[Forum SIS] 1st Seminar "D2 Seminar Series" - Florence Center for Data Science

[datascience a unifi.it]

Dear all,

The Florence Center for Data Science is happy to invite you to the first 
of 4 Seminars of the "D2 Seminar Series" launched by the FDS (see the 
flyer attached). The Seminar will be held online Friday 21st of May 
2021, from 2-3.30 pm.

Here the link to participate :
https://unifirenze.webex.com/unifirenze/j.php?MTID=m454db9a545de04f3ccdbd12c0a596d16
(Passcode: YMq98vJMEG9)

Speaker: Professor Fabrizia Mealli
Department of Statistics, Computer Science, Applications "G. Parenti", 
University of Florence
Title: Assessing causality under interference

Abstract: Causal inference from non-experimental data is challenging; it 
is even more challenging when units are connected through a network. 
Interference issues may arise, in that potential outcomes of a unit 
depend on its treatment as well as on the treatments of other units, 
such as their neighbours in the network. In addition, the typical 
unconfoundedness assumption must be extended—say, to include the 
treatment of neighbours, and individual and neighbourhood covariates—to 
guarantee identification and valid inference. These issues will be 
discussed, new estimands introduced to define treatment and interference 
effects and the bias of a naive estimator that wrongly assumes away 
interference will be shown. A covariate-adjustment method leading to 
valid estimates of treatment and interference effects in observational 
studies on networks will be introduced and applied to a problem of 
assessing the effect of air quality regulations (installation of 
scrubbers on power plants) on health in the USA.

Speaker: Professor Andrew Bagdanov
Department of Information Engineering, University of Florence
Title: Lifelong Learning at the end of the (new) Early Years

Abstract: Lifelong learning, also often referred to as continual or 
incremental learning, refers to the training of artificially intelligent 
systems able to continuously learn to address new tasks from new data 
while preserving knowledge learned from previously learned ones. 
Lifelong learning is currently enjoying a sort of renaissance due to 
renewed interest from the Deep Learning community. In this seminar, I 
will introduce the overall framework of continual learning, discuss the 
fundamental role played by the stability-plasticity dilemma in 
understanding catastrophic forgetting in lifelong learning systems, and 
present a broad panorama of recent results in class-incremental 
learning. I will conclude the discussion with a look at current trends, 
open problems, and low-hanging opportunities in this area.

Kind Regards,
Florence Center for Data Science
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