Data Science Summer School 2019

02-06 September, Pisa, Italy

About The School

About

The Data Science Summer School offers a broad multi-disciplinary perspective on the different pillars of data science, including data mining and big data analytics, machine learning and AI, network science and complex systems, digital ethics, computational social science and applied data science, featuring lectures by high-level international scholars.

Where

Centro Congressi Le Benedettine
Piazza S. Paolo a Ripa D'Arno 16
Pisa, Italy

When

Monday to Friday
02-06 September 2019

Aim of the School

Why Data Science?

Data Science is emerging as a disruptive consequence of the digital revolution. It is based on the combination of big data availability, sophisticated data analysis techniques, and scalable computing infrastructures. Data Science is rapidly changing the way we do business, socialize, conduct research, and govern society. It is also changing the way scientific research is performed. Model-driven approaches are supplemented with data-driven approaches. A new paradigm emerged, where theories and models and the bottom up discovery of knowledge from data mutually support each other.
Given the interdisciplinary nature of Data Science this summer school offers lectures by high-level scholars from different domains, giving to the students the skills to exploit data and models for advancing knowledge in different disciplines, or across diverse disciplines (e.g. biology, economics, medicine, etc).
The main topics of the summer school are related to big data analytics, i.e., extraction of knowledge from big data, machine learning, i.e., providing an overview of the main techniques used to automatically learn and improve from experience, and complex systems, i.e., methods and technologies particularly related to network science. Moreover, lectures will highlight the ethical implications that data science could lead and the countermeasures that each data scientist can apply to perform analysis with respect to the individuals involved in the data.
The Data Science Summer School 2019 is jointly organized by the Data Science Ph.D. in Pisa and the Data Science Ph.D. in Rome provided by the following institutions:

Speakers

Here are our speakers

Barabasi

Albert-László Barabási (TBC)

Professor at Northeastern University

Bontcheva

Kalina Bontcheva

Professor at University of Sheffield

Caldarelli

Guido Caldarelli

Full Professor at IMT Lucca

Cristianini

Nello Cristianini

Professor at University of Bristol

Dumas

Marlon Dumas

Professor at University of Tartu

Giannotti

Fosca Giannotti

Research Director at Consiglio Nazionale delle Ricerche

Gionis

Aristides Gionis

Professor at Aalto University

Kertesz

János Kertész

Professor at Central European University

Leonardi

Stefano Leonardi

Full Professor at Sapienza University of Rome

Pedreschi

Dino Pedreschi

Full Professor at University of Pisa

Speaker 2

John Shawe-Taylor

Professor at University College London

van den Hoven

Jeroen van den Hoven

Full Professor at Delft University of Technology

School Schedule

Here is the program af the summer school.

Registration

Guido Caldarelli

Network Science Guido Caldarelli

Coffee break

Guido Caldarelli

Network Science Guido Caldarelli

Lunch

János Kertész

Network Science János Kertész

Coffee break

Cole Emmerich

Network Science János Kertész

Aristides Gionis

Social Debates and Online Polarization Aristides Gionis

Coffee break

Aristides Gionis

Social Debates and Online Polarization Aristides Gionis

Lunch

Kalina Bontcheva

Misinformation and Information DisorderKalina Bontcheva

Coffee break

Kalina Bontcheva

Misinformation and Information Disorder Kalina Bontcheva

John Shawe-Taylor

The frontiers of machine learning John Shawe-Taylor

Coffee break

John Shawe-Taylor

The frontiers of machine learning John Shawe-Taylor

Lunch

Nello Cristianini

Social and ethical impact of AI Nello Cristianini

Coffee break

Students presentation/networking

Social Dinner (TBC)

Stefano Leonardi

Algorithms for Big Data Stefano Leonardi

Coffee break

Stefano Leonardi

Algorithms for Big Data Stefano Leonardi

Lunch

Marlon Dumas

Business Process Analytics: From Insights to Predictions Marlon Dumas

Business process analytics is a body of methods for analyzing data generated during the execution of business processes, in order to extract insights about weaknesses and improvement opportunities, both at the tactical and operational levels. Tactical process analytics methods (also known as process mining methods) allow us to understand how a given process is executed, if and how its execution deviates with respect to expected or normative pathways, and what factors contribute to poor process performance or undesirable outcomes. Meantime, operational process analytics methods allow us to monitor ongoing executions of a business process in order to predict future states and undesirable outcomes at runtime (predictive process monitoring). Existing methods in this space allow us to predict, for example, which task will be executed next in a case, when, and who will perform it? When will an ongoing case complete? What will its outcome be and how can negative outcomes be avoided? This lecture will present a framework for conceptualizing business process analytics methods and applications. The lecture will provide an overview of state-of-art methods and tools in the field and will outline open challenges and research opportunities, particularly in relation to explainability and actionability of predictions.

Coffee break

Marlon Dumas

Business Process Analytics: From Insights to Predictions Marlon Dumas

Business process analytics is a body of methods for analyzing data generated during the execution of business processes, in order to extract insights about weaknesses and improvement opportunities, both at the tactical and operational levels. Tactical process analytics methods (also known as process mining methods) allow us to understand how a given process is executed, if and how its execution deviates with respect to expected or normative pathways, and what factors contribute to poor process performance or undesirable outcomes. Meantime, operational process analytics methods allow us to monitor ongoing executions of a business process in order to predict future states and undesirable outcomes at runtime (predictive process monitoring). Existing methods in this space allow us to predict, for example, which task will be executed next in a case, when, and who will perform it? When will an ongoing case complete? What will its outcome be and how can negative outcomes be avoided? This lecture will present a framework for conceptualizing business process analytics methods and applications. The lecture will provide an overview of state-of-art methods and tools in the field and will outline open challenges and research opportunities, particularly in relation to explainability and actionability of predictions.

Jeroen van den Hoven

Ethics for data science Jeroen van den Hoven

Coffee break

Jeroen van den Hoven

Ethics for data science Jeroen van den Hoven

Lunch

Dino Pedreschi

Explainable AI Dino Pedreschi

Fosca Giannotti

Explainable AI Fosca Giannotti

Venue

The summer school will be in Pisa, Italy.

Centro Congressi
Le Benedettine
Pisa, Italy

Le Benedettine - housed in the former monastery on Lungarno Sonnino - is the University of Pisa's new congress center and guesthouse that provides accommodation for both Italian and international students, researchers and professors. The newly renovated housing covers 1900m², is situated in the old town, and i a few minutes walking distance from the central train station, various other public transport services and the main university buildings, schools and departments.

Accommodation

Here are some nearby hotels

Residence Le Benedettine

Residence Le Benedettine

0.1 Km from the Venue

Hotel Bologna

Hotel Bologna

0.5 Km from the Venue

Hotel Di Stefano

Hotel Di Stefano

1,4 Km from the Venue

Sponsors

F.A.Q

Steering Committee

Pedreschi

Dino Pedreschi

Full Professor at University of Pisa

Leonardi

Stefano Leonardi

Full Professor at Sapienza University of Rome

Organizing Committee

Milli

Letizia Milli

Postdoc at University of Pisa

Monreale

Anna Monreale

Assistant Professor at University of Pisa

Pratesi

Francesca Pratesi

Postdoc at University of Pisa

Registration

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Data Science Summer School 2019
€ 500

  • Regular Seating
  • Coffee Breaks
  • Lunches
  • Social Dinner
  • Deadline: 16/06/2019

Contact Us

If you need further information, please contact us.