May 17 is the International Day Against Homophobia, Biphobia, Interphobia, and Transphobia, also known as IDAHOBIT. Celebrated around the world, this date was chosen to commemorate the World Health Organization’s (WHO) decision on May 17, 1990, to no longer consider homosexuality as a mental illness.
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\r\nTo mark the occasion, the Office for Equality (VPH-WINC) invites you to visit the exhibition “LGBTIQphobic Discrimination Around the World, in Europe, and in Switzerland” on the flat space in front of the library at the Rolex Learning Center, from 13 May 2026.
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\r\nThis exhibition highlights the meanings of the letters in the LGBTQIA+ acronym, as well as content documenting the state of LGBTIQphobic discrimination on a global, European, and Swiss scale. The exhibition incorporates scientific data from various research institutions, such as the NGO ILGA and Unisanté. Visitors will have the opportunity to reflect and engage through interactive elements while discovering or deepening their knowledge on the subject.
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\r\nPlease feel free to share this message or spread the word!
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Two-dimensional (2D) materials have emerged as a versatile platform at the intersection of fundamental physics and applied science. Their atomically thin nature gives rise to distinctive electronic and optical properties, while simultaneously enabling them to function as highly sensitive, readily integrable probes of their local environment. Advanced optical techniques, such as super-resolution microscopy, have opened new opportunities to interrogate these materials at the nanoscale, providing optical access to individual defect behaviors, exciton diffusion and recombination dynamics, and charge transport pathways. Such approaches not only deepen our understanding of intrinsic material behavior but also position 2D systems as powerful sensors capable of resolving local dielectric variations, electric and magnetic fields, and other environmental perturbations.
\r\nCentral to this work is hexagonal boron nitride (hBN), a transparent, wide bandgap semiconductor that serves as a host for optically active defects. These defects function as reaction centers, single-photon emitters, and spatially resolved nanoscale sensors that can be readily integrated into electronic and photonic architectures. Owing to its optical transparency, chemical stability, and compatibility with diverse material platforms, hBN provides an exceptional foundation for optical sensing. Building upon this foundation, we explore material properties and present new methods for spatially resolved in situ optical imaging with hBN to advance dynamic, wide-field optical sensing.
\r\nWe investigate a previously reported class of emitters that arise from interactions between native hBN and organic solvents. These emitters are believed to originate from defect sites that bind transiently to solvent molecules. To study their behavior, we develop a platform for imaging the dynamics of these transient emitters while varying the electrochemical potential and applying electric fields with controlled orientations. By tracking the spectra of individual emitters, we enable multiplexed measurements that allow spatially resolved electrochemical imaging. Through systematic analysis, we rule out modulation mechanisms based on direct electric field effects or charge transfer. Instead, we identify a mechanism driven by changes in H⁺ concentration, which is modulated during the oxidation of trace water present in the solvent. This finding opens the possibility of using this platform for sensitive detection of H⁺ and trace water in methanol fuel cells, where such species critically influence operational efficiency.
\r\nIn the final part of the thesis, we focus on a well-characterized spin defect in hBN, the negatively charged boron vacancy, and explore strategies to enhance its photoluminescence (PL) through heterostructure engineering that facilitates energy and exciton transfer. We demonstrate the coupled structure’s improved utility in optical magnetometry compared to the defect by itself and discuss how improvements in PL could advance the development of wide-field optically detected magnetic resonance (ODMR) imaging using this spin defect. Using a defect in hBN would leverage 2D materials’ exceptional sensing capabilities and planar integrability.
\r\nOverall, this thesis aims to highlight the strengths of integrating 2D materials with optical sensing and to develop transferable techniques for introducing controlled stimuli, such as electrochemical potentials, electric fields, or electromagnetic waves, with high fidelity and minimal artifacts. These advances not only demonstrate the potential of hBN as a versatile sensing platform but also establish methodologies applicable to a wide range of low-dimensional material systems.
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\r\nFor zoom:
\r\n Meeting ID: 611 3428 2068 Passcode: 952027
Immense evolutionary pressure has driven bacteria to innovate a diverse arsenal of immune systems that defend against parasitic mobile genetic elements, including bacteriophages and plasmids. The study of these systems has proven extraordinarily fruitful for biotechnology: CRISPR-Cas9, originally discovered as a bacterial defense mechanism, has revolutionized genome editing and demonstrated the transformative potential of mining bacterial immunity for molecular tools.
\r\nYet the diversity of bacterial defense extends far beyond CRISPR. Bacteria have evolved numerous strategies to recognize and destroy foreign DNA, many of which remain poorly understood at the mechanistic level. A central question is how these systems distinguish self from non-self — and how the underlying molecular logic of DNA targeting is encoded in protein structure and biochemistry. We combine cryo-electron microscopy, quantitative biochemistry, and in vivo genomics to characterize newly discovered defense systems and dissect their targeting mechanisms. This work is revealing unexpected principles of foreign DNA recognition, with implications both for our understanding of bacterial immunity and for the development of next-generation biotechnological tools for genome editing and beyond.
The ATC (Assistive Technology Challenge : EPFL Assistive Techn... - Project - MAKE) welcomes you to discover this year's six prototypes designed by the students' teams ! Join us for this unique occasion to exchange with them about their work, meet challengers, and get a glimps at how they overcame the challenges !
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\r\nIn this talk, I will show how various computer-aided techniques can be used to study first-order optimization methods in a systematic way. From performance estimation problems with automated Lyapunov discovery, to symbolic regression and computer algebra systems, novel tools completely reshape the way we approach theory of optimization.
\r\nAs a main example, I will focus on error feedback methods used with compressed communication in distributed optimization. While error feedback has been widely studied, existing theory often provides untight (thus unreliable) bounds. I will present tight analyses with matching lower bounds that allow a fair comparison between error feedback schemes and standard compressed gradient descent, and help explain when error feedback is useful and when it is not.
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\r\nOverall, the talk aims to show how various computer-aided proofs can lead to clearer and more reliable insights into first-order optimization methods.
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\r\nBased on :
\r\nA Tight Theory of Error Feedback Algorithms in Distributed Optimization, DB Thomsen, A Taylor, A Dieuleveut
\r\nInternational Conference on Machine Learning (ICML 2026)
\r\nTight analyses of first-order methods with error feedback, DB Thomsen, A Taylor, A Dieuleveut
\r\nAdvances in Neural Information Processing Systems (NeurIPS) (2025).
\r\nPEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
\r\nB Goujaud, C Moucer, F Glineur, JM Hendrickx, AB Taylor, A Dieuleveut
\r\nMathematical Programming Computation 16 (3), 337-367 (2024)
\r\n(eventually - as a detour) Provable non-accelerations of the heavy-ball method B Goujaud, A Taylor, A Dieuleveut, Mathematical Programming, 1-59 (2025)
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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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