Login

SOSC 2018 Second International PhD School Open Science Cloud

Sep 17 – 21, 2018
Department of Physics and Geology
Europe/Rome timezone
School on Open Science Cloud

School contact - mail

Contribution List

35 out of 35 displayed
Export to PDF
  1. 0. School on Open Science Cloud - Welcome and Introduction
    Daniele Spiga (PG)
    9/17/18, 10:00 AM
  2. 3. Predictive models with Machine and Deep Learning: a scientific view
    Daniele Bonacorsi (BO)
    9/17/18, 10:20 AM
    An overview of (selected) use-cases for {Machine,Deep} Learning in various scientific disciplines is presented. Peculiarities of - and similarities across - disciplines are explored. Possible cross-fertilisations and synergic approaches, as well as the value of training paths, is also discussed.
    Go to contribution page
  3. 2. Predictive Models for Thermal Modelling
    Prof. Andrea Bartolini (Università di Bologna)
    9/17/18, 11:50 AM
    We hear it almost every day but we barely recognize, it’s the noisy fan of our laptop. We feel it with our hands in the summer, it’s our mobile getting too hot. It’s the heat dissipated by electronics devices. Digital processing elements, the heart of our smartphone, laptop, workstation and supercomputers dissipate power for flipping bits of information, this power increases the temperature of...
    Go to contribution page
  4. 4. Python Coding session 1
    Dr Stefano Calderan (Oval Money)
    9/17/18, 2:00 PM
    Material is available here https://github.com/calde12/SOSC2018-PYTHON-HANDSON
    Go to contribution page
  5. 35. ECMWF new data centre in Bologna : new opportunities for using Open Data
    Paolina Cerlini (CIRIAF-CRC)
    9/17/18, 3:35 PM
    In the next future the new data centre of the European Centre for Medium-range Weather Forecasts (ECMWF) based in Reading U.K. will be moved to Bologna, Italy. The building is to be delivered to ECMWF by 2019 and will host the Centre’s new supercomputers, whilst the Centre’s headquarters are to remain in the UK. This centre will host one of the few global dataset of environmental data used...
    Go to contribution page
  6. 7. Introduction to Big Data
    Daniele Cesini (CNAF)
    9/18/18, 9:00 AM
    In this presentation we will introduce the concept and distinctive characteristics of Big Data. Examples taken from both scientific and commercial realms will be provided and will illustrate how BIgData analytics is a key enabler for social business, but also science can benefit from using the same techniques. A quick overview of BigData computing infrastructures, in particular their...
    Go to contribution page
  7. 8. Introduction to Machine Learning and Deep Learning
    Prof. Elisa Ricci (Università di Trento)
    9/18/18, 10:00 AM
    From self-driving cars to voice-activated intelligent assistants and robots reading and mimicking human emotions, machine learning and deep learning are not only revolutionizing several industry sectors but they are slowly and steadily becoming part of our daily lives. This talk will provide an overview of deep learning, namely of modern, multi-layered neural networks trained on big data. In...
    Go to contribution page
  8. 9. Data Science: state of the art
    Dr Valentin Kuznetsov (Cornell University (US))
    9/18/18, 11:30 AM
    The Data Science is an rapidly growing field of Information Technology. In this talk we'll cover its origin, the connection to other IT fields and cover details of Data Science fields from data acquisition, processing to building Machine Learning models. We'll cover the tools, technologies and techniques a Data Scientist use in their daily activities. We'll also discuss algorithms and...
    Go to contribution page
  9. 11. Hands-on: Deploy ML Frameworks 101
    Dr Valentin Kuznestov
    9/18/18, 2:00 PM
    In this Hands-On session we will start from bare node with deploy all necessary tools to perform Machine Learning and Data Science studies. The session will require basic knowledge of Linux operating system and UNIX shell. We'll download, deploy and install Anaconda environment where we'll install various set of Python and R based packages. We'll work with a simple Iris dataset to explore...
    Go to contribution page
  10. 30. Unsupervised Learning
    Prof. Andrea De Simone (sissa)
    9/18/18, 3:35 PM
    I introduce the basic concepts of unsupervised learning, highlighting the differences with supervised learning. The focus will be on three of the main tasks addressed by unsupervised learning: cluster analysis, anomaly detection and dimensional reduction. For each of them, I describe the problem in general terms, the ways to approach it, and some of the most popular algorithms to solve it.
    Go to contribution page
  11. 32. Hands-on: Deploy ML Frameworks 101
    9/18/18, 4:30 PM
    This Hands-On session is dedicated to train your skills in area of Machine Learning (aka ML 101 course). We'll start with an Iris dataset and build our first ML model for it. Then we'll gradually expand our model to include various ML techniques, like ensemble learning, introduce concept of cross-validation and build simple Neural Network model. This session only requires basic Python...
    Go to contribution page
  12. 36. Hands-on: k-means clustering, anomaly detection e PCA
    Prof. Andrea De Simone (sissa)
    9/18/18, 6:00 PM
    Hands-on material : https://github.com/de-simone/SOSC18_handson
    Go to contribution page
  13. 15. Models and Algorithms in Medical Imaging
    Alessandra Retico (PI)
    9/19/18, 9:00 AM
    Medical Imaging techniques rely on different physical principles to obtain signals from the human body. Medical images are more than pictures, they are data, and they can be explored through image analysis techniques to go well beyond the mere visual inspection by radiologists. Image processing and data mining techniques are used to extract useful information from medical images to...
    Go to contribution page
  14. 16. The Ophidia project: towards a High Performance Data Analytics and Machine Learning framework for climate change
    Dr Sandro Fiore (Centro Euro-Mediterraneo sui Cambiamenti Climatici)
    9/19/18, 10:00 AM
  15. 14. Models and Algorithms in HEP
    Marco Zanetti (PD), Maurizio Pierini (CERN)
    9/19/18, 11:30 AM
  16. 17. Hands-on: Data analytics with Ophidia/ECAS
    Dr Sandro Fiore (CMCC)
    9/19/18, 2:00 PM
  17. 18. Coffee Seminar: Machine Learning for Particle Physics
    Dr Maurizio Pierini (CERN)
    9/19/18, 3:35 PM
  18. 33. Hands-on: The HEP Use Case
    Dr Maurizio Pierini (CERN)
    9/19/18, 4:30 PM
    Tutorial material URL: https://www.dropbox.com/s/or43zo8imt52l0x/SOSC18_Tutorial.tar.gz?dl=0
    Go to contribution page
  19. 20. Deep Learning and Machine Learning in hybrid clouds
    Davide Salomoni (CNAF)
    9/20/18, 9:00 AM
  20. 21. Big Data at CERN
    Dr Vaggelis motesnitsalis (CERN)
    9/20/18, 10:00 AM
    The LHC experiments continue to produce a wealth of valu- able High Energy Physics data, which oer numerous possibilities for new discoveries. The IT Department at CERN provides Hadoop and Spark services and works closely with the scientic communities in their quest to analyze and understand these vast amounts of physics and in- frastructure data. The number of CERN teams using these...
    Go to contribution page
  21. 22. T-Systems, Open Telekom Cloud
    Dr Jurry de la Mar (T-System)
    9/20/18, 11:30 AM
  22. 23. Hands on: Automated deployment of BigData Cluster in the Cloud
    Daniele Spiga (PG), Diego Ciangottini (PG), Mirco Tracolli (PG)
    9/20/18, 2:00 PM
    This session will include the usage of Dynamic On-Demand Analysis Service (DODAS) and as such will includes: Containers Orchestration (Mesos) Software Applications & Dependency description (TOSCA); Cloud PaaS Orchestration (INDIGO-PaaS)
    Go to contribution page
  23. 24. Coffee Seminar: Becoming a Data Scientist
    Dr Valentin Kuznetsov (Cornell University (US))
    9/20/18, 3:35 PM
    In this talk we'll cover materials and knowledge base of how to become a world class Data Scientists. We'll use real kaggle competition dataset and build for it a set of ML models. During this session we'll introduce advanced concepts of ML training, like feature embedding, data transformation and ML model fine-tuning. We'll discuss how to work large datasets, techniques to avoid...
    Go to contribution page
  24. 31. Hands on: Automated deployment of BigData Cluster in the Cloud
    Daniele Spiga (PG), Diego Ciangottini (PG), Mirco Tracolli (PG)
    9/20/18, 4:30 PM
  25. 27. Research & Business - OPENLab - IT Technology Transfer at CERN
    Dr Alberto Di Meglio (CERN)
    9/21/18, 9:00 AM
    CERN openlab as a model for R&D and Technology Transfer at CERN Abstract: CERN openlab was created in 2001 as a way of establishing a new collaboration channel between CERN and ICT industry. Over the past 17 years, it has become a reference for joint R&D and technology transfer at CERN in support of the future computing and data requirements of the increasingly challenging LHC research...
    Go to contribution page
  26. 28. Applications to Finance and Insurance
    Prof. Franco Moriconi (University of Perugia and ALEF Advanced Laboratory Economics and Finance srl)
    9/21/18, 10:00 AM
    Some applications of big data analytics techniques to the insurance business Some examples are provided to illustrate how the insurance industry is currently facing the new paradigm of big data analytics. Methodologies for analyzing telematic car driving data are illustrated. In particular, it is shown how pattern recognition and machine learning techniques can be used to derive predictive...
    Go to contribution page
  27. 26. Research & Business - NVIDIA
    Dr Piero Altoe' (NVIDIA)
    9/21/18, 11:30 AM
    The convergence of supercomputing and AI in a Post-Moore’s Law World The world is facing many very large challenges in many fields, including energy, climate, and biology. High-performance computing can help provide the answers through large-scale simulation, but ground-breaking simulations are incredibly computationally expensive. Unfortunately, Moore law is slowly stopping and there...
    Go to contribution page
  28. 29. Follow-up projects & collaboration opportunities
    Davide Salomoni (CNAF)
    9/21/18, 12:30 PM
  29. 5. Advanced Python Coding (Speaker TBD)
  30. 34. Coffe Seminar
    Paolina Cerlini (CIRIAF-CRC)
  31. 12. Computational and Statistical Learning - (Speaker TBD)
  32. 13. Hands-on 2
  33. 19. Hands-on 2
  34. 25. Hands-on 2
  35. 6. Python Coding session 2