1st Edition of the School in
Machine Learning of
Dynamic Processes and Time Series Analysis
26-27 NOVEMBER , 2020
Scuola Normale Superiore
Pisa
In Brief
The aim of the School is to present recent developments in Machine Learning focusing on data-driven approaches to statistical learning and dynamical systems. Applications will also be discussed, such as the forecasting of financial time series.
The School provides 4 lectures by:
-
Christa Cuchiero, University of Vienna.
-
Lyudmila Grigoryeva, University of Konstanz.
-
Juan-Pablo Ortega, University of St. Gallen and CNRS.
-
Josef Teichmann, ETH Zurich.
In addition, it provides a limited number of contributed talks.
IMPORTANT INFORMATION CONCERNING THE Covid-19
Due to the current worldwide situation due to the Covid-19 pandemic, the School can be attended either in presence or in on-line mode. More precisely:
-
Personal participation will only take place if the highest possible health safety can be guaranteed. Therefore, we might have to consider a constraint in the number of admitted participants in presence, because of the limited capacity of lecture halls.
-
However, for the time being we shall wait and monitor events and measures to get a clear picture of how the situation might look at the end of November: a final decision for participation in presence will be communicated at the mid of October.
-
In case a participant will cannot take part to the School because of unexpected events related to Covid-19, such as sudden transport limitations, the on-line mode will be always available.
We gratefully acknowledge the contribution of various sponsors for financial support. In particular, the School is supported by:
-
The scheme 'INFRAIA-01-2018-2019: Research and Innovation action', Grant Agreement n. 871042 'SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics'. www.sobigdata.eu.
-
The "Department of Excellence" of the Scuola Normale Superiore.
-
Unicredit Group, under the project ‘‘Dynamics and Information Research Institute-QuantumInformation, Quantum Technology.
Flyer
Flyer of the conference: DOWNLOAD
Deadlines
-
Paper submission and Registration closes on September 30th, 2020.
In order to register for the School or if you want to give a contributed talk, please follows the directions that you find when pressing the button "Apply" here below.
Organizer Committee
Fabrizio Lillo (University of Bologna and Scuola Normale Superiore);
Giulia Livieri (Scuola Normale Superiore);
Stefano Marmi (Scuola Normale Superiore);
Piero Mazzarisi (Scuola Normale Superiore).
To contact the Organizer Committee, please write an email to : mldyn2020@sns.it
The Speakers
01
DESCRIPTION: TBA
Christa Cuchiero
University of Vienna
Christa Cuchiero is Full Professor for Quantitative Risk Management at the Institute for Statistics and Operations Research at the University of Vienna since March 2020. Her interests include Machine Learning in Finance; Insurance and Economics; Mathematical Finance; Statistics of Stochastic Processes and with High-Frequency data.
Lyudmila Grigoryeva
University of Konstanz
Lyudmilla Grigoryeva is an Assistant Professor in Computational Statistics and Econometrics at the Department of Mathematics and Statistics Graduate School of Decision Science at the University of Konstanz since October 2015. Her interests include Machine Learning in Econometrics; Stochastic Dynamical Models in Mathematical Finance and Econometrics; Brain-inspired Information processing.
02
DESCRIPTION: TBA
03
DESCRIPTION: TBA
Juan-Pablo Ortega
University of St. Gallen
Juan-Pablo Ortega is a Professor PhD and Big Data Project Leader at the Faculty of Mathematics and Statistics at the University of St. Gallen since 2016 and a Senior CNRS researcher (on leave). His interests include Machine Learning and Statistical Modeling to time series; Financial Econometrics; Mathematical Finance.
Josef Teichmann
ETH Zurich
Josef Teichmann is Full Professor in Mathematics at ETH Zurich since 2009. His interests include Machine Learning, especially the mathematical foundation of Machine Learning; Stochastic Analysis; Mathematical Finance; Portfolio Management, as well as Frontier Topics such as Big Data and Fintech.
04
DESCRIPTION: TBA
(Tentative)
Agenda
NOVEMBER, 26, 2020
8:00
Registration
8:45
Opening Remarks
9:00
First Lecture (2 hours and half)
11:30
Coffee Break
12:00
Contributed Talks
13:20
Lunch & Networking
15:00
Second Lecture (2 hours and half)
19:30
Welcome Aperitivo
NOVEMBER, 27, 2020
9:00
Third Lecture (2 hours and half)
11:30
Coffee Break
12:00
Contributed Talks
13:00
Lunch & Networking
14:30
Fourth Lecture (2 hours and half)
17:00
Concluding Remarks
Venue & Accommodation
The school will take place in the main building of the Scuola Normale Superiore, located in Piazza dei Cavalieri 7, 56126, Pisa, Italy. The lecture hall are the "Sala Azzurra" (1st floor) and the "Sala Degli Stemmi" (2nd floor) and "Aula Dini" (Palazzo del Castelletto).
GETTING TO PISA
-
The Airport of Pisa (Galileo Galilei International Airport) is near the city center, and it is served by major airlines with flights from Milan, Rome, London, Paris and many other Italian, European and international cities.
-
The Florence Airport (Firenze-Peretola Airport) is the next closest airport to the city of Pisa; it also has numerous connection to Italian, European and international cities. It is linked to the central train station (Santa Maria Novella) of Florence by shuttle buses and by train to Pisa. These trains leave approximately every twenty minutes. It takes about an hour to travel from Florence to Pisa.
-
Other possible airports that may be convenient are those of Rome (ADR).
GETTING TO THE SCUOLA NORMALE SUPERIORE
-
By bus. Scuola Normale can be reached from the Pisa Airport using city buses (Compagnia Toscana Trasporti): take the LAM rossa (red) bus, which directly connects the airport with the city centre.
The “via Fermi” stop is the nearest to Scuola Normale. -
By taxi. You can also take a taxi (CoTaPi); the trip from the airport to the Scuola Normale costs about €10. Tel.: (+39)050541600.
-
By train. The main station is “Pisa Centrale”, located in the city centre about 1 km from the Scuola Normale. Pisa is connected with all main European cities by train through Florence, Turin, Genoa or Rome (Trenitalia).
HOTELS: Hotels.
RESTAURANTS: Restaurants.