セミナー
1070 イベント
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セミナーAI and Scientific Discovery
2026年10月19日(月) 14:00 - 15:30
Joseph Ledsam (Google Health Lead, Japan, Google Japan)
Artificial intelligence is having a transformative impact on health and scientific discovery. This presentation will trace the evolution from foundational breakthroughs to the sophisticated capabilities of today's large-scale AI models. It will explore how these advanced systems are creating new possibilities across the healthcare landscape, from accelerating therapeutic development to enhancing diagnostic processes and interpreting complex medical data. The session will also take a deeper look at the future possibilities for AI in health and explore the emerging role of agentic AI in scientific discovery. The core theme is the responsible development of AI to create tools that assist scientists, support healthcare professionals, and empower users. Bio: Dr Joseph Ledsam leads Google Health in Japan, where he works across AI research, digital health and health in Google products. He has led research in medical AI, genomics and drug discovery published in journals including Nature, Nature Medicine and Nature Methods. Before moving to Japan he worked as a medical doctor in the UK, and founded the Health Research and Genomics teams in Google DeepMind. He obtained his medical degree from The University of Leeds, UK, and was a research fellow at University College London during his clinical residency.
会場: 研究本館 435-437号室 (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナーPrimtive form of singularity and mirror symmetry
2026年9月11日(金) 15:30 - 17:30
テツ・オウ (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 研究員)
In 1980s, K. Saito introduced the primitive forms in his study of periods as to generalize the elliptic period integral theory to higher dimensions. An important consequence was that there exists a flat structure on the space of universal unfolding of a singularity. Later after Witten proposed his famous conjecture relating the intersection theory on moduli space to the integrable hierarchy, this flat structure was re-discovered by Dubrovin, and nowadays it is referred as a Frobenius mafniold structure. Frobenius mafniolds provide a convenient tool for formulating the mirror symmetry, which was a special type of duality among string theories orginally studied by physicits. In this talk, I will explain the related constructions, and show by examples how the study of primitive forms gives prediction of geometric quantities such as Gromov-Witten invariants.
会場: セミナー室 (359号室) 3階 359号室
イベント公式言語: 英語
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セミナー
Computational demands shape seizure susceptibility in recurrent neural networks
2026年9月3日(木) 13:00 - 14:00
Sebastian Eydam (理化学研究所 脳神経科学研究センター (CBS) 神経回路計算研究ユニット 研究員)
Why do some brain areas slip into seizures more readily than others? Anatomy and physiology are part of the story, but in this talk we argue that the computation a network performs is itself a determinant of its vulnerability. Working in the language of recurrent neural networks and attractor dynamics, we contrast two computational regimes: networks that represent continuous variables (e.g. tracking a continuous position in space) establish barrier-free state manifolds, whereas networks that store discrete memories have to separate different states by establishing deep wells. A simple energy-landscape picture suggests that continuous networks can amplify perturbations into runaway activity more easily while discrete networks contain them. We test this idea across three systems: handcrafted spiking attractor networks, recurrent networks trained on continuous or discrete computational tasks, and in vivo Neuropixels recordings comparing medial entorhinal cortex (continuous, grid-cell dynamics) with hippocampal CA3 (discrete memory). Under a shared disinhibitory "seizure perturbation," the continuous systems destabilize sooner and drive stronger epileptiform activity, and causal silencing shows this depends on intact entorhinal output. Together, these results establish a direct link between the computation a network is built to perform and its susceptibility to seizures, showing that the very features that enable a network to process information also shape its vulnerability to pathological transitions.
会場: via Zoom / セミナー室 (359号室) 3階 359号室
イベント公式言語: 英語
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セミナー
Genome Language Models: From DNA Sequences to Biological Foundation Models
2026年8月13日(木) 15:00 - 16:00
Minrui Chen (九州大学 博士課程)
Recent advances in protein language models have greatly transformed protein structure prediction, functional annotation, and biomolecular design. In contrast, genome language models aim to learn directly from DNA sequences, which represent a more upstream layer of biological information encoding genes, regulatory logic, variant effects, and evolutionary signals. In this talk, I will introduce the basic motivation and recent progress of DNA and genome language models, including DNABERT, DNABERT-2, HyenaDNA, Evo, Evo 2, and AlphaGenome. I will discuss how different model architectures and tokenization strategies address the challenges of genomic sequence modeling, such as long-range dependencies, multi-scale biological structure, and genome-scale context.
会場: 研究本館 3階 359号室 (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
In Search of The Building Blocks of the Universe
2026年8月7日(金) 15:00 - 16:30
Anamaria Hell (カブリ数物連携宇宙研究機構 (Kavli IPMU) 特任研究員)
We live in an exceptional era of precision cosmology, marked by rapidly advancing observational probes that explore the Universe across many length scales. While these experiments offer clues about the geometry, dynamics, and large-scale structure of the cosmos, they still leave the origin and much of the evolution and structure of the Universe unknown. One of the key steps to answer these questions is to uncover the building blocks of theoretical models in both linear and non-linear regimes. In this talk, I will present methods for uncovering physical degrees of freedom, outlining the standard approaches and presenting an alternative, simple and straightforward way. I will then show how one can naturally connect this approach to machine learning. Finally, I will introduce the framework of constrained gravity, and discuss how such approaches can help address long-standing challenges in fundamental physics and open new directions across disciplines.
会場: セミナー室 (359号室) 3階 359号室とZoomのハイブリッド開催
イベント公式言語: 英語
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セミナーHow Reputation Sustains Cooperation: Mathematical Theories of Indirect Reciprocity
2026年8月6日(木) 15:00 - 16:00
村瀬 洋介 (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 数理社会科学チーム チームディレクター)
Cooperation among unrelated individuals is a central puzzle in the evolution of social behavior. Indirect reciprocity offers one influential explanation: people help others not only because they expect direct returns, but also because their actions affect their reputation. In this seminar, I will review mathematical theories of indirect reciprocity, focusing on how reputation and social norms can sustain cooperation. I will begin with the classical framework of public assessment, where everyone shares the same view of each individual’s reputation, including the seminal work of Ohtsuki and Iwasa on the “leading eight” social norms. I will then turn to private assessment, where individuals may disagree about others’ reputations, and discuss why synchronization of opinions becomes essential for cooperation. Overall, the seminar aims to provide an accessible overview of how mathematical models allow us to formalize moral judgments—what counts as good or bad behavior—and to understand the evolution of cooperation through reputation.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Theoretical Approaches for Cell Deaths
2026年8月6日(木) 13:00 - 14:00
姫岡 優介 (東京大学 生物普遍性研究機構 助教)
Understanding the boundary between cell life and death is a fundamental challenge and a highly important theme in biology. In this talk, focusing on microbial cell death, I would like to discuss how "cell death" can be understood from the perspective of mathematical sciences. Research on microbial cell death has progressed by identifying the molecular mechanisms that drive relevant biochemical processes, which are identified based on empirically known cell death markers. However, there has been little discussion on what death actually is in the first place, or how it can be "defined." Furthermore, recent reports have shown that commonly used live/dead assays—such as dead-cell staining and metabolic activity measurements—can yield conflicting results regarding cell viability, highlighting the growing need to discuss what criteria we should use to define "death." In this study, we propose a definition: a cell is "dead" if it cannot return to a predetermined "representative point of the living state," no matter how gene expression levels or external nutrient concentrations are controlled. Plant seeds may appear "dead" at first glance due to their lack of apparent biochemical activity, yet they germinate when watered. Our proposal in this study is to determine the life or death of a cell based on whether an operation equivalent to "watering" exists [1]. Of course, it is experimentally impossible to prove that a cell cannot regain activity under any operation; however, it is theoretically possible using mathematical models. We developed a method called "Stoichiometric Rays" to calculate the controllability of metabolic reaction systems. Using this, we calculated states that cannot be controlled back to the "representative point of the living state" regardless of how enzyme levels and external nutrient concentrations are manipulated. Consequently, we succeeded in quantifying the separating hyperplane between the "living state" and the "dead state" in a mathematical model of metabolism [2], which we call the Separating Alive and Non-life Zone (SANZ) Hypersurface. In this talk, I will outline the theory, the quantification of the SANZ hypersurface, and its biological interpretation. In addition, through our research [3], we have partially identified a class of models that do not exhibit "death" in the sense described above. I would also like to discuss the relationship between the absence of "death" in these models and the autonomy of life.
会場: セミナー室 (359号室) 3階 359号室 (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Four Fermi Theory in Four Dimensions is Renormalisable
2026年8月3日(月) 14:00 - 15:30
Charlie Cresswell-Hogg (Post-Doctoral fellow, Department of Physics, Sussex University, UK / Post-Doctoral fellow, Dortmund University, Germany)
We demonstrate the renormalisability of quantum field theories in four dimensions with elementary self-interacting Dirac fermions and to leading order in the limit of many fermion flavours Nf. Starting from the underlying divergence structure and using Gross-Neveu-type interactions as a template, we explain why extended four-fermion theories including higher-derivative interactions are well-defined, renormalisable, and predictive with only a few free parameters. We also provide the exact large-Nf leading beta functions of couplings and discuss quantum scaling dimensions, universality, 1/Nf corrections, and extensions to other types of four fermion interactions. Implications for effective theory and model building are indicated.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Loop expansion in polymer field theory: application to phase separation
2026年7月30日(木) 16:00 - 17:00
川名 清晴 (Research Fellow, Korea Institute for Advanced Study (KIAS), Republic of Korea)
Liquid-liquid phase separation underlies phenomena ranging from protein condensate formation to the phase coexistence of synthetic polymers. In this talk, we develop a field theoretic loop expansion in homopolymer systems by identifying the inverse polymer density ρ^{-1} as the Planck constant ℏ in quantum field theory. The 1-loop approximation is known as the random phase approximation (RPA) and has been extensively applied to many (hetero)polymer systems. We calculate the leading-order (2-loop) and next-to-leading-order (3-loop) corrections to the RPA free energy, denoted as RPA+ and RPA++, respectively. Testing the binodal predicted by the RPA+ against molecular dynamics simulations of bead-spring chains with Gaussian pair interactions, we find that the RPA+ qualitatively improves the dilute-phase coexistence density over the RPA, while the critical point error remains comparable to that of the RPA. Our results establish the loop expansion as a systematic route for refining the RPA-based binodal predictions for polymer phase separation. This talk is based on arXiv: 2605.01261.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Current challenges teaching undergraduate first year physics in Canada
2026年7月30日(木) 13:00 - 14:00
カトゥリン・ボシゥメン (理化学研究所 数理創造研究センター (iTHEMS) 副センター長)
I will talk about the challenges of teaching first year physics in 2026 to undergraduate students in computer science and engineering in Canada. Topics will include issues with textbook publishers (online vs physical books), open access textbook/homework systems, teaching, learning and setting evaluations, labs and homework in the era of AI, student attitudes towards learning, academic accommodation for disabilities, etc. This seminar could be interesting to those of you who will face teaching in your future, especially abroad. Although my perspective is based on physics and Canada, a number of issues raised are broadly relevant to other fields and countries.
会場: 研究本館 3階 359号室とZoomのハイブリッド開催
イベント公式言語: 英語
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セミナー
Center-vortex condensation and monopole condensation in 4d gapped phases
2026年7月27日(月) 14:00 - 15:30
林 優依 (京都大学 基礎物理学研究所 学振特別研究員PD)
Two well-known scenarios for quark confinement are center-vortex proliferation and monopole condensation. We consider gauge-invariant criteria for center-vortex condensation and monopole condensation in terms of Z(N) 1-form symmetry. The condensation of a soliton can be characterized by the non-suppression of the partition function with a proper twisted boundary condition, and we utilize this idea for these criteria. With these definitions, we show that gapped phases with the center-vortex condensation necessarily exhibit the monopole condensation.
会場: セミナー室 (359号室) 3階 359号室とZoomのハイブリッド開催
イベント公式言語: 英語
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セミナー
Some instances where topological illustration induced new mathematics
2026年7月24日(金) 16:30 - 18:00
Sofia Lambropoulou (Professor, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Greece)
We shall present instances from generalized knot theory, braid theory and their interactions, where illustration promoted understanding and inspired new mathematics. The first instance addresses a question of V.F.R. Jones whether one can make analogous constructions to the (2-variable) Jones polynomial using other braid groups and other types of Hecke algebras. The second instance addresses the question of formulating braid equivalences, analogous to the Markov theorem for classical braids, in settings where we may not even have available algebraic structures for the related braids. The third instance is about the theory of bonded knots and bonded knotoids used for modelling proteins.
会場: via Zoom / セミナー室 (359号室)
イベント公式言語: 英語
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セミナー
Unraveling the very early universe with black holes, boson stars, and cannibal stars
2026年7月24日(金) 14:00 - 16:00
小林 洸 (Associate Professor, International School for Advanced Studies (SISSA), Italy)
According to the standard picture of cosmology, the rich structure of our universe began to form roughly 50,000 years after the big bang. In this talk I will explore the possibility that cosmic structures could also have formed in the extremely early universe, within a fraction of a second after inflation. I will show how this early structure formation can give rise to compact objects, including exotic stars and primordial black holes. These relics provide powerful probes of the first instants of cosmic history, especially the reheating epoch, and may even act as seeds for cosmological phase transitions. Note: This seminar is jointly organized by the iTHEMS-phys Study Group and the iTHEMS-ABBL Joint Astro Study Group.
会場: セミナー室 (359号室) / via Zoom
イベント公式言語: 英語
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セミナー
The decision intelligence of humans and machines
2026年7月24日(金) 10:30 - 11:30
Petter Holme (Professor, Department of Computer Science, Aalto University, Finland)
The event has been rescheduled from July 22 to July 24. To understand our near-future of artificial intelligence firmly integrated into many levels of social life, a challenge is to understand the differences and similarities between human and AI decision-making. In controlled laboratory settings assessing risk and uncertainty, LLMs demonstrate superhuman efficiency but fundamentally diverge from human behavior through a rigid hyper-rationality and an inability to disengage from obsolete strategies. However, when applied to messy, real-world dilemmas "in the wild," these models pivot to function as highly effective "satisficers". Human subjects consistently prefer this artificial counsel over human peer advice, noting its ability to carefully balance emotional context with logical constraints while actively reducing anxiety and regret. Ultimately, this synthesis shows that while AI can offer near-optimal laboratory performance and therapeutic impact in daily life, they also have a distinct lack of behavioral plasticity that we need to account for in models of the future.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
When Is Collective Intelligence a Lottery? Toward a Physics of Bounded Agent Societies
2026年7月22日(水) 16:00 - 17:30
田中 秀宣 (Group Leader, CBS-NTT Physics of Intelligence Program, Center for Brain Science, Harvard University, USA)
Multi-agent LLM systems can reach agreement quickly, but agreement alone does not reveal whether a group has integrated evidence, amplified a bias, or merely locked in the luck of early samples. In this talk, I will develop a physics-style approach to this problem through two synthetic games. In reward-free naming games, populations form conventions through mutual in-context learning: agents treat one another's sampled outputs as evidence, so early fluctuations compound into consensus. A minimal model, Quantized Simplex Gossip (QSG), identifies this regime as memetic drift and predicts scaling laws and a crossover from lottery-like drift to bias-driven selection. I will then introduce the Flag Game, in which a hidden country flag provides verifiable ground truth but each agent sees only a private crop of it. Here, bounded agents must balance private evidence against social input. We find rich phenomenologies, where adding agents can help or hurt, large groups can polarize, and social-awareness prompting, model diversity, and organizational structure all reshape collective performance. Extending QSG with grounded evidence and model-specific update rules explains these effects. More broadly, these studies frame an LLM society as a network of networks, neural networks coupled through social interaction, and outline a route toward a multiscale physics of interacting AI agents, linking model-internal representations, agent-level decision mechanisms, and population-level social dynamics.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Entanglement suppression for ΩΩ scattering
2026年7月17日(金) 15:00 - 16:30
曽根 克佳 (東京都立大学 大学院理学研究科 博士課程)
The S-matrix describing the scattering process can be expressed in terms of projection operators onto the allowed spin–flavor channels and the corresponding phase shifts. Using the entanglement entropy in the spin space of the two-particle state, one can define the entanglement power, which quantifies the ability of the S-matrix to generate entanglement in the system. By investigating the conditions under which the entanglement power of the S-matrix is minimized, namely, the conditions for entanglement suppression, one can derive relations among the phase shifts in different spin–flavor channels. Furthermore, by comparing these relations with the interaction Lagrangian, one can identify the underlying symmetries [1,2]. In this work, we apply the entanglement suppression framework to two-baryon scattering involving spin-3/2 baryons in the flavor decuplet [3]. Lattice QCD calculations have shown that the spin-0 ΩΩ system exhibits scattering close to the unitary limit. Combining this result with the relation between the phase shifts obtained from entanglement suppression, we discuss the scattering behavior of the spin-2 ΩΩchannel.
会場: 研究本館 4階 445-447号室 (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
A first step towards Non-Archimedean Geometric Quantization
2026年7月17日(金) 14:00 - 15:30
後藤 慶太 (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 基礎科学特別研究員)
Calabi--Yau manifolds have long attracted interest from both mathematics and physics, particularly in the context of mirror symmetry, and form an important class of compact Kähler manifolds. A compact Kähler manifold is Calabi--Yau if and only if it admits a Ricci-flat Kähler metric, which we shall call a CY metric. Such a metric is highly analytic in nature, as it is given as the solution to a second-order PDE on the manifold, namely the complex Monge--Ampère equation. When the Calabi--Yau manifold is a complex projective variety, one algebraic approach to understanding this analytically defined CY metric is to approximate it by algebraically defined metrics called balanced metrics. This framework was initiated by Donaldson and is now known as geometric quantization. In this talk, following the spirit of this theory, we consider a non-Archimedean analogue of this approximation theory. More precisely, for a non-Archimedean analytic space associated with a maximally degenerating family of Calabi--Yau manifolds, we study the approximation of the NACY metric, a non-Archimedean analogue of the CY metric, by algebraically defined metrics. In particular, we introduce NA balanced metrics, which are expected to provide such an approximation, and explain that, for totally degenerating families of abelian varieties, NA balanced metrics indeed approximate the NACY metric.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Artificial Intelligence in New Physics Electroweak Phase Transition Studies
2026年7月16日(木) 15:00 - 16:00
Yang Zhang (Professor, Henan Normal University, China)
The study of electroweak phase transitions in BSM involves complex numerical calculations, large parameter spaces, and the integration of multiple computational tools. In this talk, I will review recent developments in applying artificial intelligence to new physics phase transition studies. First, I will discuss how machine learning methods can accelerate electroweak phase transition studies, including efficient evaluations of phase transition dynamics, such as bounce action calculations, and the exploration of detectable parameter regions for gravitational-wave searches. Then, I will introduce the emerging role of AI agents in scientific workflows, including automated model construction, effective potential generation, and parameter scans. These developments illustrate how AI can transform traditional computational pipelines and provide new possibilities for future high-energy physics research.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Visual system and (social) behavior in zebrafish
2026年7月16日(木) 13:00 - 14:00
久保 郁 (理化学研究所 脳神経科学研究センター (CBS) 知覚運動統合機構研究チーム チームディレクター)
Animals make decisions about their actions using sensory information from the external world. Our lab investigates how the brain processes visual information and generates appropriate behavior using the vertebrate model organism, zebrafish. Our research has uncovered the neural circuits required for processing optic flow, a visual cue that animals use to estimate their own motion. We employ a diverse range of techniques, including behavioral tracking, live imaging of neural activity, and molecular and genetic characterization of neuronal cell types. More recently, our research has focused on social behavior, in which groups of animals collectively generate behavioral decisions. In this seminar, I will present our recent work investigating behavioral contagion in zebrafish.
会場: セミナー室 (359号室) 3階 359号室とZoomのハイブリッド開催
イベント公式言語: 英語
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セミナー
What determines accuracy in matrix projection models?
2026年7月10日(金) 14:30 - 15:30
Richard Shefferson (東京大学 大学院総合文化研究科 教授)
Matrix projection models (MPMs) have grown in complexity as ecologists have sought to include more factors that may influence population size and structure. However, some studies suggest that MPMs may lead to predictions inaccurate enough as to question their overall utility. I used long-term (21-36 year) demographic datasets on 6 herbaceous perennial species to examine and compare the ability of MPMs with different structural characteristics to predict future population size and structure. In absolute terms, almost all models performed poorly. In relative terms, density-dependent, ahistorical stage-based models with simple life histories and fewer stages were most successful in predicting population size. My results indicate that MPMs and IPMs are typically poor predictors of absolute population size and structure, but, when constructed properly, can still be used as useful qualitative predictors of population change.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
1070 イベント