English version of this page

Gjesteforelesninger og seminarer - Side 11

Tid og sted: –, NHA UE26

QOMBINE seminar by Jonas Eidesen (University of Oslo)

Tid og sted: –, NHA B1120
Tid og sted: –, UE26

C*-seminar by Michelle Göbel.

Tid og sted: –, Niels Henrik Abels hus, room 919

AI has made enormous progress along different domains in the last decades, from playing complex games, to analyzing and generating images and text. However, we have so far not seen the same impressive capabilities in AI for robotics. Why is that? In this presentation, I will talk about a few of the problems that makes AI for robotics a unique challenge, and what we at the group for Robotics and Intelligent Systems (ROBIN) are doing to address those challenges.

 
Tid og sted: –, UE26

C*-seminar by Johannes Christensen.

Tid og sted: –, NHA B1120
Tid og sted: –, Niels Henrik Abels building,room UE26

With the fast-paced evolution of data sciences, a deeper grasp of probabilistic methods is essential. This course studies high-dimensional probability, with applications in data sciences, focusing on random vectors, matrices, and projections in large dimensions.

Tid og sted: –, NHA UE26

C*-seminar by Are Austad (University of Oslo)

Tid og sted: –, Room 1020 NHA

The section 4 seminar will be held Tuesdays 10:15–11:00 in room 1020

Tid og sted: –, NHA B1119
I will explain how to extend the concept of spin structures from vector bundles to complexes over a scheme (or algebraic stack) X outside of characteristic 2. For a complex E on X with an oriented quadratic structure one obtains an associated Z/2Z-gerbe over X obstructing the existence of a spin structure on E. I will also touch on an expected alternative definition of spin structures on complexes using derived Clifford algebras. One application of the concepts presented is to give a refinement of the virtual structure sheaf of Oh-Thomas in Donaldson-Thomas theory of Calabi-Yau fourfolds.
Tid og sted: –, NHA720

QOMBINE seminar by Maximilian Mansky (LMU, Munich)

Tid og sted: –, UE32

C*-seminar by Roberto Conti (Sapienza University of Rome)

Tid og sted: –, Erling Svedrups plass

 

Sylvia Frühwirth-Schnatter is a distinguished Austrian statistician and professor of applied statistics and econometrics at the Vienna University of Economics and Business. Renowned for her research in Bayesian analysis, she served as President of the International Society for Bayesian Analysis.

Professor Frühwirth-Schnatter has an impressive academic background, including a doctorate in engineering mathematics from TU Wien and previous positions at Johannes Kepler University Linz. Since 2011, she has been a full professor at the Vienna University of Economics and Business. She is also a Full Member of the Austrian Academy of Sciences.

Her research focuses on Bayesian econometrics, including Markov chain Monte Carlo methods and finite mixture models. She has received numerous accolades, including the WU Best Paper Award and the DeGroot Prize for her work on Markov switching models.

 

Tid og sted: –, NHA UE32

C*-seminar by Gerardo Morsella.

Tid og sted: –, NHA 107

C*-seminar by John Quigg (Arizona State University).

Tid og sted: –, Auditorium 1 Vilhelm Bjerknes' hus - University of Oslo

The 5th Scandinavian Gathering Around Remarkable Discrete Mathematics

Tid og sted: –, NHA B1120

I will talk about how one can relate intersection theories of Hilbert schemes of points and Fulton-MacPherson compactifications.

Tid og sted: –, Erling Svedrups plass and Zoom https://uio.zoom.us/j/66503159220?pwd=alhPVFpHNUxVUTNoeHhIcVFtUWx4UT09
In this talk I will cover the recent advances in the theory of power law distributions, in particular the role of Markov modulation and random stopping emphasized by Beare and Toda (2022), which builds on Nakagawa (2007)’s Tauberian theorem. Applications include the emergence of Zipf’s law in Japanese cities and the spread of COVID-19. I will also present open mathematical problems.
 
• Beare, Brendan K., and Alexis Akira Toda. "Determination of Pareto exponents in economic models driven by Markov multiplicative processes." Econometrica 90.4 (2022): 1811-1833.
• Nakagawa, Kenji. "Application of Tauberian theorem to the exponential decay of the tail probability of a random variable." IEEE Transactions on Information Theory 53.9 (2007): 3239-3249.
• Beare, Brendan K., and Alexis Akira Toda. "On the emergence of a power law in the distribution of COVID-19 cases." Physica D: Nonlinear Phenomena 412 (2020): 132649.
 
Tid og sted: –, NHA 107

C*-algebra seminar by Eduard Vilalta (Chalmers University of Technology / University of Gothenburg)

Tid: –

QOMBINE seminar talk by Franz Fuchs (University of Oslo)

Tid og sted: –, NHA B1120
Donaldson-Thomas invariants "virtually" count curves in a given threefold. They factor into two parts: a part which only counts curves, and a degree 0 part, which counts 0-dimensional subschemes. The degree 0 part can be fully computed with a closed formula by relating them to combinatorial counting of plane partitions, which are certain configurations of boxes in 3D space. DT theory comes in various refinements. Nekrasov's formula refines the relation to counts of plane partitions to equivariant K-theoretic DT theory and gives a closed formula for refined degree 0 DT invariants.
 
Degree 0 DT invariants of orbifolds are related to counts of colored plane partitions, where the boxes are colored in a way determined by the orbifold structure. This allows the computation of closed formulas for some orbifolds. We refine these closed formulas to equivariant K-theoretic DT theory by modifying the techniques used in Okounkov's proof of Nekrasov's formula to work for orbifolds. We will explain these techniques in the case of schemes and describe some of the modifications to make them work for orbifolds.
Tid og sted: –, Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor
Climate and weather can affect disease prevalence in different ways. For instance, humidity and temperature affect the life cycles of mosquitos which can greatly influence the prevalence of vector-borne diseases like malaria and dengue. Modelling this relationship is very important, both in the short term for outbreak preparedness, and in the long term, for health systems to adapt to the changing climate. However, this modelling is difficult because of low amounts of quality health data, complexities in spatial-temporal modelling, and the many different domains (vector biology, climate, epidemiology).
In this talk I will present our work on building a framework both for developing modularized and adaptable climate-health models, and for rigorously evaluating the utility of these models.