Seminar

Hamiltonian simulation: Standard algorithms and recent developments

December 17 (Thu) 10:30 - 17:00, 2026

Kaoru Mizuta (Associate Professor, Center for Quantum Information and Quantum Biology, The University of Osaka)

This is a lecture-style seminar of quantum computation. The tentative schedule consists of three slots: 10:30-12:00, 13:30-15:00, and 15:30-17:00. Due to equipment limitations, the blackboard might not be visible on Zoom, so we recommend attending in person.

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

Event Official Language: English

A non-negative matrix factorization-based approach to integrating multimodal omics data with automatically tuned hyperparameters

November 16 (Mon) 14:00 - 15:00, 2026

Dorothy Ellis (Postdoctoral Researcher, Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences (IMS))

Venue: via Zoom

Event Official Language: English

Scaling regimes of the 1D Kuramoto-Sivashinsky equation

November 5 (Thu) 17:00 - 18:30, 2026

Liubov Gosteva (Ph.D. Student, LPMMC, Université Grenoble Alpes, France)

The Kuramoto-Sivashinsky (KS) equation describes various phenomena exhibiting chaos and instabilities, including flames, reaction-diffusion processes, and thin liquid film flows. It resembles the Kardar-Parisi-Zhang (KPZ) equation, with the key difference being the negative "viscosity" in the KS equation. The KS equation is known to belong to the KPZ universality class: at large scales, its statistical properties are governed by the KPZ fixed point, characterized by the dynamical exponent $z=3/2$ and a known PDF of fluctuations. In this talk, I will analyze the stochastic KS equation using the functional renormalization group (FRG). In this framework, attractive fixed points of the FRG equation determine large-scale universal behavior, while unstable fixed points govern small-scale universal behavior. I will show that the two-point correlation function exhibits, besides the KPZ scaling at large scale, another universal scaling regime at small scale, with $z=1$. This scaling is very robust as it is intrinsic to the KS dynamics. It arises from the vanishing of the effective viscosity when evolving from its microscopic negative KS value, to its macroscopic effective positive KPZ value. I will also present results from direct numerical simulations, which confirm these findings.

Venue: via Zoom

Event Official Language: English

Quantum Creation of the Universe: Reassessing the No-Boundary and Tunneling Proposals

October 30 (Fri) 14:00 - 15:00, 2026

Hiroki Matsui (Researcher, Osaka Central Advanced Mathematical Institute, Osaka Metropolitan University)

How was our Universe created? This is one of the most fundamental questions in cosmology. Quantum cosmology attempts to answer it by using a "wave function of the Universe," and for four decades the discussion has been dominated by two proposals: the no-boundary proposal of Hartle and Hawking, and the tunneling proposal of Vilenkin. The two predict very different initial states for our Universe, yet for a long time there was no common framework in which they could be exactly analyzed and compared. In this talk I will begin with a pedagogical introduction to the wave function of the Universe and to the Lorentzian path integral formulation based on Picard-Lefschetz theory, which makes the gravitational path integral well defined without the ambiguities of the traditional Euclidean approach and allows both proposals to be treated on the same footing. I will then present our recent results. Using Lefschetz-thimble analysis together with resurgence theory, we resolve the Stokes-line ambiguity that had obscured earlier analyses and show that the path integral uniquely yields the tunneling wave function rather than the no-boundary one. The no-boundary saddles nevertheless leave a trace: their contributions exactly cancel the Borel ambiguity of the perturbative quantum-gravity corrections around the tunneling saddle, demonstrating that resurgence is at work in quantum cosmology. I will also discuss what changes beyond general relativity, using Hořava-Lifshitz gravity as a concrete UV-complete example.

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

Event Official Language: English

AI and Scientific Discovery

October 19 (Mon) 14:00 - 15:30, 2026

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.

Venue: #435-437, Main Research Building (Main Venue) / via Zoom

Event Official Language: English

From data to cohomologies of digraphs, to representations of categories

October 13 (Tue) 10:30 - 12:00, 2026

Luigi Caputi (Research Fellow, Department of Mathematics, University of Bologna, Italy)

Motivated by applications to topological data analysis, in the last years we have been witnessing a growing interest in new (topological, homological, categorical) features associated to graphs. The aim of the talk is to describe a recently developed (co)homology theory of directed graphs, called multipath cohomology, whose definition is inspired by Khovanov homology of knots. We will show its relations with more established invariants, such as chromatic homology of graphs, Hochschild homology of algebras, and matching complexes. If time allows it, we will show that the ranks of multipath cohomology, when restricted to graphs with bounded genus, grow at most polynomially in the number of edges, and that the order of its torsion is bounded. This will depend upon structural properties of (the representation category of) graphs, such as its Noetherianity properties.

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

Event Official Language: English

Adaptive Regulation through Model-Observation Mismatch: From Process Control to Cognitive Neurorobotics

October 8 (Thu) 13:00 - 14:00, 2026

Henrique Oyama (Postdoctoral Researcher, Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University (OIST))

Adaptive behavior requires biological and artificial systems to respond when their internal models no longer match what they observe. How can such discrepancies be used not only to correct predictions, but also to regulate how a system interacts with its environment? In this talk, I will explore this question across two research directions spanning process control and cognitive neurorobotics. First, I will introduce a model predictive control framework in which a controller actively guides a nonlinear process toward informative conditions to distinguish among competing models while maintaining safe operation. I will then move to cognitive neurorobotics, where brain-inspired computational principles are used to investigate perception, prediction, and action in embodied artificial agents. In physical human-robot interaction, I will show how a predictive-coding-inspired recurrent neural network enables a humanoid robot to regulate the balance between its learned expectations and sensory information arising from human-guided movements. In particular, recent mismatches between sensory observations and the robot's internal model are used to regulate how strongly it relies on its learned dynamics or accommodates ongoing interaction. Together, these studies illustrate how model-observation mismatch can provide a general signal for adaptive regulation, connecting ideas from control engineering, artificial intelligence, neuroscience, and embodied cognition.

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

Event Official Language: English

Evolutionary Dynamics and Information Theory

October 1 (Thu) 15:00 - 16:00, 2026

Marc Harper (Data Scientist, Google Inc., USA)

We'll discuss some of the history of connections among information theory, evolutionary dynamics, and statistical inference, drawing analogies between concepts and results in these fields, for example between the replicator equation (a model of natural selection) and Bayesian inference. Time permitting, we'll continue with generalizations to finite populations with mutation and/or variations including an analogy between epigenetics from biological evolution and momentum from machine learning.

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

Event Official Language: English

Coexistence and hybridization in plant communities - lessons from mediterranean shrublands

October 1 (Thu) 13:00 - 14:00, 2026

Carmelo Gómez Martínez (Postdoctoral Researcher, Global Change Research Institute, Universidad Rey Juan Carlos, Spain)

This talk explores how two closely related Mediterranean shrubs, the rockroses Cistus ladanifer and Cistus laurifolius, share the same habitat despite being direct competitors for soil, light and pollinators. Hybridization – the natural interbreeding of two related species – often produces offspring that blend both parents' traits, and is surprisingly common in plants, including the Cistus genus. Fieldwork across Spain's Iberian Central System combining genomics, flowering timing and soil experiments shows that no single factor by itself – not genetics, climate or soil – fully explains these dynamics. The next step is to study how pollination shapes hybridization, and for that, we are developing and agent-based model that simulates bee foraging across realistic landscapes to predict new hybrid outcomes under environmental change.

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

Event Official Language: English

121st Data Assimilation and Prediction Science Seminar

September 28 (Mon) 14:00 - 16:00, 2026

Shunji Kotsuki

Title: "Exploring Live Cameras for Actionable Weather Prediction: Recent Studies with Chiba University’s BAECAST Network" Abstract: Numerical weather prediction has advanced substantially through improvements in observing technologies, data assimilation methods, and numerical weather prediction (NWP) models. More recently, artificial intelligence (AI) has increasingly been used to develop surrogates for NWP models and to advance nonlinear and non-Gaussian data assimilation. These developments are expected to enable more effective use of large volumes of observational data, including satellite observations. To further improve weather forecast accuracy, it is important not only to refine data assimilation methods and NWP models but also to explore new sources of observations. Our research focuses on the vast amounts of data generated by live-streaming cameras already deployed in everyday settings. Many of these cameras capture aspects of weather conditions, and harnessing this information could contribute to further improvements in weather prediction. This talk will present several studies on weather forecasting and diagnostics using live-streaming cameras installed at Chiba University. I will introduce BAECAST, an approach to visual weather forecasting, and discuss which meteorological variables can be retrieved from live-camera imagery. I will also present our efforts to transform the nature of weather forecasts using live-camera data using a conditional diffusion model.

Venue: Hybrid Format (RIKEN R-CCS room C107 and Zoom) / via Zoom

Event Official Language: English