The Department of Economics at the University of Verona is looking for a tenure track researcher in the field of Financial and Insurance Mathematics (SECS-S/06 - 13/D4).
The Department of Economics offers a vibrant research environment in the newly built campus of Santa Marta. According to the latest Italian Research Assessment Exercise (VQR), it ranks in the top 5% of the economics departments for the number of academics with only top publications and, according to REPEC, is in the top 10% in Italy for the proportion of cited publications.
The Verona Quantitative Finance group offers a stimulating environment with researchers focusing on different areas such as: high frequency financial econometrics and asset pricing, mathematical/quantitative finance, stochastic optimal control and computational finance.
The position is a fixed-term 3-year position (RTDB). If the researcher receives the National Scientific Habilitation (ASN), at the end of the 3 years then he/she becomes a tenured Associate Professor.
A minimum of 3 years of postdoc experience is required together with a working knowledge of Italian.
The call and the online application form can be accessed via the following link.
https://www.univr.it/it/concorsi/personale-docente/ricercatore/ricercatore-… <https://www.univr.it/it/concorsi/personale-docente/ricercatore/ricercatore-…>
For questions, please contact: alessandro.gnoatto(a)univr.it <mailto:alessandro.gnoatto@univr.it>
Bernoulli Society welcomes applications for the New Researcher Award 2022.
The deadline for applications is 15 March 2021.
Each awardee shall deliver a talk at a special invited session during the
42nd Conference on Stochastic Processes and their Applications in 2022, and
will receive a funding up to 1000€ to offset travel and other expenses.
Eligible candidates are active researchers in Probability Theory who
obtained their PhD degree on or after March 1st, 2016, and who are regular
members of the Bernoulli Society.
Candidates should apply through the web form
https://docs.google.com/forms/d/e/1FAIpQLSdajP4NBGSWFXHJxoVCQTmJMpv9rM2ZRNV…
For more information, visit the website
http://www.bernoulli-society.org/news/37-general-announcement/324-bernoulli…
--
Antonio Di Crescenzo
Dear Colleagues,
We would like to invite you to the following Probability seminar
that will take place on February 26 at 14.30 by the zoom platform.
________________________________________________________
Speaker: Luca Avena (Leiden University)
Title: Node immunization in networks: a scalable searching algorithm based
on random rooted forests
26 FEBRUARY (Friday) - 14:30 zoom link: TBA
available on the webpage https://www.math.unipd.it/~bianchi/seminari/ )
Abstract:
We are interested in the so-called multiple-node immunization for complex
networks under attack of a viral agent. The latter is a hot topic in
network science and it consists of identifying and removing a set of nodes
of a given size in a graph to maximally impede the agent spread. Several
approaches have been proposed in the literature based on numerical and
theoretical insights on how classical models for virus spread (so-called
compartmental models) evolve on graphs.
Based on the stability analysis of these compartmental models, the maximal
eigenvalue of the adjacency matrix of the graph has been proposed as a
measure for how resilient the network is. Thus one of the most common
approaches for immunization consists in identifying the set of nodes of a
given cardinality, for which the reduced network, obtained by removing
these nodes, has minimal largest eigenvalue. This optimization problem
turns out to be a computationally hard problem in the well known NP class
and the available exact or proxy algorithms offer good solutions and
performances only for small data sets.
We propose a novel randomized flexible method to efficiently identify these
sets of nodes based on random walk kernels and random rooted forests. We
explain the theoretical results underlying the method, and present
experimental results where we test method and performances on classical
synthetic and real-world benchmarks.
Joint work with Michael Emmerich, Alex Gaudilliere and Irina Gurewitsch
--
Alessandra Bianchi
Dip. di Matematica
Università di Padova
Via Trieste, 63 - 35121 Padova, Italy
phone: +39 049 827 14 06
fax: +39 049 827 14 28
e-mail: bianchi(a)math.unipd.it
http://www.math.unipd.it/~bianchi/
WEBINARS IN STATISTICS @ COLLEGIO CARLO ALBERTO
<https://www.carloalberto.org/events/category/seminars/seminars-in-statistic…>
Venerdi 26 Febbraio 2021, alle ore 17:00, si terrà il seguente webinar:
------------------------------------------------
Speaker: *Yun Wei (*Samsi and Duke University, USA)
Title: *Obtaining faster convergence rates in finite mixture models by
taking repeated measures*
Zoom link:
https://us02web.zoom.us/j/89954920036?pwd=dndsQnZqQ2crZzVlRW1pM0Q2RGo1Zz09
Meeting ID: 899 5492 0036
Passcode: 418668
Abstract:
It is known that some finite mixture models suffer from slow rates for
estimating the component parameters. Examples are mixtures of the weakly
identifiable families in the sense of [Ho and Nguyen 2016]. To obtain
faster parameter convergence rates, we propose to collect more samples from
each mixture component, hence each data is a vector of samples from the
same mixture component. Such a model is known in the literature as a finite
mixture model of repeated measures, which has been applied in psychological
study and topic modeling. This model also belongs to the mixture of product
distributions, with the special structure that the product distributions in
each mixture component are also identical. In this setup, each data
consists of conditionally independent and identically distributed samples
and thus is an exchangeable sequence.
We show that by taking repeated measures (collecting more samples from each
mixture component), a finite mixture model that is not originally
identifiable becomes identifiable. Moreover, the posterior contraction
rates for the parameter estimation are also obtained, demonstrating that
repeated measures are beneficial for estimating the component parameters.
Our results hold for general probability families including all regular
exponential families and can also be applied to hierarchical models. The
key tool to develop the results is by establishing an inverse inequality to
upper bound a suitable distance between mixing measures by the total
variational distance between the corresponding mixture densities.
Based on joint work with Xuanlong Nguyen (University of Michigan).
------------------------------------------------
Il webinar è organizzato dalla "de Castro" Statistics Initiative
www.carloalberto.org/stats
in collaborazione con il Collegio Carlo Alberto e rientra nel Complex Data
Modeling Research Network
midas.mat.uc.cl/network
Cordiali saluti,
Pierpaolo De Blasi
---
University of Torino & Collegio Carlo Alberto
carloalberto.org/pdeblasi
<https://sites.google.com/a/carloalberto.org/pdeblasi/>
Buongiorno,
inoltro l'annuncio del OWPS di domani.
Saluti
Alessandra
---------- Forwarded message ---------
Da: One World Probability <ow.probability(a)gmail.com>
Date: mer 17 feb 2021 alle ore 10:50
Subject: [owps] One World Probability Seminar Thursday February 18, 2021
To: <owps(a)lists.bath.ac.uk>
Tomorrow's speaker in the One World Probability Seminar is
(Note: all times are in UTC. *Due to time changes, you should check what
that translates to in your location*)
------------------------------------------------
(14:00-16:00 UTC) Speaker: Richard Kenyon (Yale)
Title: The multi-tiling model
Abstract: Given a graph G and collection of connected subgraphs T (called
tiles), we consider covering G with copies of tiles in T so that each
vertex of G is covered with a predetermined multiplicity. The multitiling
model is a natural probability measure on such configurations.
In the limit of large multiplicities we compute the asymptotic growth rate
of the number of multitilings: the free energy of the multitiling model. We
will show that the individual tile densities tend to a Gaussian field with
respect to an associated discrete Laplacian. We also find an exact discrete
Coulomb gas limit when we vary the multiplicities.
This is joint work with Andrei Pohoata (Yale).
Please note: Richard Kenyon will be giving both talks in the session.
------------------------------------------------
The zoom link will appear the day before on the OWPS website:
https://www.owprobability.org/one-world-probability-seminar
<https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.owpro…>
It can also be directly accessed through the link below:
https://uniroma1.zoom.us/j/83228198137?pwd=QUxhM1NXdlFTOGxvc09IUGIvenBxUT09
<https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Funiroma1.…>
Meeting-ID: 832 2819 8137
Passcode: 029896
Please feel free to circulate this email.
We hope to see you all tomorrow!
One World Probability Team
--
*************************************************
Prof. Alessandra Faggionato
http://www1.mat.uniroma1.it/~faggionato/
Department of Mathematics
University "La Sapienza"
Piazzale Aldo Moro, 5
00185 - Rome
Office 5, Phone (0039) 06 49913252
*************************************************
On behalf of the Scientific and Organising Committees we are pleased to
inform you that
the Twelth Workshop on
“*Bayesian Inference in Stochastic Processes (BISP12)*”
will be held *online* on *27-28 May 2021* (afternoons, Central European
Summer Time).
Attendance is free of charge but registration is required (and limited
to 150 people).
As in the past, the workshop will provide an opportunity to review,
discuss and
explore directions of development of Bayesian Inference in Stochastic
Processes.
Ten presentations by young scientists have been scheduled this year,
followed by an
in-depth discussion by senior scholars in the field.
The workshop is organised by CNR-IMATI (Milano), www.imati.cnr.it.
Information on programme, website and registration will be available in
early Spring.
Elisa Varini and Fabrizio Ruggeri
Chairs, Organising and Scientific Committees
Dear colleagues,
I would like to invite you to the following online seminar organized by the Probability group of the University of Pisa. The talk will be accessible under the link
Click here to join the meeting<https://teams.microsoft.com/l/meetup-join/19%3A17115d7f6ef44c5e91974362906c…>
Best regards,
Giacomo
Tuesday, Feb. 23, 16:00
Speaker: Renaud Raquépas (McGill University Montréal and Université Grenoble Alpes)
Title: Entropy production in nondegenerate diffusions: the large-time and small-noise limits
Abstract: Entropy production (EP) is a key quantity originating from thermodynamics and statistical physics which quantifies the irreversibility of the time evolution of physical systems. I will start with a general introduction to the different approaches to defining EP. Then, I will focus on the context of nondegenerate diffusions and I will describe the large-deviation properties of EP as time goes to infinity. Finally, I will discuss the behaviour of the corresponding rate function as the intensity of the noise goes to zero.
************************
Giacomo Di Gesù
Dipartimento di Matematica
Università di Pisa
Largo Bruno Pontecorvo 5
56127 - Pisa, Italy
giacomo.digesu(a)unipi.it<mailto:giacomo.digesu@unipi.it>
https://sites.google.com/site/giacomodigesu/