבטחות, וגם הניסיונות הראשונים (המוצלחים �...
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The field of artificial intelligence (AI) is undergoing a paradigm shift, moving from neural networks trained for narrowly defined tasks (e.g., image classification and machine translation) to general-purpose models such as ChatGPT. These models are trained at unprecedented scales to perform a wide range of tasks, from providing travel recommendations to solving Olympiad-level math problems. As they are increasingly adopted in society, a central challenge is to ensure the alignment of general...
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We introduce a vision-guided robotic system for automated saffron flower harvesting. Using camera-based perception and robotic manipulation, the system detects and cuts whole flowers while preserving their stigmas and avoiding plant damage....
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Understanding how large language models store, retain, and remove knowledge is critical for interpretability, reliability, and compliance with privacy regulations.My work introduces a geometric perspective on memorization and unlearning by analyzing loss behavior over semantically similar inputs through the Input Loss Landscape. I show that retained, forgotten, and unseen examples exhibit distinct patterns that reflect active learning, suppressed knowledge, and ignored information...
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I will present an overview of some topics in computational (and combinatorial, and a bit algebraic) geometry, that constitute milestones in the work in this area by myself and by many colleagues and (former) students in the past 45 years. The topics include, as time permits, algorithmic motion planning, arrangements, lower envelopes, incidences, space decomposition, polynomial partitioning, and more.  Bio: Micha Sharir received his Ph.D. in Mathematics from Tel A...
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בואו להיות חלק מקבוצת Capture The Flag-CTF של הטכניון!! המפגש יתקיים ביום שני 5.1 בשעה 18:30, בטאוב 9. מה במפגש?הרצאת אורח מאת אורי בר - אבטחת סייבר במערכות רכב חכמות: מנקודת מבט של תוקף אורי בר הוא חוקר אבטחה התקפית ומנהיג קבוצת הערכת אבטחה בצוות ה...
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אנחנו מתרגשות להזמין אתכן לערב של הרצאות TED עם פיצות ובירות!   אתן רגילות לפגוש אותן בתרגולים ליד הלוח אבל רוב זמנן בפקולטה מוקדש למחקר פורץ דרך, עכשיו זאת ההזדמנות שלכן לשמוע על המחקרים שלהן!הצטרפו לערב TED שיכלול 3 הרצאות קצרות ומרתקות של חוקרות מהפקולטה של�...
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In the context of Byzantine consensus problems such as Byzantine broadcast (BB) and Byzantine agreement (BA), the good-case setting aims to study the minimal possible latency of a BB or BA protocol under certain favorable conditions, namely the designated leader being correct (for BB), or all parties having the same input value (for BA). We provide a full characterization of the feasibility and impossibility of good-case latency, for both BA and BB, in the synchronous sleepy model. Surprising...
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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 parameter...
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From rough sketches that spark ideas to polished designs that explain complex concepts, visual communication is central to how humans think, create, and share knowledge. Yet despite major advances in generative AI, we are still far from models that can reason and communicate through visual forms.I will present my work on bridging generative models and visual communication, focusing on three complementary domains: (1) algorithms for generating and understanding sketches, (2) systems that s...
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CRISPR-Cas genome editing is a highly specific technology that enables edit operations in genomes and other DNA fragments. CRISPR-Cas as a tool in biotechnology is the result of harnessing a similar system that naturally occurs in bacteria. The technology holds promise as a component in therapy and clinical approaches. Indeed - several therapies were recently approved for use in patients. For example - Casgevy, a sickle cell anemia therapy that is showing great positive results. One of the ch...
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I will explain a phenomenon that I call the "Average Scale is Boring Principle" (you may help me come up with a better name): The 01010101... sequence, for example, has high variance on a local scale, but if you look at larger scales each segment looks identical. For another example, the sequence 0^n1^n looks different at a global scale, but on almost all short intervals it is constant. I will formalize a sense in which some sequences have high variance on some scales, but for *all* sequences...
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