The talk is jointly organized by the EPFL AI Center and the EPFL Human-Computer Interaction Group as part of the AI for Health seminar series.
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\r\nHosting professor: Prof. Pearl Pu (GR-PU)
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\r\nTitle
\r\nBeyond Recommendations: Designing Conversational Music Agents for Self-Awareness and Psychological Well-being
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\r\nAbstract
\r\nAs the demand for scalable mental health tools grows, the potential of Large Language Models (LLMs) lies in their ability to move beyond generic retrieval toward deeply adaptive, trustworthy support. This talk explores how music-driven conversational systems can bridge the gap between algorithmic recommendation and psychological intervention. The journey begins with establishing a foundation of trust through user-centric exploration. Using conversational music recommenders as a case study, I will discuss how critiquing strategies—such as progressive and cascading suggestions—shape user perceptions of helpfulness and serendipity. However, because trust is personal, I demonstrate how individual traits, including personality and trust propensity, dictate whether a user thrives under system-led or user-led initiatives.
\r\nBuilding on this rapport, the talk examines how these interfaces transition from suggesting songs to fostering psychological well-being. I will illustrate how music acts as a catalyst for self-awareness, using agents to guide users through emotional resonance and self-reflection. Furthermore, I will share findings on the role of generative AI in music-based reminiscence for older adults, where adaptive dialogue helps overcome cognitive barriers to memory recall. Collectively, these studies provide a roadmap for designing AI that moves beyond the playlist, transforming music-based chatbots into sustained partners for emotional and psychological health.
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\r\nBio
\r\nProfessor Li Chen is currently a Full Professor and Associate Head (Research) in the Department of Computer Science at Hong Kong Baptist University (HKBU). She obtained her PhD degree in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, and her Bachelor's and Master's degrees from Peking University, China. Her recent research focus has mainly been on conversational AI, explainable AI, recommender systems, and human-computer interaction. She has authored and co-authored over 160 publications, with over 13,000 citations (H-index 53). Her co-authored papers have received several awards, such as the RecSys’24 Best Student Paper Award, CHI’22 Honourable Mention Award, UMAP’20 Best Student Paper Award, UMUAI 2018 Best Paper Award, and UMAP’15 Best Student Paper Award. She received the President’s Award for Outstanding Performance in Teaching (Individual) 2024/25 and the President’s Award for Outstanding Performance in Research Supervision 2022/23. She has been included in the list of the world’s top 2% most-cited scientists by Stanford University since 2021, and the list of the Best Computer Science Scientists 2025/2026 by Research.com.
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\r\nShe is now an ACM senior member, founding co-editor-in-chief of ACM Transactions on Recommender Systems (TORS), steering committee member of ACM Conference on Recommender Systems (RecSys), associate editor of International Journal of Human-Computer Studies (IJHCS), editorial board member of User Modeling and User-Adapted Interaction Journal (UMUAI), and associate editor of ACM Transactions on Interactive Intelligent Systems (TiiS). She has also served as program co-chair of IUI’26, general co-chair of ACM RecSys’23, program co-chair of ACM RecSys’20, and program co-chair of ACM UMAP’18.
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The 2026 Journée Georges de Rham will take place on Wednesday 03 June 2026 in the Polydôme at EPFL, from 15:00 to 17:30 followed by an apéritif.
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\r\nProfessor Wang will deliver a talk titled “Kakeya sets in R^3″. A Kakeya set is a compact subset of R^n that contains a unit line segment pointing in every direction. Kakeya set conjecture asserts that every Kakeya set has Minkowski and Hausdorff dimension n. We prove this conjecture in R^3 as a consequence of a more general statement about union of tubes. Joint work with Josh Zahl.
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\r\nProfessor Candès will deliver a talk titled “What Statistics and AI Offer Each Other?“, exploring how thinking carefully about AI inputs and outputs yields more powerful, safer AI. By examining several vignettes, we shall answer questions such as: how do we train language models under cost constraints? What happens when we’ve exhausted all available data? If I start a clinical trial using the drug AI thinks is best, will it pan out? How can we ensure high-quality products when AI is used in a larger workflow? That is, how do I know whether AI automated a task correctly? AI powered imputations are beginning to substitute for real data when collection of the latter is difficult, slow, or costly. How then should we leverage machine learning predictions both as a substitute for high-quality data and as a tool for guiding real data collection?
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\r\nBernoulli Center for Fundamental Studies
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The EPFL AI Center is delighted to invite you to join the exchange session about the Research Computing Platform (RCP).
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\r\nAre you building, training ML models? Are you currently facing challenges regarding IT infrastructure, and looking for reliable, collaborative, high-capacity, and affordable storage solutions to manage your research data? Are you seeking information about the computing costs?
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\r\nThe Research Computing Platform (RCP) offers campus-wide IT infrastructure geared for the research community. The offering includes a GPU infrastructure (> 400) aiming at making AI compute as accessible as possible throughout the campus.
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\r\nThis event will provide insights into the various services RCP offers to the EPFL research community.
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\r\nEvent Details:\r\n
Recent advances in foundation models are extending AI beyond perception and language understanding toward embodied systems that can perceive, reason, and act in the physical world. This transition is accelerating the development of vision-language-action (VLA) models and robot foundation models that aim to provide robots with more general, adaptive, and deployable intelligence.
\r\nThis talk presents recent progress toward self-working humanoid robots, covering key ideas in embodied AI such as multimodal understanding, long-horizon planning, action generation, and real-world deployment. It also introduces recent research from Seoul National University BI Lab and AI Institute on human-robot interaction, personalization, continual adaptation, and language-conditioned robot learning, together with the Habilis framework developed at Tommoro Robotics for deployable humanoid intelligence.
\r\nFinally, the talk discusses a future architecture for self-working humanoids that integrates perception, memory, reasoning, planning, and action into a unified learning system. The central theme is that while large language models enabled machines to communicate, vision-language-action models will enable machines to work autonomously in the real world.
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\r\nByoung-Tak Zhang's biography
\r\nPresent:
\r\nProfessor of Computer Science, Seoul National University,
\r\nDirector of the Humanoid AI Robot Center (HARC),
\r\nChairman of the K-Humanoid Alliance (Korean Government)
\r\n2019-2025:
\r\nFounding Director of SNU AI Institute (AIIS)
\r\n2007-2008:
\r\nVisiting Professor, Samsung Advanced Institute of Technology
\r\n2003-2004:
\r\nVisiting Professor, MIT CS&AI Lab (CSAIL)
\r\n1992-1995:
\r\nResearch Associate, German National Research Center for Computer Science (GMD)
\r\n1988-1992:
\r\nPhD in Computer Science, University of Bonn
\r\n1982-1988:
\r\nBS and MS in Computer Science, Seoul National University
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This workshop brings together researchers actively working on graph learning and financial networks. Our goal is to informally exchange ideas and define new research agendas at the interface of these fields.
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\r\nTopics will include interbank payment systems, financial risk monitoring, and dynamic graph neural networks, among others.
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\r\nThe workshop is open to interested researchers and students. Space is limited, so please register using the link below. The workshop is organized by the Swissquote chair in quantitative finance and kindly sponsored by Swiss quote.
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\r\nRegistration required.