{"count":203,"next":"https://memento.epfl.ch/api/v1/events/?format=json&limit=10&offset=170&ordering=event__is_internal","previous":"https://memento.epfl.ch/api/v1/events/?format=json&limit=10&offset=150&ordering=event__is_internal","results":[{"id":71922,"title":"U.S. CDMRP – Alzheimer's Research Program | Research Funding","slug":"us-cdmrp-alzheimer-s-research-program-research-fun","event_url":"https://memento.epfl.ch/event/us-cdmrp-alzheimer-s-research-program-research-fun","visual_url":"https://memento.epfl.ch/image/33239/200x112.jpg","visual_large_url":"https://memento.epfl.ch/image/33239/720x405.jpg","visual_maxsize_url":"https://memento.epfl.ch/image/33239/max-size.jpg","lang":"en","start_date":"2026-06-22","end_date":"2026-06-22","start_time":null,"end_time":null,"description":"<strong>Aim</strong>: Through the Congressionally Directed Medical Research Program (CDMRP), the Department of Defense is announcing the 2026 <strong>Alzheimer's Research Program (AZRP) </strong>Awards. Several award programs are available:\r\n<ul>\r\n\t<li>The <strong>Transforming Care Award </strong>aims to support well-designed, non-incremental clinical research that develops and implements interventions, innovations, and solutions to address critical challenges in dementia care. Projects should lead to improved quality of life, reduced caregiver burden and stress, and enhanced care delivery and outcomes for individuals living with AD/ADRD.</li>\r\n\t<li>The <strong>Transforming Diagnosis Award </strong>supports non-incremental, solution-oriented research that addresses critical barriers to timely and accurate diagnosis, disease monitoring, and prognosis in AD/ADRD. Projects should demonstrate clear potential to improve accessibility, clinical implementation, and the reliability of diagnostic and prognostic approaches.</li>\r\n\t<li>The <strong>Transforming Research Award </strong>supports non-incremental, high-impact research that aims to drive transformative advances in reducing risk and preventing the development of AD/ADRD. Projects should demonstrate strong potential to accelerate prevention strategies and deliver meaningful improvements in patient outcomes and quality of life.<strong> </strong>All projects must respond to one of the focus areas listed <a href=\"https://ebrap.org/eBRAP/public/ViewFile.htm?fileId=36241238&amp;fileType=pdf\">here</a>.</li>\r\n</ul>\r\n<strong>Funding</strong>:\r\n\r\n<ul>\r\n\t<li>Transforming Care Award: $1.6M - $1.9M</li>\r\n\t<li>Transforming Diagnosis Award: $1.5M - $1.7M</li>\r\n\t<li>Transforming Research Award: max. $1M</li>\r\n</ul>\r\n<br>\r\n<sub>Note: Cost caps are <strong>total costs (direct plus indirect costs). </strong>In accordance with EPFL guidelines, budgets should include a 20% overhead, and the total budget must remain within the grant’s maximum allowable funding</sub><br>\r\n<br>\r\n<br>\r\n<strong>Duration</strong>:   \r\n\r\n<ul>\r\n\t<li>Transforming Care Award: 4 years</li>\r\n\t<li>Transforming Diagnosis Award: 4 years</li>\r\n\t<li>Transforming Research Award: 3 years</li>\r\n</ul>\r\n<strong>Eligibility: </strong>Applicants from international organizations or institutions are eligible to apply, and there are no citizenship restrictions. Program-specific seniority levels and eligibility criteria are further defined within each award mechanism <a href=\"https://ebrap.org/eBRAP/public/ViewFile.htm?fileId=36243245&amp;fileType=pdf\">here</a>.<br>\r\n<br>\r\n<strong>How to Apply</strong>: Pre-applications are submitted through the <a href=\"https://ebrap.org/eBRAP/public/index.htm\">eBRAP platform</a>. They should contain contact information, a letter of intent, and a list of collaborators and key personnel as suggested on the platform. Full applications will be made via a Grants.gov workspace.<br>\r\n<br>\r\n<strong>Pre-Application Deadline: 22 June 2026, </strong>5:00 p.m. Eastern time<br>\r\n<strong>Full Application Deadline</strong>: <strong>24 September 2026</strong>, 11:59 p.m Eastern Time<br>\r\n<br>\r\n<strong>Further information:</strong>\r\n\r\n<ul>\r\n\t<li>To find the full program announcements and to start your pre-application see <a href=\"https://ebrap.org/eBRAP/public/ProgramFY.htm?programFYId=719106\">here</a></li>\r\n\t<li>A useful summary of each call can be found <a href=\"https://ebrap.org/eBRAP/public/ViewFile.htm?fileId=36243245&amp;fileType=pdf\">here</a></li>\r\n\t<li>For questions about the eBRAP or Grants.gov platforms, contact the <a href=\"mailto:research@epfl.ch\">Research Office</a></li>\r\n\t<li>Grants.gov Funding Opportunity Numbers:\r\n\t<ul>\r\n\t\t<li>Transforming Care Award: <a href=\"https://ebrap.org/eBRAP/public/ViewFile.htm?fileId=36241229&amp;fileType=pdf\">HT942526AZRPTRCA</a></li>\r\n\t\t<li>Transforming Diagnosis Award: <a href=\"https://ebrap.org/eBRAP/public/ViewFile.htm?fileId=36241231&amp;fileType=pdf\">HT942526AZRPTRDA</a></li>\r\n\t\t<li>Transforming Research Award: <a href=\"https://ebrap.org/eBRAP/public/ViewFile.htm?fileId=36241238&amp;fileType=pdf\">HT942526AZRPTRRA</a></li>\r\n\t</ul>\r\n\t</li>\r\n</ul>","image_description":"","creation_date":"2026-05-21T16:12:42","last_modification_date":"2026-05-21T16:12:42","link_label":"","link_url":"","canceled":"False","cancel_reason":"","place_and_room":"","url_place_and_room":"","url_online_room":"","spoken_languages":["https://memento.epfl.ch/api/v1/spoken_languages/2/?format=json"],"speaker":"","organizer":"","contact":"<a href=\"mailto:research@epfl.ch\">Research Office</a>","is_internal":"False","theme":"","vulgarization":{"id":1,"fr_label":"Tout public","en_label":"General public"},"registration":{"id":3,"fr_label":"Entrée libre","en_label":"Free"},"keywords":"","file":null,"icalendar_url":"https://memento.epfl.ch/event/export/120829/","category":{"id":16,"code":"PROP","fr_label":"Appel à proposition","en_label":"Call for proposal","activated":true},"academic_calendar_category":null,"domains":[],"mementos":["https://memento.epfl.ch/api/v1/mementos/9/?format=json","https://memento.epfl.ch/api/v1/mementos/140/?format=json"]},{"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","visual_url":null,"visual_large_url":null,"visual_maxsize_url":null,"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 ","image_description":"","creation_date":"2026-05-22T12:53:32","last_modification_date":"2026-05-22T13:09:04","link_label":"","link_url":"","canceled":"False","cancel_reason":"","place_and_room":"BC 333","url_place_and_room":"https://plan.epfl.ch/?room==BC%20333","url_online_room":"","spoken_languages":[],"speaker":"Edouard Dufour","organizer":"","contact":"edic@epfl.ch","is_internal":"False","theme":"","vulgarization":{"id":1,"fr_label":"Tout public","en_label":"General public"},"registration":{"id":3,"fr_label":"Entrée libre","en_label":"Free"},"keywords":"EDIC candidacy exam","file":null,"icalendar_url":"https://memento.epfl.ch/event/export/120837/","category":{"id":1,"code":"CONF","fr_label":"Conférences - Séminaires","en_label":"Conferences - Seminars","activated":true},"academic_calendar_category":null,"domains":[],"mementos":["https://memento.epfl.ch/api/v1/mementos/48/?format=json","https://memento.epfl.ch/api/v1/mementos/6/?format=json","https://memento.epfl.ch/api/v1/mementos/27/?format=json"]},{"id":71931,"title":"Access Bit Scanning for Page Replacement Policies","slug":"access-bit-scanning-for-page-replacement-policies","event_url":"https://memento.epfl.ch/event/access-bit-scanning-for-page-replacement-policies","visual_url":null,"visual_large_url":null,"visual_maxsize_url":null,"lang":"en","start_date":"2026-06-22","end_date":"2026-06-22","start_time":"10:00:00","end_time":"12:00:00","description":"<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Thomas Bourgeat<br>\r\nThesis advisor: Prof. Babak Falsafi<br>\r\nCo-examiner: Prof. Sanidhya Kashyap<br>\r\n<br>\r\n<u>Abstract</u><br>\r\nModern workloads with memory footprints ranging from hundreds of gigabytes to several terabytes have become widespread over the last decade. With memory accounting for nearly 50% of server costs in modern data centres, provisioning large DRAM capacities significantly increases total cost of ownership (TCO). Tiered memory architectures combine memories with different capacities, latencies, and cost characteristics to improve cost efficiency while satisfying application performance requirements. Application performance in these architectures is highly sensitive to page placement decisions. Consequently, making informed placement decisions requires precise tracking of data access patterns and accurate identification of hot pages.<br>\r\n<br>\r\nExisting operating systems commonly rely on hardware-maintained access bits in page table entries to estimate memory access behaviour and identify hot data regions. They periodically scan the application's entire virtual address space to construct workload access profiles. However, linear scanning of page table access bits does not scale for modern applications with terabyte-scale memory footprints. Since scanning overhead scales with the memory footprint, a single full scan can take several seconds for workloads with large memory footprints. Reducing scanning frequency lowers monitoring overhead but also decreases profiling accuracy and responsiveness to changing access patterns, resulting in a fundamental tradeoff between profiling accuracy and monitoring overhead. Therefore, we need scalable, low-overhead mechanisms for access monitoring in next-generation memory management systems.<br>\r\n<br>\r\nIn this talk, I will discuss recent approaches to scalable memory access profiling in large-memory systems. Specifically, I will present works that use techniques such as adaptive sampling, region-based monitoring, and hierarchical profiling of page table trees to reduce and bound scanning overhead. <br>\r\n<br>\r\n<u>Selected papers</u><br>\r\ncoming soon","image_description":"","creation_date":"2026-05-22T13:08:29","last_modification_date":"2026-05-22T13:18:11","link_label":"","link_url":"","canceled":"False","cancel_reason":"","place_and_room":"BC 010","url_place_and_room":"https://plan.epfl.ch/?room==BC%20010","url_online_room":"","spoken_languages":[],"speaker":"<br>\r\nArunkrishna Annai Madalam Sivasubramanian  ","organizer":"","contact":"edic@epfl.ch","is_internal":"False","theme":"","vulgarization":{"id":1,"fr_label":"Tout public","en_label":"General public"},"registration":{"id":3,"fr_label":"Entrée libre","en_label":"Free"},"keywords":"EDIC candidacy exam","file":null,"icalendar_url":"https://memento.epfl.ch/event/export/120838/","category":{"id":1,"code":"CONF","fr_label":"Conférences - Séminaires","en_label":"Conferences - Seminars","activated":true},"academic_calendar_category":null,"domains":[],"mementos":["https://memento.epfl.ch/api/v1/mementos/48/?format=json","https://memento.epfl.ch/api/v1/mementos/6/?format=json","https://memento.epfl.ch/api/v1/mementos/27/?format=json"]},{"id":71932,"title":"Failure-Aware LLM Serving for Agentic Workloads","slug":"failure-aware-llm-serving-for-agentic-workloads","event_url":"https://memento.epfl.ch/event/failure-aware-llm-serving-for-agentic-workloads","visual_url":null,"visual_large_url":null,"visual_maxsize_url":null,"lang":"en","start_date":"2026-06-17","end_date":"2026-06-17","start_time":"09:00:00","end_time":"11:00:00","description":"<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Anastasia Ailamaki<br>\r\nThesis advisor: Prof. Anne-Marie Kermarrec<br>\r\nCo-examiner: Prof. Robert West<br>\r\n<br>\r\n<u>Abstract</u><br>\r\n<br>\r\n<br>\r\n<u>Selected papers</u>\r\n<ul>\r\n\t<li>ReAct (<a href=\"https://arxiv.org/pdf/2210.03629\" target=\"_blank\">https://arxiv.org/pdf/2210.03629</a>)</li>\r\n\t<li>Agentix (<a href=\"https://www.usenix.org/system/files/nsdi26-luo.pdf\" target=\"_blank\">https://www.usenix.org/system/files/nsdi26-luo.pdf</a>)</li>\r\n\t<li>Why do multi-agent systems fail? (<a href=\"https://arxiv.org/pdf/2503.13657\" target=\"_blank\">https://arxiv.org/pdf/2503.13657</a>)</li>\r\n</ul>","image_description":"","creation_date":"2026-05-22T13:11:09","last_modification_date":"2026-05-22T13:12:05","link_label":"","link_url":"","canceled":"False","cancel_reason":"","place_and_room":"BC 133","url_place_and_room":"https://plan.epfl.ch/?room==BC%20133","url_online_room":"","spoken_languages":[],"speaker":"Palak","organizer":"","contact":"edic@epfl.ch","is_internal":"False","theme":"","vulgarization":{"id":1,"fr_label":"Tout public","en_label":"General public"},"registration":{"id":3,"fr_label":"Entrée libre","en_label":"Free"},"keywords":"EDIC candidacy exam","file":null,"icalendar_url":"https://memento.epfl.ch/event/export/120839/","category":{"id":1,"code":"CONF","fr_label":"Conférences - Séminaires","en_label":"Conferences - Seminars","activated":true},"academic_calendar_category":null,"domains":[],"mementos":["https://memento.epfl.ch/api/v1/mementos/48/?format=json","https://memento.epfl.ch/api/v1/mementos/6/?format=json","https://memento.epfl.ch/api/v1/mementos/27/?format=json"]},{"id":71933,"title":"Controlling and Guiding Generative Models for Three-Dimensional Shape Optimization","slug":"controlling-and-guiding-generative-models-for-thre","event_url":"https://memento.epfl.ch/event/controlling-and-guiding-generative-models-for-thre","visual_url":null,"visual_large_url":null,"visual_maxsize_url":null,"lang":"en","start_date":"2026-07-07","end_date":"2026-07-07","start_time":"11:00:00","end_time":"13:00:00","description":"<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Martin Rajman<br>\r\nThesis advisor: Prof. Pascal Fua<br>\r\nCo-examiner: Prof. Mark Pauly<br>\r\n<br>\r\n<u>Abstract</u><br>\r\nOptimizing 3D shapes within the latent spaces of deep generative models is fundamental to computer assisted engineering, yet remains prone to a critical failure mode we term manifold drift: the tendency of gradient-based optimization to move latent vectors away from the manifold of valid shapes. This problem is exacerbated in state-of-the-art 3D shape generative models that operate in increasingly high-dimensional latent spaces where valid shapes occupy a vanishingly small fraction of the full space. Existing mitigation strategies, including latent regularization and flow-matching approaches, either sacrifice expressiveness, demand a difficult trade-off between objective guidance and generative fidelity that remains prone to manifold drift, or are computationally infeasible to scale to modern, large-capacity 3D shape models. We introduce a novel optimizer-corrector framework that alternates between gradient steps for objective minimization and guided flow matching to drive the latent state back to the valid shape manifold. By decoupling objective minimization from flow-based correction, optimizing freely and correcting strictly, this alternating design avoids inherent trade-offs, preserving geometric validity without sacrificing expressiveness while remaining computationally feasible on modern 3D shape models. We demonstrate its effectiveness across generative priors of varying complexity, from simple vector latent spaces to large-scale architectures across a variety of downstream optimization tasks, including aerodynamic drag reduction and object compliance optimization.<br>\r\n<br>\r\n<u>Selected papers</u><br>\r\n ","image_description":"","creation_date":"2026-05-22T13:14:54","last_modification_date":"2026-05-22T13:14:54","link_label":"","link_url":"","canceled":"False","cancel_reason":"","place_and_room":"BC 333","url_place_and_room":"https://plan.epfl.ch/?room==BC%20333","url_online_room":"","spoken_languages":[],"speaker":"Emilien Seiler","organizer":"","contact":"edic@epfl.ch","is_internal":"False","theme":"","vulgarization":{"id":1,"fr_label":"Tout public","en_label":"General public"},"registration":{"id":3,"fr_label":"Entrée libre","en_label":"Free"},"keywords":"EDIC candidacy exam","file":null,"icalendar_url":"https://memento.epfl.ch/event/export/120840/","category":{"id":1,"code":"CONF","fr_label":"Conférences - Séminaires","en_label":"Conferences - Seminars","activated":true},"academic_calendar_category":null,"domains":[],"mementos":["https://memento.epfl.ch/api/v1/mementos/48/?format=json","https://memento.epfl.ch/api/v1/mementos/6/?format=json","https://memento.epfl.ch/api/v1/mementos/27/?format=json"]},{"id":71934,"title":"Future of Health Grant","slug":"future-of-health-grant-3","event_url":"https://memento.epfl.ch/event/future-of-health-grant-3","visual_url":"https://memento.epfl.ch/image/33245/200x112.jpg","visual_large_url":"https://memento.epfl.ch/image/33245/720x405.jpg","visual_maxsize_url":"https://memento.epfl.ch/image/33245/max-size.jpg","lang":"en","start_date":"2026-07-31","end_date":"2026-07-31","start_time":"11:00:00","end_time":"12:00:00","description":"<p>The <strong>Future of Health Grant</strong> is a support program dedicated to accelerating early‑stage digital health startups developing solutions relevant to the Swiss healthcare system.<br>\r\nCreated by CSS and the EPFL Innovation Park, it combines equity‑free funding, expert guidance and access to healthcare stakeholders to help founders progress from prototype to real‑world validation.<br>\r\n<br>\r\n<strong>For whom</strong>\r\n</p><ul>\r\n\t<li>For early‑stage digital health startups developing solutions that can be applied within the Swiss healthcare system.</li>\r\n\t<li>Eligible teams may be based in Switzerland or abroad, provided their innovation is relevant to Swiss clinical, operational or preventive care contexts.</li>\r\n\t<li>The program targets founders at the prototype, MVP or early validation stage.</li>\r\n</ul>\r\n<strong>What you gain</strong>\r\n\r\n<ul>\r\n\t<li><strong>equity‑free financial support</strong> at three possible levels (CHF 10k, 30k or 50k), depending on project maturity and needs</li>\r\n\t<li>teams benefit from <strong>structured coaching</strong>, <strong>industry mentoring</strong></li>\r\n\t<li>access to <strong>paid specialists</strong> through the “CxO as a Service” model</li>\r\n\t<li>The program also facilitates <strong>real‑world validation</strong> opportunities with healthcare professionals, hospitals, insurers and ecosystem partners in Switzerland</li>\r\n</ul>\r\n<strong>How it works</strong><br>\r\n<br>\r\nThe program operates fully virtually and follows a milestone‑based approach. Selected startups receive regular guidance from experts in digital health, business development and regulatory pathways. Each team works toward defined objectives, with progress reviewed at set intervals. The program supports founders in strengthening their product, business model and clinical relevance.<br>\r\n<strong>Program context</strong><br>\r\n<br>\r\nLaunched in 2022, the Future of Health Grant is an initiative of <strong>CSS</strong> and the <strong>EPFL Innovation Park</strong>. Its mission is to accelerate the adoption of impactful digital health solutions by bridging early‑stage innovation with the needs of real healthcare systems.","image_description":"","creation_date":"2026-05-22T13:39:38","last_modification_date":"2026-05-22T13:39:44","link_label":"Webpage","link_url":"https://future-of-health.org/","canceled":"False","cancel_reason":"","place_and_room":"","url_place_and_room":"","url_online_room":"","spoken_languages":["https://memento.epfl.ch/api/v1/spoken_languages/2/?format=json"],"speaker":"","organizer":"<a href=\"https://future-of-health.org/\">Future of Health Grant</a>, a programm powered by Fondation EPFL Innovation Park","contact":"Mathilde Durvy <a href=\"mailto:info@fohg.ch\">info@fohg.ch</a>","is_internal":"False","theme":"","vulgarization":{"id":2,"fr_label":"Public averti","en_label":"Informed public"},"registration":{"id":1,"fr_label":"Sur inscription","en_label":"Registration required"},"keywords":"","file":null,"icalendar_url":"https://memento.epfl.ch/event/export/120842/","category":{"id":17,"code":"DEADLINE","fr_label":"Echéance","en_label":"Deadline","activated":true},"academic_calendar_category":null,"domains":[],"mementos":["https://memento.epfl.ch/api/v1/mementos/422/?format=json"]},{"id":71936,"title":"Understanding and Predicting the Membrane Permeability of Macrocyclic Peptides Based on Large-Scale Data","slug":"understanding-and-predicting-the-membrane-permeabi","event_url":"https://memento.epfl.ch/event/understanding-and-predicting-the-membrane-permeabi","visual_url":"https://memento.epfl.ch/image/33247/200x112.jpg","visual_large_url":"https://memento.epfl.ch/image/33247/720x405.jpg","visual_maxsize_url":"https://memento.epfl.ch/image/33247/max-size.jpg","lang":"en","start_date":"2026-09-15","end_date":"2026-09-15","start_time":"16:15:00","end_time":"17:15:00","description":"<p>Macrocyclic peptides are promising molecules for targeting intracellular protein–protein interactions and other challenging biological targets. However, their poor membrane permeability often limits their biological and therapeutic applications. Understanding the structural factors that govern passive membrane permeation is therefore essential for the rational design of cell-permeable macrocyclic peptides.<br>\r\n<br>\r\nIn this seminar, I will present our recent efforts to understand and predict the membrane permeability of macrocyclic peptides using large-scale experimental data, conformational analysis, and machine learning. We aim to clarify how conformational flexibility, including molecular chameleonicity, influences membrane permeability.<br>\r\n<br>\r\nI will also introduce our ongoing efforts toward DNA-encoded macrocyclic N-methyl peptide libraries as a future platform for discovering functional intracellular binders.</p>","image_description":"Prof. Shinsuke Sando","creation_date":"2026-05-22T16:51:02","last_modification_date":"2026-05-22T16:51:02","link_label":"Prof. Shinsuke Sando","link_url":"https://park.itc.u-tokyo.ac.jp/sandolab/index_e.html","canceled":"False","cancel_reason":"","place_and_room":"CH G1 495","url_place_and_room":"https://plan.epfl.ch/?room==CH%20G1%20495","url_online_room":"","spoken_languages":["https://memento.epfl.ch/api/v1/spoken_languages/2/?format=json"],"speaker":"Prof. Shinsuke Sando, University of Tokyo","organizer":"C. Heinis","contact":"C. 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