Thesis Directors: Dr. C. Cao, Prof. M. Dal Peraro
\r\nBiotechnology and Bioengineering doctoral program
\r\nThesis Nr. 11784
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\r\nTo take part in the public defense, please contact directly the speaker
Current research indicates that the role of generative AI in education is a double-edged sword: on one hand it can assist teachers for designing effective learning tasks and resources, on the other hand it can have negative effects on student learning and raises a range of ethical questions.
\r\nIn our new workshop series “Responsible AI skills for education” we will try to navigate these questions by focusing on specific teaching and learning activities and identifying practical skills you can develop for your everyday practice. Through a blend of concise theoretical input and hands-on activities, we will explore both the benefits and the risks of generative AI in education in short 1-hour sessions on campus. The first two workshops in the series focus on quiz design and critical analysis skills.
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\r\nTeaching critical analysis:
\r\nIn this 1-hour workshop, we will focus on how to help students develop critical analysis skills for reviewing LLM outputs in the context of learning tasks. The workshop will combine theoretical inputs and hands-on activities, drawing on your disciplinary expertise and fact-checking strategies.
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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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Ultra-high-field MRI is delivering tangible clinical impact. With four 7T systems nationwide, Switzerland is uniquely positioned to drive efficient translational innovation in advanced imaging. This first national UHF MRI symposium brings together the experts shaping the future of UHF MRI in Switzerland and beyond. The event aims to address unmet clinical needs, spark new collaborative projects, and strengthen a community dedicated to advancing patient care through innovation. The CIBM is proud to be one of the organizers of the event, which will take place at the Sitem-insel on in Bern on June 3, 2026.\r\n
Join us for the 9th edition of the Sustainable Space Hub (SSH) Coffee, an opportunity to connect with the community and explore the latest developments in space sustainability.
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\r\nStephan Hellmich will present the new SSA (Space Situational Awareness) activities: the NASO (Extracting Space Object Observations from large Astronomical Data Archives) and TESSA (Technologies to Enhance Space Situational Awareness) projects.
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\r\nIn collaboration with the University of Bern, the TESSA project continues to advance capabilities in satellite tracking and space object monitoring. Work in 2026 pursues two parallel research tracks: refining 6D pose estimation techniques for the precise determination of object position and orientation in space, and developing adaptive optics solutions for the Zimmerwald Observatory, including the associated algorithms for correcting atmospheric distortion. Together, these efforts contribute to the broader challenge of space situational awareness and safe orbital operations.
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\r\nDuring the event, the space semester projects for the fall semester will also be announced.
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\r\nProvisional schedule
\r\n12:30 – Welcome, coffee, cookies & networking
\r\n13:00 – Stephan Hellmich will present the new SSA activities: the NASO and TESSA projects.
\r\n13:10 – Presentation of the space semester projects for the fall semester.
\r\n13:45 – Q&A and open discussion
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\r\nWe look forward to welcoming you for an engaging discussion and informal networking around the latest advances in sustainable space activities.
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\r\nMore information
\r\nSemester projects: https://espace.epfl.ch/education/student-projects/
\r\nPresearch at EPFL Space Center: https://espace.epfl.ch/research/
Thesis Directors: Prof. A. Chiesa, Prof. M. T. Göös
\r\nComputer and Communication Sciences doctoral program
\r\nThesis Nr. 11413
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\r\nTo take part in the public defense, please contact directly the speaker
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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Current research indicates that the role of generative AI in education is a double-edged sword: on one hand it can assist teachers for designing effective learning tasks and resources, on the other hand it can have negative effects on student learning and raises a range of ethical questions.
\r\nIn our new workshop series “Responsible AI skills for education” we will try to navigate these questions by focusing on specific teaching and learning activities and identifying practical skills you can develop for your everyday practice. Through a blend of concise theoretical input and hands-on activities, we will explore both the benefits and the risks of generative AI in education in short 1-hour sessions on campus. The first two workshops in the series focus on quiz design and critical analysis skills.
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\r\nEffective quiz design:
\r\nIn this 1-hour workshop, we will focus on the design of effective quiz questions for your class with the assistance of LLM tools. We will explore both the ethical and practical questions associated with LLM use for this task through theoretical inputs and hands-on activities.