Tidlegare gjesteforelesninger og seminarer - Side 14
Clara Bertinelli Salucci is a Postdoctoral Fellow at the Department of Mathematics, affiliated with the PLUMBIN' (Physics Learning Using Machines and Bayesian INference) project at the University of Oslo. She completed her PhD in Statistics and Machine Learning at the University of Oslo in April 2024, where she developed statistical and machine learning methods for diagnostics and prognostics of lithium-ion batteries for maritime applications. Currently, she is investigating the role of likelihood ratio test statistics for hypotheses tests in experimental and theoretical particle physics, focusing on deviations from Wilks' theorem in the search for new particles. Her postdoctoral research combines her expertise in statistics with her background in high-energy physics.
Atmospheric clouds are complex three-phase systems containing ice crystals, supercooled liquid droplets, and water vapor. They are responsible for rain/snow formation and play a primary role in the planet’s radiative energy budget and the water cycle. In this talk, after describing the peculiar thermodynamics problems of water below 0 °C and the relative open scientific questions, I will present our first results on the role of turbulence in the condensation/evaporation processes in mixed-phase clouds where ice crystals grow at the expense of liquid droplets.
It is time for our monthly "mingle" meeting. Students and employees at the institute will meet up in the lobby.
Recent experiments have shown that epithelial monolayers can behave as self-propelled solid elastic membranes, and that global directed motion (polar order) emerges through the hierarchical annihilation of contractile elastic vortices and their anti-vortices[1]. Polar ordering dynamics in fluid cell layers is often modelled by neighbour alignment (Vicsek or Vicsek like models), another model is collective flow-alignment (move in the direction of flow). Here I will discuss the differences and similarities of these two mechanisms in solids. Both can give rise to interacting elastic vortices , but one model is essentially linear, while the other is non-linear and exhibit a rich dynamics which likely is relevant to many types of biological cell layers in the solid state. [1] E. Lång, A. Lång, P. Blicher, T. Rognes, P.G. Dommersnes, S. O. Bøe, Topology-guided polar ordering of collective cell migration, Sci. Adv. (2024)
Claudia Cicone, Cosmology and extragalactic astronomy research group, Institute of Theoretical Astrophysics.
By Prof. Erik Stensrud Marstein, Chief scientist PV at IFE, and Center Director FME SUSOLTECH and FME SOLAR
Note the non-standard time!
In the first part of the talk, I will give an introductory talk to o-minimality, giving the definitions and some of intuition through understanding which Lie groups are amenable to being understood in o-minimal contexts.
In the second part I will state the two theorems which have had the most important applications to algebra and give some intuition of how (at least one of them) has been used.
Dr Kevin Burke is an Associate Professor of Statistics at the University of Limerick (UL) with research interests in flexible statistical modelling, penalised regression, neural networks, random effects, survival/reliability analysis, and industrial modelling. See https://kevinburke.ie/ for more details. Previously he was Director of UL's Industrial Mathematics Unit (from 2016 - 2020), and a Science Foundation Ireland Public Service Fellow with the Food Safety Authority of Ireland (2020 - 2021). He is the Vice President of the Irish Statistical Association, and the Chair of the International Workshop on Statistical Modelling (IWSM) 2025. IWSM 2025 will be hosted in Limerick City, Ireland, from Sunday 13th July to Friday 18th July 2025; the call for papers is currently open (https://iwsm2025.ie/call-for-papers).
The efficient redistribution and control of flow is essential in many biological and engineered systems, from our cardiovascular system to plants and soft robotics. Inspired by nature, microfluidic devices with passive valves have been developed to perform a variety of tasks, from cell manipulation to fluid mixing and reaction control, giving rise to the field of soft hydraulics. In this talk, we present two prototypes of passive valves: one harnessing buckling in slender beams and another leveraging snapping in spherical shells, where fluid-induced elastic instabilities are tamed to achieve function. Through a combination of precision desktop-scale experiments, numerical simulations, and theoretical analysis, we rationalize these behaviors and provide design rules based on scaling regimes of material, geometric, and fluid parameters.
Erik Asphaug, University of Arizona and PHAB, UiO.
In 2008, looking to bound the face vectors of tropical linear spaces, Speyer introduced the g-invariant of a matroid. He proved its coefficients nonnegative for matroids representable in characteristic zero and conjectured this in general. Later, Shaw and Speyer and I reduced the question to positivity of the top coefficient. This talk will overview work being written up with Andy Berget (Western Washington) that proves the conjecture. The first half will be introductory, aiming to define the g-invariant, state the theorem, and compare to other recent work in matroid theory. The second half will look at the proof techniques.
Geometrically, the main ingredient in the proof is a Kempf collapsing constructed from the matroid tautological vector bundles of Berget--Eur--Spink--Tseng, and an initial degeneration thereof. Combinatorially, it is an extension of the definition of external activity to a pair of matroids and a way to compute it using the fan displacement rule on tropical Chern classes. The work of Ardila and Boocher on the closure of a linear space in (P^1)^n is a special case.
Ada Ortiz-Carbonell, Expert Analytics AS and European Solar Telescope Canarian Foundation.
Based on J. Kollár and C. Voisin. Flat pushforwards of Chern classes and the smoothability of cycles below the middle dimension. Ann. of Math. (2), 200(2):771–797, 2024.
QOMBINE seminar by Timo Hillmann (Chalmers)
Leiv Rønneberg is a DSTrain Postdoctoral Fellow at the Department of Mathematics and affiliated with The Norwegian Centre for Knowledge-driven Machine Learning (INTEGREAT) at the University of Oslo. Prior to this he was a Research Associate at the MRC Biostatistics Unit at the University of Cambridge, and completed his PhD in Biostatistics at the University of Oslo in 2022. His interests are broadly within the realms of probabilistic machine learning, and connections to Bayesian nonparametrics.
Earthquake, landslide, and glacier instabilities are multi-scale processes that cannot be predicted. When a rock is subjected to large stress, it can develop fractures that grow and connect to form a network. This network of fractures spans the entire rock right before it fails catastrophically. Despite extensive research on rock damage, researchers are still far from being able to predict when a rock will fail. Here, I will show how to use a multi-view convolutional neural network model to identify characteristics of a failing rock. By training a neural network model on images of rock samples exposed to different levels of stress, one can predict how close a rock is to failure. The neural network model outperforms traditional estimates based on fracture density. More importantly, the model provides fundamental insight that may offer precursory information on impending material failure. For instance, according to the model, the angle of the fracture plane relative to the principal loading direction becomes a key factor contributing to failure.
Eilif Sommer Øyre, Rosseland Centre for Solar Physics (RoCS), UiO.
Based on preprints https://arxiv.org/abs/2006.07213 and https://arxiv.org/abs/2406.05382.
QOMBINE Seminar by Chloe Kim (University of Illinois Urbana-Champaign)
By Prof. François Renard, University of Oslo
QOMBINE seminar by Roy Araiza (University of Illinois at Urbana-Champaign)