Ricevo e inoltro. Il seminario si terrà ad Atlanta il 31 gennaio alle ore (per noi) 21:00.
Cordiali saluti
Giuseppina.
Giuseppina Guatteri
Dipartimento di Matematica
Politecnico di Milano
Via Bonardi, 9
20133 Milano
tel.: +390223994556
fax.:+390223994513
email: giuseppina.guatteri(a)polimi.it
"Happiness can be found even in the darkest of times, if one only remembers to turn on the light" A. Dumbledore
________________________________
________________________________
From: pde-seminar-request(a)lists.gatech.edu <pde-seminar-request(a)lists.gatech.edu> on behalf of Chen, Gong <gc(a)math.gatech.edu>
Sent: Sunday, January 29, 2023 6:33 AM
To: pde-seminar(a)lists.gatech.edu <pde-seminar(a)lists.gatech.edu>
Subject: [pde-seminar] PDE seminar with Filippo de Feo
Dear all,
On Tuesday, Jan 31, we will have our visitor Filippo de Feo from Politecnico di Milano to speak for our PDE seminar in person in Skiles 006.
Filippo’s talk information can be found here
https://math.gatech.edu/seminars-colloquia/series/pde-seminar/filippo-de-fe…
The talk will also be streamed on zoom: https://gatech.zoom.us/j/95574359880?pwd=cGpCa3J1MFRkY0RUeU1xVFJRV0x3dz09 Meeting ID: 955 7435 9880<tel:7435%209880> Passcode: PDE
Best wishes,
Gong
Dear Colleagues,
We would like to invite you to the following SPASS seminar, jointly
organized by UniPi, SNS, UniFi and UniSi:
*Convergence rates and CLT for stochastic inviscid Leray-model with
transport noise*
*Dejun Luo* (Chinese Academy of Science)
The seminar will take place on TUE, 31.1.2023 at 14:00 CET in Sala
Seminari, Dipartimento di Matematica, Pisa and streamed online at the link
below.
The organizers,
A. Agazzi, G. Bet, A. Caraceni, F. Grotto, G. Zanco
https://sites.google.com/unipi.it/spass
--------------------------------------------
*Abstract: *
*The stochastic inviscid Leray- model perturbed by multiplicative transport
noise is considered on the torus. Under a suitable scaling of the noise, it
is shown that the weak solutions converge, in some negative Sobolev spaces,
to the unique solution of the deterministic viscous Leray- model.
Interpreting such limit result as a law of large numbers, we also study the
underlying central limit theorem and provide an explicit convergence rate.
This talk is based on a joint work with PhD Bin Tang.*
Dear colleagues,
I would like to inform you that
*Dr. Nicola Turchi* (University of Milano-Bicocca) will deliver a PhD
course entitled
*"Concentration of probability measures"*
from *January 30th* to April 3rd 2023* in presence* at the Department of
Mathematics and its Applications, University of Milano-Bicocca, and *live
streaming*.
*For more information*
https://drive.google.com/file/d/1pRkD4HP2vMGE_AssgQ4LR75TqzCj0wvb/view
Please do not hesitate to write an email to *nicola.turchi(a)unimib.it
<nicola.turchi(a)unimib.it>* should you need further information.
Best regards,
Maurizia Rossi
--
Maurizia Rossi
Dipartimento di Matematica e Applicazioni
Università degli Studi di Milano-Bicocca
https://mauriziarossi.wordpress.com
Dear Colleagues,
The department of Mathematics at the TU Chemnitz invites applications for two 3 years Ph.D. positions to work on projects in "Physics-informed or theory-guided machine learning." The deadline for the application is 10.02.
More information on the call can be found in the link below.
https://www.tu-chemnitz.de/verwaltung/personal/stellen/224034_1_LH.php
Don't hesitate to write to imma.curato(a)uni-ulm.de<mailto:imma.curato@uni-ulm.de> for any position inquiries.
Best Regards
Imma Curato
Con preghiera di massima diffusione a tutti i potenziali interessati.
***
Nell'ambito del Progetto di Partenariato Esteso GRINS è appena stata bandita una posizione RTD-A nel settore SECS-S/01 sul tema
Statistical Learning per il progetto GRINS - Growing Resilient, INclusive and Sustainable
Scadenza: 13-02-23
Dettagli della procedura: bando<https://www.polimi.it/personale-docente/lavorare-al-politecnico/bandi-e-con…>
L’attività di ricerca verterà sullo sviluppo di modelli statistici per la previsione ex-ante e la valutazione ex-post dell'impatto degli investimenti in infrastrutture e servizi per migliorare l'accessibilità, la resilienza e la sostenibilità dei territori e delle città (WP2 Progetto GRINS – PNRR PE9 Spoke7). Sarà necessario comprendere come i fattori tangibili (infrastrutture e infostrutture) interagiscono con quelli intangibili (coesione e inclusione sociali, mobilità,…) per definire la sostenibilità di un territorio e a) identificare gli elementi funzionali a rendere i territori accessibili; b) individuare i gap territoriali e che limitano la sostenibilità e c) definire e valutare politiche per colmare tali gap.
Sono richieste competenze avanzate di Statistical Learning e Analisi Dati, e comprovata conoscenza di Data Management e programmazione (R, Python).
Il ruolo prevede lo svolgimento di attività didattica.
Per informazioni: Prof. Francesca Ieva – francesca.ieva(a)polimi.it<mailto:francesca.ieva@polimi.it>
——
Laura Maria Sangalli
MOX - Dipartimento di Matematica
Politecnico di Milano
Piazza Leonardo da Vinci 32
20133 Milano - Italy
(+39) 02 2399 4554
laura.sangalli(a)polimi.it<mailto:laura.sangalli@polimi.it>
https://sangalli.faculty.polimi.it
Buongiorno,
con piacere segnalo il seguente seminario:
--------------------------------
31 gennaio ore 16.30
- aula 101, campus Perrone, via Perrone, 18, Novara. Università del
Piemonte Orientale.
- on-line: meet.google.com/yvq-ccbt-pzt
Title: *An Introduction to Saddlepoint Approximations*
*Prof. Elvezio Ronchetti*
Research Center for Statistics
and Geneva School of Economics and Management
University of Geneva, Switzerland
Elvezio.Ronchetti(a)unige.ch
www.unige.ch/gsem/en/research/faculty/honorary-professors/elvezio-ronchetti/
Abstract: Classical inference in statistics is typically carried out by
means of standard (first-order) asymptotic theory. However, the asymptotic
distribution of estimators and test statistics can provide a poor
approximation of tail areas especially when the sample size is moderate to
small. This can lead to inaccurate p-values and confidence intervals.
Several techniques, both parametric and nonparametric, have been devised to
improve first-order asymptotic approximations, including e.g. Edgeworth
expansions, Bartlett's corrections, and bootstrap methods. Here we focus on
saddlepoint techniques, introduced into statistics by H. Daniels, and more
generally on small sample asymptotic techniques, an expression coined by F.
Hampel to express the spirit of these methods. Indeed they provide very
accurate approximations of tail probabilities down to small sample sizes
and /or out in the tails. Moreover, these approximations exhibit a relative
error of order 1/n, an improvement with respect to other available
approximations obtained by means of Edgeworth expansions and similar
techniques.
We will review the basic ideas, show the link with other nonparametric
methods such as empirical likelihood, and outline some connections to
information theory and optimal transportation.
-----------------------------------
Locandina dell'evento
<https://drive.google.com/file/d/1INut-6sD210FwZ437k3ydQA5P3xchdIr/view?usp=…>
Seminari Matematici Statistici
<https://sites.google.com/uniupo.it/seminari-ms/home-page>
-----------------------------------
Tutti gli interessati sono invitati a partecipare.
Cordiali saluti,
Enea
--
Enea Bongiorno,PhD
Associate Professor of Statistics
Università degli Studi del Piemonte Orientale
Via Perrone 18, 28100, Novara, Italia
Phone: +390321375317
enea.bongiorno(a)uniupo.it
upobook.uniupo.it/enea.bongiorno
We announce the following seminar (held only in person):
26/01/2023 at 12:00
Bocconi University, Via Roentgen 1, Milan
Room 3-B3-sr01, floor 3
Speaker: Olga Klopp (ESSEC and CREST).
Personal webapge: http://kloppolga.perso.math.cnrs.fr/
Title: Optimality of Variational Inference for Stochastic Block Model
Abstract: Variational methods are extremely popular in the analysis of
network data. Statistical guarantees obtained for these methods typically
provide asymptotic normality for the problem of estimation of global model
parameters under the stochastic block model. In the present work, we
consider the case of networks with missing links that is important in
application and show that the variational approximation to the maximum
likelihood estimator converges at the minimax rate. This provides the first
minimax optimal and tractable estimator for the problem of parameter
estimation for the stochastic block model. We complement our results with
numerical studies of simulated and real networks, which confirm the
advantages of this estimator over current methods.
See also
https://bidsa.unibocconi.eu/newsevents/bidsa-seminar-series-optimality-vari…
.
Best regards,
Giacomo Zanella