Seminar

Prediction of viral evolution and exploration of next-pandemic viruses

June 15 (Mon) 15:00 - 16:00, 2026

Jumpei Ito (Professor, Research Institute for Microbial Diseases, The University of Osaka)

One of the major challenges in controlling viral infectious diseases is that viruses continuously alter their properties through evolution. During the COVID-19 pandemic, for example, variants with enhanced immune escape and increased fitness emerged successively, thereby making epidemic control substantially more difficult. In this seminor, I will introduce our research on understanding and predicting viral evolution and epidemic dynamics by integrating protein language models, massive viral genome sequence data, and large-scale experimental datasets to model the relationships among viral genotypes, antigenicity, and fitness. Another major factor complicating the control of viral infectious diseases is the cross-species transmission of viruses harbored by wild animals to humans and livestock, leading to the emergence of novel infectious diseases. The COVID-19 pandemic, for instance, is thought to have originated from a coronavirus carried by horseshoe bats that subsequently spilled over into humans. To prepare for future pandemics, it is essential to comprehensively identify and systematically catalog viruses circulating in wildlife populations. In this seminar, I will also present our research on efficiently discovering novel viruses from massive public RNA-seq datasets by predicting viral infection based on host immune responses.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

Patient-adaptive medical AI: Similarity-based fine-tuning for cross-patient generalization

June 15 (Mon) 14:00 - 15:00, 2026

Xuyang Zhao (Assistant Professor, Graduate School of Medicine / Faculty of Medicine, The University of Osaka)

Medical AI models often face performance degradation when applied to new patients due to inter-patient variability in physiological characteristics, disease manifestations, and clinical histories. This challenge, commonly referred to as the cross-patient problem, limits the generalizability and clinical applicability of machine learning systems. We introduce a similarity-driven framework for patient-adaptive learning that improves model performance on previously unseen patients. The proposed approach first trains a base model using conventional supervised learning and subsequently estimates the similarity between a target patient and the training population using intermediate model representations. The similarity information is then incorporated into a fine-tuning procedure through patient-dependent weighting, enabling the model to adapt its decision boundaries toward the characteristics of each individual patient. We demonstrate the effectiveness of this strategy in two medical AI applications, including seizure onset zone classification in epilepsy and medical image classification tasks. Experimental results show consistent improvements over standard cross-patient learning approaches, highlighting the potential of similarity-based adaptation as a practical solution for personalized and generalizable medical AI systems.

Venue: Hybrid Format (3F #359 and Zoom), Main Research Building

Event Official Language: English

Which Cosmological EFTs Survive the UV? A first step from quantum consistency to late-time cosmology

June 15 (Mon) 10:00 - 11:30, 2026

Carlos Pastor-Marcos (Ph.D. Student, ITP, Heidelberg University, Germany)

EFTs for cosmology are one of our best tools to describe possible departures from GR in the Universe we observe. However, not every low-energy theory can arise from a consistent quantum theory at high energies. In this talk, I will discuss how this question can be addressed using asymptotic safety (AS), and how UV consistency can constrain the space of viable modified-gravity EFTs. Instead of treating all EFT parameters as equally possible, we can ask which regions of theory space are connected to a well-defined fixed point in the UV. This provides the first ingredients of a UV-to-IR strategy, restricting the allowed low-energy theories and indicating how quantum-gravity information may reach cosmology. I will first give a pedagogical introduction to AS and the functional RG, focusing on the physical picture rather than technical details. I will then apply the framework to generalized Proca theories, a class of vector–tensor modified-gravity EFTs with relevant cosmological applications, to illustrate how this analysis is performed in practice and how it can constrain viable IR theories. I will close by discussing how UV completion can become a practical guide for cosmology, translating quantum-consistency conditions into phenomenological signatures, from late-time modified gravity to early-universe observables, strong-gravity tests and GW probes.

Venue: Hybrid Format (3F #359 and Zoom), Seminar Room #359

Event Official Language: English

Noncritical Conformal Gravity and 4D Liouville Theory

June 12 (Fri) 15:00 - 16:30, 2026

Nobuyoshi Ohta (Visiting Professor, Nambu Yoichiro Institute of Theoretical and Experimental Physics (NITEP), Osaka Metropolitan University)

We study the quantum aspects of the conformal gravity in four dimensions, specifically addressing a known discrepancy in beta functions between general quadratic curvature theories and conformal gravity, which corresponds to two scalar degrees of freedom. We demonstrate that this mismatch is resolved by carefully introducing gauge-fixing and ghost terms via the BRST symmetry, which effectively adds the two scalar modes. Drawing lessons from two-dimensional quantum gravity and Liouville theory, we proceed to integrate the four-dimensional trace anomaly to derive a consistent Liouville action, which is given by a free-field action for the conformal mode with a consistent conformal anomaly. We give the condition that the BRST transformation is anomaly free. Finally I would like to talk about some application of this theory.

Venue: Hybrid Format (3F #359 and Zoom), Seminar Room #359

Event Official Language: English

Testing quantum gravity

June 12 (Fri) 10:30 - 12:00, 2026

Daniel Carney (Scientist, Lawrence Berkeley National Laboratory (LBNL), USA)

I will give an overview of proposals to test the quantization of the gravitational field using terrestrial experiments. This will include gravitational entanglement experiments, "single-graviton detection" experiments, and searches for anomalous gravitational noise and decoherence.

Venue: Hybrid Format (3F #359 and Zoom), Seminar Room #359

Event Official Language: English

Quantum Improved Black Holes in Asymptotically Safe Gravity

June 11 (Thu) 15:00 - 16:30, 2026

Chiang-Mei Chen (Professor, Department of Physics, National Central University, Taiwan)

In this talk, I will explore quantum-improved black hole solutions within the framework of asymptotic safety. In this approach, the Newton coupling becomes scale-dependent, necessitating a meaningful identification between the energy scale and a corresponding physical (length) scale to derive observable consequences for black hole spacetimes. I will argue that the requirement of consistency with the first law of black hole thermodynamics provides a physically motivated criterion for this scale-setting, particularly near the event horizon. Applying this principle, we propose a specific identification scheme that leads to a regularized geometry capable of resolving the ring singularity of Kerr black holes.

Venue: Hybrid Format (3F #359 and Zoom), Seminar Room #359

Event Official Language: English

Mode Estimation in the Space of Phylogenetic Trees with Applications to Species Tree Reconstruction

June 11 (Thu) 13:00 - 14:00, 2026

Yuki Takazawa (Project Research Associate, Graduate School of Information Science and Technology, The University of Tokyo)

Analyzing samples of phylogenetic trees arises in many settings, including bootstrap tree sets, Bayesian posterior samples, and collections of gene trees. The Billera–Holmes–Vogtmann (BHV) tree space provides a geometric framework in which such samples can be viewed as point clouds in a common metric space. A fundamental summary in this space is the Fréchet mean, but it has a property known as stickiness: mean trees tend to lie on lower-dimensional boundaries of the space, corresponding to unresolved, non-binary trees. This behavior can be undesirable, as the mean may then fail to represent the center of interest. In this talk, I will introduce the BHV tree space framework and discuss mode estimation as an alternative way to summarize distributions of phylogenetic trees. After motivating the use of the mode, I will present simple approaches to mode estimation and discuss their consistency and robustness properties. I will then discuss how these ideas can be applied to species tree reconstruction from conflicting gene trees. To handle larger taxon sets, I will use quartet-based aggregation, in which local modal summaries are constructed from trees restricted to sets of four taxa and then combined to reconstruct a species tree. This approach provides a scalable way to apply mode estimation to trees with many taxa and helps reduce the influence of contamination in gene tree collections, as illustrated in simulation studies.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

Disorder and Defects in Critical Systems

June 8 (Mon) 13:30 - 15:00, 2026

Baishali Roy (Postdoctoral Fellow, Indian Institute of Technology Kanpur, India)

Real critical systems are often constrained by boundaries and affected by impurities. In 3d, the effect of disordered impurities on the boundary can be modeled by a random magnetic field on a two-dimensional defect. In this talk, I will discuss how such disorder affects the Wilson-Fisher fixed point in d=4−\epsilon dimensions. By analyzing the one-loop RG flow of the defect couplings using the replica formalism, we find a non-trivial "dirty" fixed point which represents a new boundary universality class, stabilized by the bulk \phi^4 interaction. Disordered systems at critical points are known to exhibit logarithmic behavior — I will also discuss how operator mixing in the replica limit gives rise to a logarithmic defect CFT in our setup.

Venue: #359, 3F, Main Research Building (Main Venue) / via Zoom

Event Official Language: English

Classification of certain actions of representation categories

June 5 (Fri) 15:00 - 17:00, 2026

Mao Hoshino (Special Postdoctoral Researcher, Division of Fundamental Mathematical Science, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS))

Last year I showed a classification theorem for certain classes of module categories over the representation category of quantum SU(n). Although its proof is elementary, the classification itself has a natural interpretation based on the concept of quantization.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

reflection positivity in de Sitter space

June 5 (Fri) 10:30 - 11:30, 2026

Yuki Suzuki (Special Postdoctoral Researcher, Division of Fundamental Mathematical Science, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS))

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States

June 4 (Thu) 15:00 - 16:00, 2026

Wei-Lin Wu (Ph.D. Student, School of Physics, Peking University, China)

Recent discoveries of multiquark candidates have opened a new frontier in hadron spectroscopy and nonperturbative QCD. Understanding these multiquark states poses a challenging quantum many-body problem governed by SU(3) color interactions. Traditional approaches based on basis expansions often encounter severe bottlenecks as the system size and dynamical complexity increase. In this talk, I will present DeepQuark, a deep-neural-network-based variational Monte Carlo framework for solving multiquark bound states. I will discuss the general methodology behind neural-network quantum states, the challenges of extending existing approaches from electronic and nuclear systems to hadron physics, and the architecture of DeepQuark. By combining physics-informed symmetry constructions with the expressive power of deep neural networks, DeepQuark provides a scalable framework for studying multiquark spectroscopy and exploring confinement dynamics.

Venue: via Zoom

Event Official Language: English

Membrane Geometry Regulates Phase Morphology in Postsynaptic Condensates

June 4 (Thu) 14:00 - 15:00, 2026

Risa Yamada (Ph.D. Student, Division of Biological Sciences, Graduate School of Science, Kyoto University)

Biomolecular condensates are generally regarded as membrane-less organelles formed through liquid–liquid phase separation (LLPS). However, some condensates in living cells emerge in close proximity to biological membranes, where spatial confinement and surface geometry can critically influence their organization and function. In this talk, I will discuss recent advances in understanding how membrane association regulates the phase behavior of postsynaptic density (PSD) condensates. Using mesoscale molecular simulations constrained by experimental interaction data, our study reproduced the distinct condensate architectures observed in solution and on membranes. In three-dimensional solution, AMPA receptor/PSD-95 complexes form the condensate core, whereas NMDA receptor/CaMKII complexes localize to the shell. Strikingly, this organization becomes reversed in membrane-associated two-dimensional systems. The analysis revealed that this transition arises from the competition between CaMKII’s large excluded volume and its highly multivalent interactions. While excluded-volume effects dominate in solution, membrane confinement favors specific multivalent interactions, stabilizing distinct receptor nanodomains. These results provide a physical framework for understanding how spatial dimensionality and molecular architecture regulate biomolecular condensates and synaptic organization.

Venue: Hybrid Format (3F #359 and Zoom), Seminar Room #359

Event Official Language: English

Generative diffusion model with inverse renormalization group flows

June 2 (Tue) 14:00 - 15:00, 2026

Kanta Masuki (Ph.D. Student, Graduate School of Science, The University of Tokyo)

Diffusion models have recently emerged as one of the most powerful frameworks for generative modeling, achieving remarkable success in a wide range of domains, including image generation, audio synthesis, and scientific data generation. However, despite their empirical success, conventional diffusion models often require many denoising steps and do not explicitly exploit the multiscale structure naturally present in various types of data. This limitation motivates us to ask whether ideas from the renormalization group (RG), which is designed to describe scale-dependent effective degrees of freedom, can provide a useful principle for constructing more efficient generative models. In this talk, I will present our recent work on renormalization-group diffusion models (RGDMs) [1], a generative framework that connects diffusion models with RG flows. By establishing a correspondence between diffusion dynamics and exact RG flow equations, we construct a diffusion model whose reverse process generates data in a coarse-to-fine manner, thereby effectively reversing an RG flow. I will first introduce the theoretical formulation of RGDMs and explain how the RG perspective leads to a coarse-to-fine generative process. I will then present numerical results in protein structure prediction and image generation, where RGDMs improve sample quality and/or sampling efficiency compared with conventional diffusion models. Finally, I will discuss possible extensions and open questions, including broader applications of RG-inspired generative modeling.

Venue: Seminar Room #359

Event Official Language: English

't Hooft anomaly matching and symmetry enforced gaplessness

June 1 (Mon) 13:00 - 14:00, 2026

Kantaro Ohmori (Senior Research Scientist, Division of Fundamental Mathematical Science, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS))

I will talk about the 't Hooft anomaly matching and its enforcement of gaplessness. I will also briefly touch on my recent work with Takamasa Ando on this topic.

Venue: via Zoom / Seminar Room #359

Event Official Language: English

Closed Seminar on Quantum Topology and Related Topics

May 29 (Fri) 14:00 - 18:00, 2026

Mao Hoshino (Special Postdoctoral Researcher, Division of Fundamental Mathematical Science, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS))
Kan Kitamura (Assistant Professor, Department of Mathematics, College of Science, Rikkyo University)
Yuya Murakami (Research Scientist, Division of Fundamental Mathematical Science, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS))
Vladimir Sosnilo (Research Scientist, Division of Fundamental Mathematical Science, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS))

We will hold a closed seminar on quantum topology and related topics. The talks will be given by the following four speakers. The talks will not be streamed online or recorded. 14:00–14:30 Mao Hoshino 14:30–15:00 Kan Kitamura (15:00–15:30 Coffee break) 15:30–16:00 Yuya Murakami 16:00–16:30 Vladimir Sosnilo (16:30–17:30 Casual reception)

Venue: Seminar Room #359

Event Official Language: English

Bootstrapping Cosmological Correlators

May 28 (Thu) 16:00 - 18:00, 2026

Mang Hei Gordon Lee (Post-Doctoral fellow, Leung Center for Cosmology and Particle Astrophysics, National Taiwan University, Taiwan)

Currently there are hundreds of models describing inflation, a period of accelerated expansion in our universe. Each model lead to different imprints in cosmological observables, and for the purpose of testing the idea of inflation itself, it is essential to understand which predictions are model independent. This lead to the idea of cosmological bootstrap, a set of constraints from physical principles and symmetries alone. In this talk I will give an overview on the cosmological bootstrap program. I will first explain how locality, unitarity and symmetry can constrain the kinematics of cosmological correlators. I will then talk about some recent progress on constructing positivity bounds on cosmology, which places constraints on the interactions of fields in inflation.

Venue: Hybrid Format (3F #359 and Zoom), Main Research Building

Event Official Language: English

Introduction to categorification and link homology

May 28 (Thu) 14:00 - 15:30, 2026

Mikhail Khovanov (Professor, Department of Mathematics, Johns Hopkins University, USA)

Quantum link invariants relate topology in 3 dimensions to mathematical physics and representation theory. They admit liftings to 4-dimensional structures, known as link homology. We will explain how the skein relations for quantum invariants turn into homological structures at this higher level and how semisimple representation theory turns into non-semisimple representations and homological algebra upon categorification.

Venue: Okochi Hall (Main Venue) / via Zoom

Event Official Language: English

Towards rock-solid evolutionary genomics

May 28 (Thu) 13:00 - 14:00, 2026

Leo Speidel (RIKEN ECL Unit Leader, Mathematical Genomics RIKEN ECL Research Unit, Division of Fundamental Mathematical Science, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS))

I will present an overview of ongoing and future projects in our lab. We aim to understand how human genomes retain information about our evolutionary past; a central goal is to reconstruct a high-resolution history of humans, pushing the limits of what we can learn about our origins, past migrations, and adaptation to changing environments and survival pressures. Our genomes reveal events that would otherwise be lost to history, revealing how evolutionary forces have shaped genetic variation and influence our health today. How can we confidently infer events that occurred tens of thousands of years ago? I will discuss how converging and independent lines of genomic evidence can provide “rock-solid” support for major evolutionary events, including archaic admixture, large-scale migrations across continents, and population bottlenecks, and how we aim to extend these approaches to study the evolutionary history and origins of humans and other species.

Venue: via Zoom / Seminar Room #359

Event Official Language: English

Cooperating on networks: inequality and social structure

May 27 (Wed) 14:00 - 15:00, 2026

Manuel Staab (Lecturer, University of Queensland, Australia)

We analyse how inequality in endowments and social structure jointly affect individuals' ability to cooperate. Individuals repeatedly invest in a local public good ("cooperation'') in an environment that is described by a distribution of endowments and a network of beneficiaries. We measure the cooperativeness of an environment by the minimum discount factor needed to sustain (any) cooperation in equilibrium. We characterise the endowment distribution that maximises cooperativeness for any given network and the corresponding minimum discount factor. The latter is shown to be inversely proportional to the maximal index of the graph describing the network. The corresponding dominant eigenvalue of the adjacency matrix characterises the most cooperative income distribution. Moreover, we show that if an environment maximises cooperativeness (over all income distributions and networks of a certain size), then the network is described by a nested split graph. We further show that this is the same class of graphs that maximise welfare for any given discount factor, and yet, the most cooperative graph need not be equal to the most efficient.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

Harnessing inequality for cooperation

May 26 (Tue) 14:00 - 15:00, 2026

Maria Kleshnina (Senior Lecturer, School of Mathematical Sciences, Queensland University of Technology, Australia)

Inequality in resources is widely thought to undermine cooperation in social dilemmas. Yet cooperation among unequals is ubiquitous: between senior and junior colleagues, firms of different sizes, nations with asymmetric stakes. Here, we offer a resolution to this puzzle and derive a novel prediction: if the returns from cooperation are shared in accordance with the individuals' strategic incentives, inequality enables and strengthens cooperation. We develop a strategic framework to systematically explore cooperation when the returns of a joint project can be shared unevenly. We characterise the optimal sharing rule, which we call resilient sharing, that can sustain cooperation in repeated interactions when no other rule can. Resilient sharing equalises incentives to defect across players, but is neither egalitarian nor proportional. Surprisingly, it typically rewards weaker partners beyond their relative contributions. We show that cooperation can be sustained through direct reciprocity in any environment whenever individual contributions are sufficiently unequal. Evolutionary simulations and a behavioural experiment confirm the central prediction: under resilient sharing, cooperation succeeds among unequal partners where it fails among equals. This suggests that cooperation is more likely to evolve and thrive when individuals can vary contributions and divide returns flexibly, pointing to the role of institutions and norms in harnessing inequality to stabilize cooperation.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English