retrieve:
Return the details about the given Event id.

list:
List all Event objects.

GET /api/v1/events/?format=api&offset=60&ordering=-event__speaker
HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

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            "id": 71930,
            "title": "Generative models for black-box optimization of complex objectives",
            "slug": "generative-models-for-black-box-optimization-of-co",
            "event_url": "https://memento.epfl.ch/event/generative-models-for-black-box-optimization-of-co",
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            "lang": "en",
            "start_date": "2026-07-09",
            "end_date": "2026-07-09",
            "start_time": "10:00:00",
            "end_time": "12:00:00",
            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Nicolas Flammarion<br>\r\nThesis advisor: Prof. Pascal Fua<br>\r\nCo-examiner: Prof. Alexander Mathis<br>\r\n<br>\r\n<u>Abstract</u><br>\r\nMany high-impact problems in science and engineering reduce to optimizing a complex objective, where each evaluation is costly and often unreliable: physical experiments fail, simulators diverge, and stochastic systems return noisy feedback. Classical black-box optimizers struggle in this regime, where evaluation budgets are tight and the feasible region is hard to characterize a priori. Our work investigates how the statistical expressivity of modern generative samplers can be leveraged in black-box optimization. We propose that these samplers enable efficient navigation of feasible sets and optimization of geometrically complex objectives under limited, unreliable observations. <br>\r\n<br>\r\nTwo complementary lines of work have already been studied. The main one, SPARROW, demonstrated that a sampler-driven optimizer can substantially reduce the number of function calls needed to maximize complex and unreliable objectives. A parallel line established that samplers can be steered to satisfy hard constraints without sacrificing the statistical diversity of the generated samples. Because SPARROW imposes minimal assumptions on the underlying sampler, these constrained samplers can drive it directly, opening a path to constrained black-box optimization.<br>\r\n<br>\r\nBuilding on these results, two directions are planned. The first is theoretical: a deeper mathematical study of sampler-driven optimization, drawing on the broader literature on sample-efficient black-box optimization, in order to derive stronger guarantees and more efficient algorithms. The second is applied: applying the methods to concrete domains and exploiting the structure those domains expose to sharpen performance beyond what a pure black-box treatment can achieve.<br>\r\n<br>\r\nThe applications of low-budget black-box optimization are diverse, including engineering design (e.g. aerodynamic shape optimization, mechanical components), scientific discovery (e.g. protein and molecule design under synthesizability and stability constraints), and the safety and security of opaque ML systems (e.g. query-efficient adversarial probing, red-teaming of black-box models).<br>\r\n<br>\r\n<u>Selected papers</u><br>\r\n ",
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            "creation_date": "2026-05-22T12:53:32",
            "last_modification_date": "2026-05-22T13:09:04",
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            "place_and_room": "BC 333",
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            "spoken_languages": [],
            "speaker": "Edouard Dufour",
            "organizer": "",
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        {
            "id": 71172,
            "title": "Anisotropic Inviscid Limit for the Navier-Stokes Equations with Transport Noise Between Two Plates",
            "slug": "anisotropic-inviscid-limit-for-the-navier-stokes-e",
            "event_url": "https://memento.epfl.ch/event/anisotropic-inviscid-limit-for-the-navier-stokes-e",
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            "lang": "en",
            "start_date": "2026-03-24",
            "end_date": "2026-05-25",
            "start_time": "16:30:00",
            "end_time": "17:30:00",
            "description": "<p>We investigate an anisotropic vanishing viscosity limit of the 3D stochastic Navier-Stokes equations posed between two horizontal plates, with Dirichlet no-slip boundary condition. The turbulent viscosity is split into horizontal and vertical directions, each of which approaches zero at a different rate. The underlying Cylindrical Brownian Motion driving our transport-stretching noise is decomposed into horizontal and vertical components, which are scaled by the square root of the respective directional viscosities. We prove that if the ratio of the vertical to horizontal viscosities approaches zero, then there exists a sequence of weak martingale solutions convergent to the strong solution of the deterministic Euler equation on its lifetime of existence. A particular challenge is that the anisotropic scaling ruins the divergence-free property for the spatial correlation functions of the noise.</p>",
            "image_description": "",
            "creation_date": "2026-02-18T12:59:24",
            "last_modification_date": "2026-03-22T19:58:56",
            "link_label": "Working-Reading Seminar",
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            "speaker": "Dr Daniel Goodair (EPFL)",
            "organizer": "<a href=\"https://people.epfl.ch/xue-mei.li\">Xue-Mei Li</a>",
            "contact": "<a href=\"https://people.epfl.ch/xue-mei.li\">Xue-Mei Li</a>",
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            "keywords": "Working-Reading seminar, Probability, stochastic analysis",
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        {
            "id": 69172,
            "title": "Dynamic Decisions and Temporal Frictions in On-Demand Mobility",
            "slug": "dynamic-decisions-and-temporal-frictions-in-on-d-2",
            "event_url": "https://memento.epfl.ch/event/dynamic-decisions-and-temporal-frictions-in-on-d-2",
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            "start_date": "2026-06-22",
            "end_date": "2026-06-22",
            "start_time": "10:30:00",
            "end_time": "11:30:00",
            "description": "<strong>ABSTRACT</strong><br>\r\nApp-based, on-demand transport systems have transformed urban mobility by enabling real-time coordination between vehicles and users. Yet behind this flexibility lies a fundamental tension: operational decisions that appear beneficial in the present can deteriorate the future state of the system. This talk explores these temporal frictions in assigned on-demand systems—where passengers and vehicles are matched before meeting—and how they shape the performance and reliability of shared mobility services.<br>\r\nFirst, we show how assignment decisions evolve through what we term the Increasing Gap Dynamics, a negative feedback cycle through which vehicles progressively concentrate in space, reducing system efficiency over time. We then examine how a tension between efficiency and reliability emerges: while highly dynamic operations can improve short-term efficiency and flexibility, they may also increase unreliability by exposing passengers to future, uncertain requests. We discuss how this tension manifests both in routing decisions and in the selection of pickup and dropoff locations, and show how carefully designed anticipatory techniques can help reconcile these competing objectives.<br>\r\n<br>\r\n <br>\r\n<strong>SHORT BIO</strong><br>\r\nDr Andrés Fielbaum is a Senior Lecturer in Transport Engineering at the University of Sydney and a DECRA Fellow of the Australian Research Council. His work lies at the intersection of transport systems and applied mathematics, with a focus on public transport, on-demand mobility, and developing analytical models to inform system design and operations. He completed his PhD in Systems Engineering at Universidad de Chile in 2019 and held a postdoctoral appointment at TU Delft between 2019-2023.<br>\r\n<br>\r\nDr Fielbaum has authored over 35 peer-reviewed journal articles in leading outlets and serves as an Associate Editor for Transportation Research Part B: Methodological,  and the Journal of Intelligent Transportation Systems.<br>\r\n<br>\r\n ",
            "image_description": "",
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            "speaker": "Dr Andrés Fielbaum",
            "organizer": "HOMES - Prof. Kenan Zhang",
            "contact": "Prof. Kenan Zhang<br>\r\nAdministrative: Mélanie Thuillard",
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        {
            "id": 70782,
            "title": "“Mechanisms driving the rapid evolution of genomes”",
            "slug": "mechanisms-driving-the-rapid-evolution-of-genomes",
            "event_url": "https://memento.epfl.ch/event/mechanisms-driving-the-rapid-evolution-of-genomes",
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            "start_date": "2026-06-04",
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            "start_time": "12:15:00",
            "end_time": "13:15:00",
            "description": "<strong>A Lola and John Grace Distinguished Lecture in Cancer Research</strong>\r\n<div class=\"elementor-element elementor-element-98cefd8 elementor-widget elementor-widget-heading\">\r\n<div class=\"elementor-widget-container\">\r\n<div class=\"is-active tabs-panel\">\r\n<div class=\"bundle--tabs paragraph paragraph--type--tabs paragraph--view-mode--default\">\r\n<div class=\"row\">\r\n<div class=\"columns small-12\">\r\n<div class=\"body field__item\">David Pellman, M.D. is the Margaret M. Dyson Professor of Pediatric Oncology at the Dana-Farber Cancer Institute, a Professor of Cell Biology at Harvard Medical School, an Investigator of the Howard Hughes Medical Institute, and the Associate Director for Basic Science at the Dana-Farber/Harvard Cancer Center.  He received his undergraduate and medical degrees from the University of Chicago.  During medical school, he did research at the Rockefeller University.  His postdoctoral fellowship was at the Whitehead Institute/Massachusetts Institute of Technology.<br>\r\n<br>\r\nThe Pellman Lab works on the mechanism of cell division and how certain cell division errors drive rapid genome evolution.  His research has contributed to the understanding of cell division, the origin of cell division errors in cancer, how cell division errors drive genetic instability and the mechanisms driving rapid evolution of cancer genomes. Our contributions include (1) the co-discovery of formin-dependent actin nucleation and a mechanism for spindle positioning during asymmetric cell division, (2) discoveries establishing that whole genome duplication can drive tumorigenesis, alter cell physiology, and accelerate evolutionary adaptation, and (3) the discovery of a mechanism explaining chromothripsis, a mutational process that generates rapid karyotype evolution in cancer and congenital disease.</div>\r\n</div>\r\n</div>\r\n</div>\r\n</div>\r\n</div>\r\n</div>\r\n\r\n<div class=\"elementor-element elementor-element-e552a16 elementor-widget elementor-widget-text-editor\">\r\n<div class=\"elementor-widget-container\"> </div>\r\n</div>",
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            "id": 71746,
            "title": "Control Policy and Shape Co-Design",
            "slug": "control-policy-and-shape-co-design",
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            "start_date": "2026-06-15",
            "end_date": "2026-06-15",
            "start_time": "10:00:00",
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            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Mark Pauly<br>\r\nThesis advisor: Prof. Pascal Fua<br>\r\nCo-examiner: Prof. Colin Jones<br>\r\n<br>\r\n<u>Abstract</u><br>\r\ncoming soon<br>\r\n<br>\r\n<u>Selected papers</u>\r\n<ul>\r\n\t<li><em>Developing a Co-Design Framework for Hypersonic Vehicle Aerodynamics and Trajectory </em><a href=\"https://arc.aiaa.org/doi/10.2514/6.2024-0238\" title=\"https://arc.aiaa.org/doi/10.2514/6.2024-0238\">[link]</a></li>\r\n\t<li><em>Co-Design Optimisation of Morphing Topology and Control of Winged Drones </em><a href=\"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10611506\" title=\"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10611506\">[link]</a></li>\r\n\t<li><em>Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification </em><a href=\"https://pdf.sciencedirectassets.com/271897/1-s2.0-S1367578825X00031/1-s2.0-S1367578826000015/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjED8aCXVzLWVhc3QtMSJGMEQCIGWdZsRooNaRSE9Jvm42gFGma19EDUmA%2F6qh9vRVW79eAiBVOd6jClS4f9hZhP74JWxrBGX3h6wFijICbZSAIAGQRiqyBQgHEAUaDDA1OTAwMzU0Njg2NSIM%2F2qu0CW5XnqjqmccKo8Fayr5Tlv%2BM%2FslOsihSWoNqXNS8Pgnao%2FdAGPJo2VJcHgcd9rpFY8Bc3Sp%2FEMv3tklkQCEoAQN%2FKyZ4wzymNg%2Fh11UG9YphufDs3wh19sV5ZjoMolctx4w9%2BQunLXlmS2cd2G83Le2hXhCMgWwI1Vxo9j5TfP9ChdPn5OvaCfNFDG1jhuhFWpD7lOs%2F1oaVoSVof4iPSEFs8nh7mUSFeNZizYkGDGdg4qjQB49x8TPzCPhiguiYxjXYyUjDZReKclPXS8mHy4PrHBk5y1%2F371DbHLKPLa3NhYSpkhIH7EEyWhiTWwXY8S5vRWX%2F619m7LyFisF61Ojks6SxmFdZQCKJ4OX0vR45zRbJIcodzFWDSZR65HdAXWbOuXj9IpQED5EpC0Uw4ehRobahFmdwwPskJ8qPTJg8Uj2sQI2onqy5%2F9o86fk87qgZrHrfKezmNoG5dzpz7bd%2FIq26xJ94RDpsgntiimjhQABdSVugnPAckZ7r9QvFiUJ3wrLV3JmhIbFzMJaG%2F0kf%2FmNf%2Btx3DB01VFYRk3E32j7qkmuDeYKaIYyDRmsrMwUhoow3KbntpsK7x9Lr9CYijUIUMYftG7HFuzoWOUyB2rIxuQQtkEF9POY%2Btl5VQcA3nIJ2OIsCuaGyUYhRczKz66e9TUU1LVQGk4MuXeu5QQiMghj%2FuWRZr9Jtkz44eJsrdVhdyeggsOy7vj3ipLR3t5b%2F0YJ1HUPzaYjrsAtQIPdeS%2FvjAJt2XsfZXOHM5cf2pvWw9sKM62dqnZkePmVm9IdpnH5kJc%2BbSsYtTlX0NvTtYduGzjn622WgKEMqg6QoKGHVH1opHOgRCXmjeW0s9bZ%2BA8X87GZRpY6uGX%2BvTKdabrIo3wi5zDx5MvPBjqyAXiQqZ%2FuLieJ5LH3JrfhIjOcsc2QDriCkds7r2G25UPv96CCMpPmgWYyWf6yp1YXBRMqWZjCrmb1IyiF1bTHe%2FojoM3%2Ft3ECJ4Jp9TBAscMvymXYDsuNw%2BHBbZEy8%2FkKdBl%2FzTk4Vo9s%2Bsv62VoZDmBtIOnhXzwTNdAe46xv1ay1A%2Ffq61jSf45%2BAS2ifn%2FCXIw%2Bt8nP%2FuiP4d6xN3zFLlWu%2B%2BPF7hQsXS5bGSbgpiZ5SV0%3D&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Date=20260430T071347Z&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Expires=300&amp;X-Amz-Credential=ASIAQ3PHCVTY6G7L7JZ7%2F20260430%2Fus-east-1%2Fs3%2Faws4_request&amp;X-Amz-Signature=16b74f5ed082681d7b1166d729aac7886cc60232acd7e769ece224358496a9a2&amp;hash=b0e49fc41b616214d8d1147210f05c37d78da2a9936148b9faebbeb914e9b590&amp;host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&amp;pii=S1367578826000015&amp;tid=spdf-e5ddd93b-3eb0-4ff4-8c47-ebbcb2e2115a&amp;sid=4c9100ad4590a444994ad633e1000a43eccbgxrqb&amp;type=client&amp;tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&amp;rh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&amp;ua=19075a0152510400085f&amp;rr=9f44bef12e9d3b5e&amp;cc=ch\" title=\"https://pdf.sciencedirectassets.com/271897/1-s2.0-S1367578825X00031/1-s2.0-S1367578826000015/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjED8aCXVzLWVhc3QtMSJGMEQCIGWdZsRooNaRSE9Jvm42gFGma19EDUmA%2F6qh9vRVW79eAiBVOd6jClS4f9hZhP74JWxrBGX3h6wFijICbZSAIAGQRi\">[link]</a></li>\r\n</ul>",
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            "title": "MoRAT: Mathematics of Randomized linear Algebra Techniques - Summer School 2026",
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            "id": 70490,
            "title": "Manager : Pilotez les objectifs et accompagnez la progression de vos équipes",
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            "end_time": "17:00:00",
            "description": "<p>La performance en entreprise ne se mesure pas seulement aux résultats, mais à la manière dont les objectifs sont fixés, suivis et ajustés. Cette formation vous offre les outils essentiels pour gérer la performance de votre équipe avec précision et bienveillance.<br>\r\n<br>\r\nÀ travers des méthodes concrètes, des échanges en sous-groupes et des mises en situation, vous apprendrez à accompagner pertinemment votre collaborateur.trice durant tout le cycle de sa performance, de la fixation des objectifs à leur évaluation.<br>\r\n<br>\r\nA la fin de cette formation vous saurez comment rédiger des objectifs efficaces, réaliser des suivis motivants, donner des feedbacks constructifs et conduire des entretiens d’évaluation structurés. Rejoignez-nous pour maîtriser l'art de la gestion de la performance et accompagner vos collaborateurs.trices vers l’excellence !</p>",
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        {
            "id": 70582,
            "title": "Summer School on Numerical methods for Random Differential Models (NUMRAD) - June 2026",
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            "id": 71751,
            "title": "From Exploitation to Mitigation: Analyzing Memory Corruption Primitives to Harden the Linux Kernel",
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            "event_url": "https://memento.epfl.ch/event/from-exploitation-to-mitigation-analyzing-memory-c",
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            "start_date": "2026-06-23",
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            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. George Candea<br>\r\nThesis advisor: Prof. Mathias Payer<br>\r\nThesis co-advisor: Dr. Anil Kurmus<br>\r\nCo-examiner: Prof. Sanidhya Kashyap<br>\r\n<br>\r\n<u>Abstract</u><br>\r\ncoming soon<br>\r\n<br>\r\n<u>Selected papers</u><br>\r\ncoming soon",
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            "id": 71750,
            "title": "Efficiency Datalog Engine for Formal Research",
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