retrieve:
Return the details about the given Event id.

list:
List all Event objects.

GET /api/v1/events/?format=api&offset=250&ordering=-event__is_internal
HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

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            "id": 72754,
            "title": "Exploring the ∞-categorical semantics of homotopy type theory",
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            "start_date": "2026-10-15",
            "end_date": "2026-10-15",
            "start_time": "10:00:00",
            "end_time": "11:00:00",
            "description": "<p>This talk is meant to give an overview of the semantics of HoTT in (∞,1)-categories, tackling the key challenge of rigidification: the process of bridging homotopy-coherent notions phrased in models of higher categories such as quasicategories, and their type-theoretical counterparts that must account for the inherent strictness of type theory.<br>\r\nAfter quickly recalling the usual categorical semantics of extensional Martin-Löf type theory within locally cartesian closed category, the plan is to present some useful ideas to carry over some of the argument to the higher setting. Specifically, I will discuss the process of turning an elementary higher topos into a model of HoTT, and sketch important ideas used to prove the internal language conjecture for locally cartesian closed (∞,1)-categories.<br>\r\n<br>\r\n </p>",
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            "speaker": "El Mehdi Cherradi, IRIF",
            "organizer": "Virgile Constantin",
            "contact": "Maroussia Schaffner",
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            "id": 72757,
            "title": "Measurement of the differential branching fraction of B -> K pi pi mu mu at LHCb",
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            "start_date": "2026-10-30",
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            "description": "<p>Thesis Directors: Prof. L. Shchutska, Prof. E. Graverini<br>\r\nPhysics doctoral program<br>\r\nThesis Nr. 11987<br>\r\n<br>\r\nTo take part in the public defense, please contact directly the speaker</p>",
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            "creation_date": "2026-10-02T16:26:38",
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            "speaker": "<strong>Anni Matilda KAUNISKANGAS</strong>",
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            "id": 72758,
            "title": "Safe Guaranteed Exploration for Non-linear Systems",
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            "description": "<strong>Abstract</strong>: <br>\r\nSafely exploring environments with a-priori unknown constraints is a fundamental challenge that restricts the autonomy of robots. While safety is paramount, guarantees on sufficient exploration are also crucial for ensuring autonomous task completion. To address these challenges, we propose a novel safe guaranteed exploration framework using optimal control, which achieves first-of-its-kind results: guaranteed exploration for non-linear systems with finite time sample complexity bounds, while being provably safe with arbitrarily high probability. The framework is general and applicable to many real-world scenarios with complex non-linear dynamics and unknown domains. For efficient implementation, we exploit goal-directed exploration, and receding-horizon replanning while preserving the framework’s guarantees, and demonstrate safe, efficient exploration in challenging unknown environments using a car model.<br>\r\n<br>\r\n<strong>Biography</strong>: <br>\r\nManish Prajapat earned his Ph.D. in reinforcement learning and control from ETH Zurich in May 2026. He was a Doctoral Fellow at the ETH AI Center working with Prof. Melanie Zeilinger and Prof. Andreas Krause. Previously, he earned his master’s degree in Robotics, Systems, and Control from ETH Zurich and was a visiting scholar at Caltech. He received his bachelor’s degree from the Indian Institute of Technology (IIT) Madras in 2017. At IIT Madras, he was honored as the Best Graduating Student (Co-curricular) in 2017 and received the Sivasailam Merit Prize for the best thesis. His research interests are sequential decision-making under complex scenarios, e.g., non-Markovian objectives, unknown constraints or unknown dynamics of non-linear systems.<br>\r\n<br>\r\n ",
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            "creation_date": "2026-10-02T16:39:32",
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            "speaker": "Dr Manish Prajapat Ph.D. reinforcement learning and control from <a href=\"https://control.ee.ethz.ch\">ETH Zurich</a>\r\n\r\n<a href=\"https://ieee.ch/chapters/control-systems-society/\">Winner of the IEEE CSS Young Author Best Journal Paper Award 2026</a>",
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            "id": 72759,
            "title": "Strategies in Pd-Catalyzed Carboamination of Unsaturated Bonds and Applications to the Synthesis of N-Heterocycles",
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            "start_date": "2026-12-04",
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            "description": "<p>Thesis Director: Prof. J. Waser,<br>\r\nChemistry and Chemical Engineering doctoral program<br>\r\nThesis Nr. 12072<br>\r\n<br>\r\nTo take part in the public defense, please contact directly the speaker</p>",
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            "creation_date": "2026-10-02T17:11:58",
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            "contact": "<a href=\"mailto:[email protected]\"><strong>Helena SOLÉ ÀVILA</strong></a><br>\r\n ",
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            "id": 72760,
            "title": "AI Center Seminar - AI Fundamentals series - Ricardo Olmedo - \"Speedrunning SWE-bench, or the $1,000 coding agent\"",
            "slug": "ai-center-seminar-ai-fundamentals-series-ricardo-2",
            "event_url": "https://memento.epfl.ch/event/ai-center-seminar-ai-fundamentals-series-ricardo-2",
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            "start_date": "2026-10-07",
            "end_date": "2026-10-07",
            "start_time": "15:00:00",
            "end_time": "16:00:00",
            "description": "<p>The talk is jointly organized by the <a href=\"https://ai.epfl.ch/\">EPFL AI Center</a> and the <a href=\"https://www.epfl.ch/labs/mlo/\">MLO</a> Lab as part of the AI fundamentals seminar series.<br>\r\n<br>\r\n<strong><em>Hosting Professor: Prof. Martin Jaggi</em></strong><br>\r\n<br>\r\n<strong>Title</strong><br>\r\nSpeedrunning SWE-bench, or the $1,000 coding agent<br>\r\n<br>\r\n<strong>Abstract</strong><br>\r\nSpeedrunning has become a popular engineering challenge in machine learning: achieve the best performance within a fixed compute budget. We use it as a scientific tool to study how models acquire specialized skills and how far those skills transfer. We speedrun SWE-bench by training coding agents from scratch on demonstrations from stronger models. With about $1,000 of training compute, we reach a 25% solve rate, outperforming GPT-4o. Compared to models with extensive pre-training, our speedrun models solve largely the same SWE-bench problems, and generalize about as well to new bugs and codebases. What, then, does pre-training buy for SWE-bench? Our experiments suggest a one-student hypothesis: pre-training helps a student imitate its teacher, but at matched imitation loss, pre-training choices barely matter. We stress-test this hypothesis by comparing a model trained on the web against a “vintage\" one trained only pre-1931 documents. Up to the SWE-bench scores we reach, pre-training is interchangeable with specialization data.  We release nanoswe as an open project for studying specialization through speedrunning.<br>\r\n<br>\r\n<strong>Bio</strong><br>\r\nRicardo Olmedo is a final-year PhD student at the Max Planck Institute for Intelligent Systems in Tübingen and a Google PhD Fellow. Advised by Moritz Hardt and Bernhard Schölkopf, he works on language model benchmarking and specialization. His research has earned three oral presentations at ICLR and NeurIPS. Most recently, he spent spring and summer 2026 visiting Sanmi Koyejo at Stanford University.</p>",
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            "speaker": "Ricardo Olmedo",
            "organizer": "<a href=\"https://ai.epfl.ch/\">EPFL AI Center</a> and the <a href=\"https://www.epfl.ch/labs/mlo/\">MLO</a> Lab",
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            "id": 72764,
            "title": "IMX Talks - Resolving Hierarchical Structure with X-ray scattering imaging",
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            "event_url": "https://memento.epfl.ch/event/imx-talks-resolving-hierarchical-structure-with-x",
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            "description": "<p>The function of many materials, from biological tissues to industrial polymers, depends on how their nanoscale building blocks are arranged and aligned within a macroscopic structure, which is essential to understand but difficult to resolve: established high-resolution techniques typically access only small sample volumes, limiting the ability to capture structural or biological variability across extended, heterogeneous samples.<br>\r\nSmall-angle X-ray scattering (SAXS) imaging probes local nanostructure by raster-scanning a focused synchrotron beam across a sample. This can be extended to resolve nanostructure orientation voxel-by-voxel in three dimensions in a method called SAXS tensor tomography, which we implement in our open-source software package Mumott. In this presentation, I will show how this approach, and its continued development, can be applied across different material systems.<br>\r\nFor injection-molded polymer packaging, SAXS imaging has resolved the layered anisotropic nanostructure important for its mechanical properties, and, combined with industrial processing simulations, the structure formation during thermal processing. Another example is bone, where resolving structure requires both three-dimensional information and sufficient sampling to capture biological variability. Combining 2D and 3D SAXS imaging, we studied regional differences in the human femoral neck, an anatomical site relevant to hip fractures, and, at higher resolution, resolved alternating chirality between neighboring lamellae within individual osteons, a structural feature proposed on mechanical grounds over a century ago.<br>\r\nThese examples illustrate how tensor tomography, extended beyond X-ray scattering to wide-angle diffraction and visible-light polarimetry, provides a route to resolving hierarchical structure across a wide range of material systems.<br>\r\n<br>\r\nBio:<strong> </strong>Marianne Liebi studied food science at ETH Zurich, where she also completed her PhD in 2013, followed by postdoctoral research at the Paul Scherrer Institute and MAX IV Laboratory, Lund. In 2017, she started her own research group at Chalmers University of Technology, Gothenburg, and was appointed in 2021 as tenure-track Assistant Professor at EPFL, where she heads the Laboratory for X-ray Characterization of Materials (cam-X) within the Institute of Materials, in a joint position with the Paul Scherrer Institute (PSI). Her research develops advanced X-ray and optical imaging techniques, centered on small-angle X-ray scattering (SAXS) in 2D and 3D, to resolve the hierarchical structure of materials ranging from fiber composites to pharmaceutical formulations and bone.</p>",
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            "creation_date": "2026-10-05T09:26:03",
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            "speaker": "Prof. Marianne Liebi, EPFL STI IMX CAM-X",
            "organizer": "Prof. Michele Ceriotti",
            "contact": "Prof. Michele Ceriotti",
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            "id": 72769,
            "title": "Locality-Aware and Recoverable Synchronization Primitives",
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            "start_date": "2026-12-16",
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            "description": "<p>Thesis Director: Prof. S. Kashyap,<br>\r\nComputer and Communication Sciences doctoral program<br>\r\nThesis Nr. 11376<br>\r\n<br>\r\nTo take part in the public defense, please contact directly the speaker</p>",
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                "id": 12,
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            "id": 72770,
            "title": "Exploiting Structure in Nonconvex Stochastic Optimization: Auxiliary Information, Second-Order Methods, and Compositional Objectives",
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            "start_date": "2026-10-16",
            "end_date": "2026-10-16",
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            "description": "<p>Thesis Director: Prof. M. Jaggi,<br>\r\nComputer and Communication Sciences doctoral program<br>\r\nThesis Nr. 10949<br>\r\n<br>\r\nTo take part in the public defense, please contact directly the speaker</p>",
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            "creation_date": "2026-10-05T16:13:36",
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            "id": 72771,
            "title": "Document selection: Do robots dream of working alongside humans?",
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            "url_online_room": "",
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            "organizer": "EPFL Library",
            "contact": "<a href=\"mailto:[email protected]\">[email protected]</a>",
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            "keywords": "robots",
            "file": null,
            "icalendar_url": "https://memento.epfl.ch/event/export/122072/",
            "category": {
                "id": 5,
                "code": "EXPO",
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