Dear all,
you're invited to the next seminar in Probability and Finance that will
take place next Monday, at 4 pm, in hybrid mode.
All the details follow:
* Speaker: Prof. Umut Cetin (LSE)
* Date and time: Monday 6th June 2022, 4pm
* Room (Torre Archimede): 1BC45
* Zoom link: please find it here
https://www.math.unipd.it/~bianchi/seminari/
* Title: POWER LAWS IN MARKET MICROSTRUCTURE
* Abstract: We develop an equilibrium model for market impact of trades
when investors with private signals execute via a trading desk. Fat tails
in the signal distribution lead to a power law for price impact, while the
impact is logarithmic for lighter tails. Moreover, the tail distribution of
the equilibrium trade volume obeys a power law. The spread decreases with
the degree of noise trading and increases with the number of insiders. In
case of a monopolistic insider, the last slice traded against the limit
order book is priced at the fundamental value of the asset reminiscent of
Kyle (1985). However, competition among insiders leads to aggressive
trading, hence vanishing profit in the limit. The model also predicts that
the order book flattens as the amount of noise trading increases converging
to a model with proportional transactions costs with non-vanishing spread.
See you soon,
Giorgia
--
Giorgia Callegaro
Associate Professor
Department of Mathematics - University of Padova
Via Trieste 63 , I-35121 Padova - ITALY
Tel: +39-0498271481 Fax: +39-0498271499
E-Mail: gcallega(a)math.unipd.it
<https://webmail.math.unipd.it/horde3/imp/message.php?mailbox=Sent&index=598#>
Personal web-page: https://sites.google.com/site/giogiocallegaro/Home
Si avvisa che
in data 07-06-2022, alle ore 15:30 precise
presso il Politecnico di Milano, Dipartimento di Matematica, Aula Saleri sesto piano (Edificio 14 - La Nave),
nell’ambito delle attività del MOX, si svolgerà il seguente seminario:
Stefano Castruccio, University of Notre Dame
Titolo: Physics-Informed, Data-Driven and Hybrid Approaches to Space-Time Systems
Abstract: In this talk I will discuss two different approaches to characterize space-time systems. This first one is model-driven and loosely inspired by physics, assumes that the system is locally diffusive through a stochastic partial differential equation, and can be efficiently approximated with a Gaussian Markov random field. This approximation will be used to produce a stochastic generator of simulated multi-decadal global temperature, thereby offering a fast alternative to the generation of large climate model ensembles.
The second approach is instead data-driven, and relies on (deep) neural networks in time. Instead of traditional machine learning methods aimed at inferring an extremely large parameter space, we instead rely on an alternative fast, sparse and computationally efficient echo state network dynamics on an appropriately dimensionally reduced spatial field. The additional computational time is then used to produce an ensemble and probabilistically calibrate the forecast. The approach will be used to produce air pollution forecasts from a citizen science network in San Francisco and forecasting wind energy in Saudi Arabia.
Towards the end of the presentation, I will discuss how these two broad frameworks could be used in synergy to allow for improved predictability and understanding of space-time systems whose physical understanding is currently limited and/or largely influenced by parametrizations.
Link: https://mox.polimi.it/mox-seminars/?id_evento=2169
Il link per seguire il seminario online sarà reso disponibile su
Link zoom: https://polimi-it.zoom.us/j/91015435557
L’evento è patrocinato da GRASPA
https://graspa.org
Tutti gli interessati sono cordialmente invitati a partecipare,
Laura Sangalli
——
Laura Maria Sangalli
MOX - Dipartimento di Matematica
Politecnico di Milano
Piazza Leonardo da Vinci 32
20133 Milano - Italy
tel: +39 02 2399 4554
fax: +39 02 2399 4568
email: laura.sangalli(a)polimi.it<mailto:laura.sangalli@polimi.it>
url: http://mox.polimi.it/~sangalli
Cari tutti,
in data odierna è stato pubblicato il Bando n. RTDA/03/2022 PROT. N.
805/2022 della
procedura selettiva, per titoli e colloquio, per il reclutamento di* n. 1
ricercatore con rapporto di lavoro a tempo determinato di tipologia “A”*, per
l’esecuzione del programma di ricerca relativo al seguente progetto:
“Teoria dei processi aleatori” - Settore concorsuale 01/A3, *Settore
scientifico-disciplinare **MAT/06*, presso il Dipartimento di Scienze
Statistiche, Università di Roma "La Sapienza".
Il bando è visionabile sul sito della trasparenza Sapienza al seguente link:
https://web.uniroma1.it/trasparenza/bando/191351_rtda/03/2022
Si prega di dare la massima diffusione del presente avviso presso tutti gli
interessati.
Cordiali saluti,
Alessandro De Gregorio
UNIVERSITA' DI SALERNO
Dipartimento di Matematica
AVVISO DI SEMINARI
Mercoledì 1 giugno 2022, nella sala del consiglio del
Dipartimento di Matematica, edificio F2, livello 1, si terranno i seguenti
seminari in presenza e online (su Teams):
1) ore 15:00-15:45 - Prof. Enrico Scalas (University of Sussex, Brighton,
UK)
Limit theorems for prices of options written on semi-Markov processes
link:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZmRiNmFjZmUtNmFlMS00…
We consider plain vanilla European options written on an underlying asset
that follows a continuous time semi-Markov multiplicative process. We
derive a formula and a renewal type equation for the martingale option
price. In the case in which intertrade times follow the Mittag-Leffler
distribution, under appropriate scaling, we prove that these option prices
converge to the price of an option written on geometric Brownian motion
time-changed with the inverse stable subordinator. For geometric Brownian
motion time changed with an inverse subordinator, in the more general case
when the subordinator's Laplace exponent is a special Bernstein function,
we derive a time-fractional generalization of the equation of Black and
Scholes.
This is joint work with Bruno Toaldo
2) ore 16:00-16:45 - Dott. Giacomo Ascione (Scuola Superiore Meridionale,
Napoli)
Bulk behaviour of ground states for relativistic Schrödinger operators with
spherical potential well
link:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_OTMzYTBmYTAtZDFiYS00…
In this talk, we show a probabilistic representation of the ground state of
a massive or massless Schrödinger operator with a spherical potential well.
Such a representation will lead to a two-sided approximation with different
behaviours depending on the fact that we are inside or outside the well.
Both of them rely on some functionals of the first exit time of a
subordinated Brownian motion from a suitable open set (the well or its
complement). We also develop a moving planes-type argument to prove the
radial monotonicity of the ground state, which is one of the main tools of
the two-sided bounds. This is joint work with József Lőrinczi.
Gli interessati sono cordialmente invitati a partecipare,
Cordiali saluti,
Barbara Martinucci
Vi segnalo che la sede milanese dell'IMATI CNR ha organizzato un evento
online il 5 luglio in cui 12 ricercatori CNR, di diversi istituti e
diversa formazione, presenteranno loro lavori. L'iniziativa, in
collaborazione con la Societa' Italiana di Statistica e nell'ambito delle
iniziative verso il Festival della Statistica e della Demografia (Treviso,
16-18 Settembre), intende mostrare alla comunita' statistica nazionale cio'
che si fa al CNR (e non e' praticamente conosciuto da questa) e anche
favorire interazioni fra ricercatori CNR che spesso operano senza sapere
che ci sono altri colleghi che utilizzano e sviluppano metodi e modelli
stocastici.
Il sito web e' https://sis.mi.imati.cnr.it/index.html
E' possibile registrarsi entro il 26 giugno. Nei giorni successivi
verranno inviate ai registrati le indicazioni sulla piattaforma online
che verra' utilizzata.
Cordiali saluti
Fabrizio Ruggeri
--
Fabrizio Ruggeri fabrizio AT mi.imati.cnr.it
CNR IMATI tel +39 0223699532
Via Alfonso Corti 12 fax +39 0223699538
I-20133 Milano (Italy) www.mi.imati.cnr.it/fabrizio
We are looking for teaching assistants for various mathematical courses in
the fall semester. Some of these courses are taught in English and some in
Italian. In particular, we need TAs for a master course in Probability
(taught in English).
*******************************************************
Marco Scarsini
Dipartimento di Economia e Finanza
Luiss University
Viale Romania 32
00197 Roma, ITALY
URL: http://docenti.luiss.it/scarsini/
Dear All,
next Tuesday Nicolas Forien (Sapienza Università di Roma) will give a
seminar talk on particle systems and interacting random walks. Please find
the title and the abstract below.
Best regards,
Lorenzo Taggi
Tuesday 31st of May, 16.15, Dipartimento di Matematica Università Sapienza,
Aula Consiglio
*Title:* On the phase transition of activated random walks
*Abstract:* The Activated Random Walk model consists of particles which
perform independent random walks on a graph and fall asleep with a certain
rate. Sleeping particles stop moving and are awaken when another particle
arrives on the same site. The model on Z^d presents a phase transition:
depending on the density of particles (initially all active) and on the
sleep rate, either almost surely each particle eventually falls asleep
forever (fixating phase), or almost surely no particle falls asleep forever
(active phase). In this talk, I will present a joint work with Alexandre
Gaudillière (arxiv.org/abs/2203.02476) showing the existence of an active
phase on Z^2: for every positive initial density of particles, for a
sufficiently low sleep rate, almost surely no particle falls asleep forever.
Dear all,
We are delighted to announce a tentative programme of the 8th International Conference on Mathematical Neuroscience (ICMNS 2022) which will take place in a virtual format, via Zoom, on July 6-8, 2022, between 14:00 and 18:00 (GMT+2). Attendance is free but requires registration via our website
https://www.danieleavitabile.com/icmns2022digital
We would like to remind you that the call for micro-talk submissions is open until the 31st of May. Micro-talk sessions are one-hour sessions consisting of a sequence of very short introductions (2 minutes, 1 slide) by the presenters to their research, followed by an opportunity to chat with individual presenters about their research in breakout rooms.
You can submit micro-talks using this link
https://tinyurl.com/3e3fu8wv
It is our pleasure to confirm that this year's plenary speakers will be
* Dani S. Bassett (University of Pennsylvania, USA)
* Sue Ann Campbell (University of Waterloo, Canada)
* Thomas Serre (Brown University, USA)
In addition, there will be 6 minisymposium sessions:
MS1: Learning (and compressing) stochastic sequences of events, organised by Antonio Galves (Universidade de São Paulo, Brasil).
Speakers: Aline Duarte, Noslen Hernández, Marcela Swarc, Claudia Vargas.
MS2: Patterns and Rhythms in Balanced Neural Networks, organised by James MacLaurin (New Jersey Institute of Technology, USA).
Speakers: Mark Goldman Olivia Gozel Robert Rosenbaum, Moshe Silverstein.
MS3: Nonlinear PDEs in neuroscience, organised by Pierre Jules Abel Roux (University of Oxford, UK) and Susanne Solem (Norwegian University of Life Sciences, Norway).
Speakers: Alain Blaustein, Xu’an Dou, Delphine Salort.
MS4: Metastable dynamics in neural circuits, organised by Tilo Schwalger (Technische Universität Berlin, Germany).
Speakers: Daniel Levenstein, Maurizio Matti, Luca Mazzucato, Bastian Pietras.
MS5: Dynamical systems for neurological disorders, Organised by Christoffer Alexandersen (University of Oxford, UK) and Louisiane Lemaire (Humboldt-Universität zu Berlin, Germany).
Speakers: Georgia Brennan, Damien Depannemaecker, Efstathios Pavlidis, Heike Stein,
MS6 Stochastic models for neuronal activity, organised by Laura Sacerdote (University of Turin, Italy).
Speakers: Giuseppe D'Onofrio, Ryota Kobayashi, Tomar Rimjhim, Ohla Shchur.
We look forward to seeing you at ICMNS2022.
The Organising Committee
Daniele Avitabile
Áine Byrne
Massimiliano Tamborrino
Etienne Tanré
------
Dr. Massimiliano Tamborrino
Assistant Professor
Department of Statistics
University of Warwick
https://warwick.ac.uk/tamborrino
Dear all,
I forward you an invitation for an incoming talk at the One World ABC seminar series on Wednesday May 25, 11.30am UK time. Title, abstract and link to join are reported below.
Best,
Massimiliano
------
Dr. Massimiliano Tamborrino
Assistant Professor
Department of Statistics
University of Warwick
https://warwick.ac.uk/tamborrino<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwarwick.a…>
---------------------------------------------------------------------
Dear all,
our next One WorldABC Seminar<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwarwick.a…> on May 25 is quickly approaching!
Our next speaker will be Harita Dellaporta<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwarwick.a…>, who will talk about "Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap", on Wednesday May 25, at 11.30am UK time, with an abstract reported below. Please note the different time and date!
The Zoom link to join the talk is
https://univ-grenoble-alpes-fr.zoom.us/j/99914483254?pwd=UzhjYWJFSndHdEN2Rk…<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Funiv-gren…>
Password: 601768
We look forward to seeing you on Wednesday!
Best wishes,
Massimiliano on the behalf of the One World ABC Seminar Organisers.
When: Wednesday May 25, 11.30am UK time
Speaker: Harita Dellaporta<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwarwick.a…> (University of Warwick)
Title: Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Abstract: Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible. They are often used to describe complex real-world phenomena, and as such can often be misspecified in practice. In this talk, I will present a novel algorithm based on the posterior bootstrap and maximum mean discrepancy estimators. This leads to a highly-parallelisable Bayesian inference algorithm with strong robustness properties. This is demonstrated through an in-depth theoretical study which includes generalisation bounds and proofs of frequentist consistency and robustness of our posterior. The approach is then assessed on a range of examples including a g-and-k distribution and a toggle-switch model.
------
Dr. Massimiliano Tamborrino
Assistant Professor
Department of Statistics
University of Warwick
https://warwick.ac.uk/tamborrino<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwarwick.a…>
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Dear Colleagues,
we would like to invite you to the following seminar by Enrico Malatesta
(Bocconi) to be held Wednesday, May 25th, at Dipartimento di Matematica in
Pisa and online via Google Meets.
The organizers,
A. Agazzi and F. Grotto
--------------------------------------------
Location: Sala Seminari, Dipartimento di Matematica, Pisa
Google Meet Link: https://meet.google.com/gji-phwo-vbg
Time: May 25th, 2022, 14:00-15:00 CET
Speaker: Enrico Malatesta
Title: Phase transitions in the landscape of solutions of overparametrized
neural networks.
Abstract: Current deep neural networks are nonlinear devices composed of a
number of parameters that far exceed the number of data points.
Understanding how these systems can fit the data almost perfectly through
variants of gradient descent algorithms and achieve exceptional levels of
prediction accuracy without overfitting are key conceptual challenges. In
this talk I will show how common techniques used in machine learning (e.g.
the choice of the activation function or the loss) deeply affect the loss
landscape, tending to mild its roughness. Then we shed light on the role of
overparameterization in non-convex neural networks. By analytically
studying a non-convex model of random features, we identify a novel
(non-equilibrium) phase transition, that we call “Local Entropy”
transition, controlled by the degree of overparameterization. In non-convex
models this transition is strictly different to the SAT/UNSAT threshold and
it coincides with the appearance of highly entropic minima of the error
loss function. Those minima, in turn, are found to be highly attractive to
the learning algorithms currently used in deep learning.