retrieve:
Return the details about the given Event id.

list:
List all Event objects.

GET /api/v1/events/?format=api&offset=80&ordering=-start_time
HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

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            "end_time": null,
            "description": "<p>Thesis Director: Prof. S. Carrara,<br>\r\nMicrosystems and Microelectronics doctoral program<br>\r\nThesis Nr. 12239<br>\r\n<br>\r\nTo take part in the public defense, please contact directly the speaker</p>",
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            "creation_date": "2026-10-26T10:52:11",
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            "id": 72959,
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            "description": "<p>Thesis Director: Prof. F. Stellacci,<br>\r\nMaterials Science and Engineering doctoral program<br>\r\nThesis Nr. 12065<br>\r\n<br>\r\nTo take part in the public defense, please contact directly the speaker</p>",
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            "creation_date": "2026-10-28T14:29:30",
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            "id": 73126,
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            "end_time": null,
            "description": "<p>Thesis Directors: Prof. Ph. Schwaller, Prof. J. Luterbacher<br>\r\nChemistry and Chemical Engineering doctoral program<br>\r\nThesis Nr. 12070<br>\r\n<br>\r\nTo take part in the public defense, please contact directly the speaker</p>",
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            "event_url": "https://memento.epfl.ch/event/strategies-in-pd-catalyzed-carboamination-of-uns-2",
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            "end_time": null,
            "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>",
            "image_description": "",
            "creation_date": "2026-10-02T17:11:58",
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            "id": 72799,
            "title": "ENAC General Assembly 2026",
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            "start_date": "2026-12-14",
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            "start_time": "16:30:00",
            "end_time": "18:00:00",
            "description": "<strong>Invitation to the ENAC Community</strong><br>\r\n<br>\r\nDear colleagues,<br>\r\nDear students,<br>\r\n<br>\r\nWe are pleased to invite you to the ENAC General Assembly. This will be an opportunity to meet and discuss the activities and achievements of the past year, as well as looking ahead to the future of our school together.<br>\r\nOur traditional Christmas apéritif will follow the General Assembly, starting at 18:15 at the Piano.\r\n<ul>\r\n\t<li>DATE: <strong>14 December 2026</strong></li>\r\n\t<li>TIME: <strong>16:30–18:00</strong></li>\r\n\t<li>LOCATION: <strong>CM 1 2 or <a class=\"text-link\" href=\"https://epfl.zoom.us/j/63945879576\" target=\"_blank\">online</a></strong></li>\r\n</ul>\r\nWe look forward to welcoming you. No registration is required.<br>\r\n<br>\r\nKind regards,<br>\r\nKatrin Beyer<br>\r\nDean",
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            "creation_date": "2026-10-07T23:11:07",
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            "id": 72862,
            "title": "EPFL Pre-NeurIPS 2026 Poster Session",
            "slug": "epfl-pre-neurips-2026-poster-session",
            "event_url": "https://memento.epfl.ch/event/epfl-pre-neurips-2026-poster-session",
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            "start_date": "2026-12-02",
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            "description": "<p>The ELLIS Lausanne Unit and the EPFL AI Center are pleased to host the <strong>EPFL Pre-NeurIPS 2026 Poster Session</strong>, an informal research gathering showcasing recent contributions from the EPFL community to <strong>NeurIPS 2026</strong> and other leading AI and machine learning conferences and workshops.<br>\r\n<br>\r\nThis event offers an opportunity to discover recent research from across EPFL's AI and machine learning community, exchange ideas, and connect with fellow researchers ahead of NeurIPS 2026. EPFL researchers will also have the opportunity to present their latest work during the poster session.<br>\r\n<br>\r\n<strong>Registration is required to attend the event. Attendance is open to all interested participants.</strong><br>\r\n<br>\r\n<strong>Poster presentations are reserved for work with at least one EPFL-affiliated author: [<a href=\"https://forms.cloud.microsoft/e/yUDwvkshfX\">Register and submit your poster HERE</a>]</strong><br>\r\n<br>\r\nThe session will primarily feature research accepted to <strong>NeurIPS 2026</strong>, with priority given to:\r\n</p><ol start=\"1\">\r\n\t<li><strong>NeurIPS 2026 main conference papers</strong>, including Oral and Spotlight presentations</li>\r\n\t<li><strong>NeurIPS 2026 Evaluations &amp; Datasets Track papers</strong></li>\r\n\t<li><strong>NeurIPS 2026 workshop papers</strong></li>\r\n\t<li><strong>Other recent peer-reviewed papers</strong> from leading AI and machine learning conferences and workshops, subject to space availability</li>\r\n</ol>\r\n<strong>Poster presenters will be responsible for printing their poster and bringing it to the venue. </strong>The session will provide an informal and welcoming setting to share research findings, engage in discussions, explore potential collaborations, and network over snacks, coffee, and refreshments.<br>\r\n<br>\r\n<strong>Confirmed posters (updated on a rolling basis)</strong><br>\r\nThe list of confirmed posters and presenters will be published and updated regularly as registrations are received.<br>\r\n<br>\r\n<strong>Programme</strong><br>\r\n16:00–16:30 — Arrival &amp; Poster Set-up<br>\r\n16:30–19:00 — Poster Session &amp; Networking<br>\r\n19:00 — Wrap-up<br>\r\n<br>\r\nThe list of confirmed posters below is updated on a rolling basis.\r\n<div>\r\n<ul>\r\n\t<li><strong>\"Beyond Accuracy: A Diagnostic Benchmark for Hypothesis-Driven Experiment Planning in LLM Agents\" </strong>- <em>Ryo Kuroki, Amin Mansouri, Philippe Schwaller</em></li>\r\n\t<li><strong>\"Matchability-Aware Conformal Prediction for Open-Ended Language Model Generation\" </strong>- <em>Ilia Azizi</em></li>\r\n\t<li><strong>\"Sequence-to-Sequence Multi-view Stereo\" </strong>- <em>Aoxiang Fan, Corentin Dumery, Nicolas Talabot, Pascal Fua</em></li>\r\n\t<li><strong><em>\"</em>Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping\" </strong>- <em>Hsun-Yu Kuo, El Mahdi Chayti, Patrik Reizinger, Wieland Brendel, Martin Jaggi</em></li>\r\n\t<li><strong>\"Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning\"</strong> - <em>Sayan Biswas, Antoine Boutet, Davide Frey, Romaric Gaudel, Rachid Guerraoui, Maxime Jacovella, Anne-Marie Kermarrec, Dimitri Lerévérend, François Taïani, Martijn de Vos</em></li>\r\n\t<li><strong><em>\"</em>MIAM: Modality Imbalance-Aware Masking for Multimodal Ecological Applications\"</strong> - <em>Robin Zbinden, Wesley Monteith-Finas, Gencer Sumbul, Nina van Tiel, Chiara Vanalli, Devis Tuia</em></li>\r\n\t<li><strong>\"Gradient Descent’s Last Iterate is Often (slightly) Suboptimal\" </strong>- <em>Guy Kornowski, Ohad Shamir</em></li>\r\n\t<li><strong>\"Graph Matching Relaxations and Amortization for Supervised Graph Prediction\" </strong>- <em>Federico Méndez, Paul Krzakala, Gabriel Melo, Charlotte Laclau, Rémi Flamary, Florence d’Alché-Buc</em></li>\r\n\t<li><strong>\"Learning Lean Feedback for Efficient Proof-Candidate Evaluation on GPUs\"</strong> - <em>Lazar Milikic, Etienne Bamas, Emmanuel Abbe, Viktor Kunčak</em></li>\r\n\t<li><strong>\"Stable and Scalable Probabilistic Numerical Solvers for Stiff and High-Dimensional ODEs\" </strong>- <em>Nathanael Bosch</em></li>\r\n\t<li><strong>\"Euclidean Fourier Neural Operators\" </strong>- <em>Nathanael Bosch, Niklas Frederik Schmitz, Michael F Herbst</em></li>\r\n\t<li><strong>\"Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning\" </strong>- <em>Yatin Dandi, Matteo Vilucchio, Luca Arnaboldi, Hugo Tabanelli, Florent Krzakala</em></li>\r\n\t<li><strong><em>\"</em>Geometric Latent Reasoning Induces Shorter Generations in LLMs\" </strong>- <em>Shashi Kumar*, Yacouba Kaloga*, Petr Motlicek, Ina Kodrasi, Andrea Cavallaro</em></li>\r\n</ul>\r\n</div>",
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            "contact": "<a href=\"https://people.epfl.ch/nicolas.machado?lang=en\">Nicolas Machado</a>",
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            "id": 72927,
            "title": "Label-free single-molecule detection and discrimination of microcystins using aerolysin nanopore",
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            "description": "<p>Thesis Directors: Prof. M. Dal Peraro, Prof. T. Kohn<br>\r\nBiotechnology and Bioengineering doctoral program<br>\r\nThesis Nr. 11744<br>\r\n<br>\r\nTo take part in the public defense, please contact directly the speaker</p>",
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