retrieve:
Return the details about the given Event id.

list:
List all Event objects.

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

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            "id": 72357,
            "title": "Materials for the Future",
            "slug": "materials-for-the-future",
            "event_url": "https://memento.epfl.ch/event/materials-for-the-future",
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            "start_date": "2026-10-09",
            "end_date": "2026-10-09",
            "start_time": "18:00:00",
            "end_time": "19:30:00",
            "description": "<p>In 2026, EPFL named Professor Konstantin Novoselov its first-ever EPFL Fellow, a newly created honorary title recognising scientific personalities of exceptional international stature.<br>\r\n<br>\r\nEPFL is delighted to host a public lecture to celebrate this nomination of one of the most influential physicists of our time.<br>\r\n<br>\r\nKonstantin Novoselov was awarded the Nobel Prize in Physics in 2010 and is globally renowned for his pioneering work on graphene and two-dimensional materials. By isolating graphene for the first time, a sheet of carbon just one atom thick, he opened an entirely new field of research, with profound implications for electronics, energy and quantum technologies.<br>\r\n<br>\r\n<em>Picture this: it was from the graphite of an ordinary pencil lead that this \"super-material\" was first isolated. Using nothing more than a piece of adhesive tape to peel away a layer a single atom thick, Konstantin Novoselov revealed to the world a material with extraordinary properties. A million times thinner than a human hair, 200 times stronger than steel and remarkably conductive, graphene pushes every limit. From the lead of your pencil to the batteries of tomorrow, join us to  discover how a deceptively simple gesture set off a genuine technological revolution.</em><br>\r\n<br>\r\nThis Campus Lecture offers an introduction to that revolution: how a two-dimensional material is transforming our understanding of matter and how today's fundamental discoveries become tomorrow's technologies.<br>\r\n<br>\r\n<strong>About Konstantin Novoselov</strong><br>\r\nKonstantin Novoselov is a physicist specialising in condensed matter and nanotechnology. He conducted his Nobel Prize–winning research at the University of Manchester, where, together with Professor Andre Geim, he isolated graphene for the first time. He has held academic positions in the United Kingdom as well as internationally and is currently based at the National University of Singapore. Widely recognised as a leading figure in the study and application of two-dimensional materials, he has received numerous honours for his contributions to science.</p>",
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            "id": 72700,
            "title": "The Case for Collaboration and a New Paradigm for Machine Learning",
            "slug": "the-case-for-collaboration-and-a-new-paradigm-for",
            "event_url": "https://memento.epfl.ch/event/the-case-for-collaboration-and-a-new-paradigm-for",
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            "start_date": "2026-11-11",
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            "description": "<p>Since 2006, the Erna Hamburger Prize is awarded by the EPFL-WISH Foundation (Women In Science and Humanities) and distinguishes influential, leading women scientists who are transforming their field and executing change.<br>\r\n<br>\r\nThe Erna Hamburger Prize 2026 will be awarded to Prof. Margo Seltzer. The award ceremony will be held on November 11th 2026 at 6:30 pm at SG 1138. On this occasion, the awardee will give a conference on the EPFL Campus. Don’t miss this exceptional opportunity to hear from one of the most influential figures in contemporary computer science.<br>\r\n<br>\r\n<strong>Conference abstract</strong><br>\r\n<em>It's 2026, and the answer to every problem today seems to be a multiple choice exercise of selecting answers from: AI, Agents, LLMs or machine learning. Is our future really just all about prompt engineering?</em><br>\r\n<br>\r\nAt a time when artificial intelligence, autonomous agents and large language models seem to be presented as the answer to every challenge, <strong>Margo Seltzer</strong> offers a thought-provoking perspective on the value of simplicity in computer science and machine learning. She will demonstrate how simpler and more transparent approaches can deliver performance comparable to that of the most sophisticated models, while providing greater interpretability, more efficient use of resources and a more natural way of addressing issues of fairness and privacy. The lecture will also explore a theme that lies at the heart of her career: <strong>collaboration</strong>. Drawing on her own experience of collaborative research, Margo Seltzer will explain how working together can broaden one’s impact, help overcome imposter syndrome, and enable individuals to discover and develop their unique strengths.<br>\r\n<br>\r\n<strong>About Prof. Margo Seltzer</strong><br>\r\n<a href=\"https://www.seltzer.com/margo\">Prof. Margo Seltzer</a> is Co-Head of the Department of Computer Science at the University of British Columbia (Vancouver), where she moved after two decades as a Full Professor at Harvard University. She earned her Ph.D. in Computer Science in 1992 from the University of California, Berkeley. A pioneering computer systems researcher, she is the recipient of numerous prestigious honors, including the 2023 ACM Athena Lecturer Award and the USENIX Lifetime Achievement Award. She is a Member of the National Academy of Engineering. Alongside her academic career, Margo Seltzer is an entrepreneur: she co-founded Sleepycat Software, maker of Berkeley DB, a widely used embedded database engine later acquired by Oracle. A recognized leader in her field, she is also a devoted mentor to young women in computer science and founded the \"Systers Lunch,\" the original diversity gathering at SOSP, the flagship conference of the systems research community.</p>",
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            "id": 72560,
            "title": "Information session - CAS in Resilience",
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            "lang": "en",
            "start_date": "2026-10-08",
            "end_date": "2026-10-08",
            "start_time": "12:30:00",
            "end_time": "13:00:00",
            "description": "<strong>Building the Foundations for Resilience</strong><br>\r\n<br>\r\nJoin our online information session to get to know the EPFL executive program for in Resilience (CAS in Resilient Value Chain Management) with Program Director Ms. Elaine Moran.<br>\r\nLearn how this program offers a deep understanding of the complete value chain on a strategic, financial, operational and human level.<br>\r\n<br>\r\n<strong>&gt; <a href=\"https://epfl.zoom.us/meeting/register/Y-0y7tXSSP-V5mqlCyJZnQ\">Register here</a></strong><br>\r\n<br>\r\n<br>\r\n<br>\r\n+ info &amp; agenda: <a contenteditable=\"false\" href=\"https://go.epfl.ch/srvc-infosessions\" title=\"https://go.epfl.ch/srvc-infosessions\">go.epfl.ch/srvc-infosessions</a>",
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            "id": 72775,
            "title": "Information session - CAS in Technology",
            "slug": "information-session-cas-in-technology-2",
            "event_url": "https://memento.epfl.ch/event/information-session-cas-in-technology-2",
            "visual_url": "https://memento.epfl.ch/image/33991/200x112.jpg",
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            "lang": "en",
            "start_date": "2026-11-17",
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            "start_time": "18:00:00",
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            "description": "<p>Join our online information to get to know the <strong>EPFL SRVC executive program for Transforming Business with AI &amp; Data Tech (CAS in Value Chain Data Technologies)</strong> with Executive Director Ms. Elaine Moran.<br>\r\n<br>\r\nLearn how this program empowers professionals to drive sustainable innovation with AI, IoT &amp; Digital Twins - and get all your questions answered.<br>\r\n<br>\r\n<strong>&gt; <a href=\"https://epfl.zoom.us/meeting/register/Kz6P8fbyR-qii_938ml8gQ\">Register here</a></strong></p>",
            "image_description": "",
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            "spoken_languages": [
                "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api"
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            "speaker": "- Elaine Moran, <em>SRVC Executive Director</em>",
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            "title": "Information session - CAS in Technology",
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            "description": "<p>Join our online information to get to know the <strong>EPFL SRVC executive program for Transforming Business with AI &amp; Data Tech (CAS in Value Chain Data Technologies)</strong> with Executive Director Ms. Elaine Moran.<br>\r\n<br>\r\nLearn how this program empowers professionals to drive sustainable innovation with AI, IoT &amp; Digital Twins - and get all your questions answered.<br>\r\n<br>\r\n<strong>&gt; <a href=\"https://epfl.zoom.us/meeting/register/FPwsHW4-QTSmN8kUVse2Mw\">Register here</a></strong></p>",
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        {
            "id": 72673,
            "title": "lunch&LEARN | Access Timing as Scaffolding: Does the timing of GenAI access matter?",
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            "event_url": "https://memento.epfl.ch/event/lunchlearn-access-timing-as-scaffolding-does-the-3",
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            "lang": "en",
            "start_date": "2026-10-13",
            "end_date": "2026-10-13",
            "start_time": "12:15:00",
            "end_time": "13:00:00",
            "description": "<p>Generative AI has become a part of students' everyday working life. If used without guidance, GenAI can cause over-reliance and leave students with a distorted sense of what they have actually learned. Most research and practice have focused on how we can scaffold students' GenAI use, for example through prompts, guardrails, or redesigned tasks. This talk asks a simpler question that has received far less attention. When should students be allowed to use AI so that it helps their learning?<br>\r\n<br>\r\nIn this lunch&amp;LEARN session, Janne Rotter turns to a question that's been largely overlooked: not how, but when should students be allowed to use AI for it to actually support learning? Janne will present findings from a study of 105 university students comparing three conditions: unrestricted AI access, no access at all, and a \"timed\" condition where students could only turn to AI after first attempting the task on their own.<br>\r\n<br>\r\nDrawing on multiple pedagogical theories to build a decision rule for when access should open up, the results show that students in the timed condition outperformed those with unrestricted access on an objective post-test, and had a more accurate sense of their own understanding, without an explicit metacognitive prompting. Compared to withholding AI entirely, timed access also seemed to reduce task errors and time spent on task.<br>\r\n<br>\r\nJoin us to learn more about the topic and engage in further discussion. <br>\r\n </p>",
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            "creation_date": "2026-09-24T15:53:46",
            "last_modification_date": "2026-09-28T15:37:37",
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            "speaker": "<a href=\"https://people.epfl.ch/janne.rotter?lang=en\">Janne Rotter</a>",
            "organizer": "<a href=\"https://learn.epfl.ch/en/homepage/\">Center LEARN</a>",
            "contact": "<a href=\"mailto:[email protected]\">[email protected]</a>",
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        {
            "id": 72760,
            "title": "AI Center Seminar - AI Fundamentals series - Ricardo Olmedo - \"Speedrunning SWE-bench, or the $1,000 coding agent\"",
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            "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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