1. C.Roda Universita` e INFN Pisa
BigDAPHNE … take 2 
November288th 2016 ITN.EJD
C.Roda Universita` e INFN Pisa

2. C.Roda Universita` e INFN Pisa
List of things to do
Work on draft according to next slide
revise your ESR projects
revise table of your institution
letter for joint degree
letters from partners
New draft ready by: December 5th
November288th 2016 ITN.EJD
C.Roda Universita` e INFN Pisa

3. C.Roda Universita` e INFN Pisa
Work sharing
Section 1 and WP description Chiara, Stefania Section 2 Chara, Rosy Section 3 Nikos, Tim
Letters for Double Degree – Status
UNIPI, UCL – Done
UNILE – Phd schoold meeting done, expected next week
Saclay – letter requested need to wait
AUTH – should arrive this week
November288th 2016 ITN.EJD
C.Roda Universita` e INFN Pisa

4. Asked letter and project
Who
Title
Description
Letter
1. Net7
Textual and geographical Data Analytics on information produced by business projects x
Vodafone 1
Contact - talk on Friday 
Vodafone 2
2. CMCC 1
Meteo
3. CMCC 2
UNICREDIT
4. Thales 1
confirmed
5. Thales 2
6. Thales 3
CERN 1
CERN 2
AUTH 1
OTE/KOSMOTE`
7. NCC 1
8. NCC 2
TFL 1
TFL 2
Asked letter and project
November288th 2016 ITN.EJD
C.Roda Universita` e INFN Pisa

5. Partners – not giving secondments
Name
Status
Done
SAS / Chiara
in contact
INFN / Chiara
INAF / Chiara
Movie company / Chara
draft ready
HITS / Chara
Request from Nik to add University of Parma
November288th 2016 ITN.EJD
C.Roda Universita` e INFN Pisa

6. Training focus: BigData technology and tools
Event 1: Establishing the foundation (M. Morandin, F.Giacomini)
based on Bertinoro school – 2016 edition
program mostly on Good programming practices in C++ / Architectures / Parallel Computing / GPU programming
will add more seminars on BigData analytic aspects
Event 2: Introduction to BigData techniques for science and society (?)
SAS (C.Gianfiori): introduction to SAS programming and statistics, DataMiner tool from SAS (7days)
Ophidia (D.Salomoni): a tool for BigData analytics
Introduction to BigData aspects in science
Event 3: Summer schools on Big Data techniques in Particle Physics and Cosmology (A.Di Meglio, …)
tools for Data Analytics in particle physics and cosmology
can we try to have a list of possible course subjects ?
Applicazioni FPGA per data analytics – specifiche tecniche di programmazione
Evento 3 UCL collaborare - Konstatinidis
November288th 2016 ITN.EJD
C.Roda Universita` e INFN Pisa

7. C.Roda Universita` e INFN Pisa
Work packages
Suggestion from Nikos/Tim/Chiara:
Merge WP1/WP2  Research in fundamental physics - CEA
Merge WP3/WP4  BigData tools in fundamental physics and private sectors – UCL
Training – UNILE
Outreach and dissemination - AUTH
Management and coordination - UNIPI
November288th 2016 ITN.EJD
C.Roda Universita` e INFN Pisa

8. C.Roda Universita` e INFN Pisa
Suggestions
November288th 2016 ITN.EJD
C.Roda Universita` e INFN Pisa

9. C.Roda Universita` e INFN Pisa
NCP – D’Agostino
General positive feedback – he thinks that in the evaluation report there are not strong objections
Suggestions to improve the project:
add a table with the list of institutions and list of needed expertise and show who has which expertise
describe also in document 1 the thesis that we have supervised and mention if we have students that have won prizes or that have found very good jobs
documentary check if we have described in the correct way what we wanted to do, he thinks the idea is good
Improve the description of the impact on the career: have two sections short term impact on long term impact: . short term: profit of the Marie Curie fellow to learn … long term: they will be able to submit ERC …
He is available to read the new version of the document to give feedback  beginning of Monday 5th December the document should be ready for review.
November288th 2016 ITN.EJD
C.Roda Universita` e INFN Pisa

10. G.Chiarelli – My colleague EU evaluator
- need to better evidentiate the connections and role of the different institutions and it is better to have more than a single role for the various institutions - Dissemination should be done: . be more precise on the actions for example if we mention we want to publish mention to which Journals . companies: we could propose to build a boot of the project to send around to company meetings to disseminate results of the project . "success story board" is not clear what it means, mention that for the movie we have a company that will do this and that we have a precise plan and mention successful experiences in similar actions . it seems that one very fancy thing to propose in the dissemination are actions related to the citizen science (from wikipedia since I did not know what it meant): Citizen science (CS) (also known as crowd science, crowd-sourced science, civic science, volunteer monitoring or networked science) is scientific research conducted, in whole or in part, by amateur or nonprofessional scientists. we could ask our CMCC colleagues to make a weather forecast contests for public :-) ... any other ideas ? - in Pisa we could have "sabato in Virgo" and enroll the students (from any experiment) as guides
November288th 2016 ITN.EJD
C.Roda Universita` e INFN Pisa

11. Tom Kitching – school content 1/2
In terms of Event 3 for Big Data aspects in PP and Cosmology, I think a good list of broad things to include from the cosmology side would be
* Some basic stats and training in particular codes
* Practical aspects in particular using public machine learning data sets from here , and also from the public supernovae and weak lensing cosmology data sets
A list of topics could be:
* Basic Stats:
- What is probability?
- The Laws of Probability and Bayes’ Theorem
- Priors
- Parameter inference
- Marginalization
- Confidence intervals, credibility intervals
* Parameter Estimation:
- Bayesian Computation: Parameter Estimation and Sampling
- Grid-based methods
- Markov Chain Monte Carlo
- Metropolis-Hastings algorithm
- Convergence tests – Rubin-Gelman
- Hands on: MCMC code from scratch. Cosmology from the Supernova Hubble Diagram.
November288th 2016 ITN.EJD
C.Roda Universita` e INFN Pisa

12. Tom Kitching – school content 2/2
* Machine learning
- Unsupervised and supervised ML methods
- Random forests
- Neural networks
- Feature extraction
- Hands on: Applying TensorFlow to image recognition. Galaxy shapes from Galaxy Zoo.
* Cosmology codes
- Public and Widely used cosmology codes
- CosmoMC tutorial
- Cosmosis tutorial
- MontePython tutorial
November288th 2016 ITN.EJD
C.Roda Universita` e INFN Pisa

13. Similar project – Stefania, Nik
Asterics-Obelix project:
CTA, SKA, KM3NeT, EUCLID, LSST, EGO-Virgo, E-ELT
November288th 2016 ITN.EJD
C.Roda Universita` e INFN Pisa