Data Science Summer School 2019

02-06 September, Pisa, Italy

Deadline: June 16th 2019 extended to June 27th 2019

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:

Sponsors

Speakers

Here are our speakers

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

We shall see the properties of complex systems and the role of the network architecture from a physicist’s point of view. We then introduce the properties of complex networks and in particular the scale invariance, the small world and the clustering. We shall introduce the basic formalism for graph theory (adjacency and biadjacency matrix), diffusion on graphs, Hubs Authorities and Pagerank

Slides (1) available (PDF)
Slides (2) available (PDF)

Coffee break

Guido Caldarelli

Network Science Guido Caldarelli

We shall see some basic modeling of complex networks (random graphs, Barabási-Albert, fitness model, exponential random graphs) And applications to financial systems

Slides (1) available (PDF)
Slides (2) available (PDF)

Lunch

János Kertész

Dynamics on and of networks János Kertész

Networks as scaffolds of complex systems are intrinsically dynamic: They grow and shrink, split and merge, as well as there are processes taking place on them like spreading, pocket transfer, etc. As long as the time scale of the change of the network is much slower than that of the processes a static network picture is ade- quate. When these scales get closer to each other, a different, dynamic approach is necessary. There is a class of networks, in which the connections between the nodes are only temporarily present - these are the temporal networks. Examples are communication networks, networks based on proximity or the networks of - financial transactions. Here we briefly review the characteristics of such temporal networks with special emphasis on the motifs, i.e., small, typical spatio-temporal units. We also discuss the effect of time distributions of events on spreading in temporal networks.

Slides (1) available (PDF)
Slides (2) available (PDF)

Coffee break

Cole Emmerich

Dynamics on and of networks János Kertész

Networks as scaffolds of complex systems are intrinsically dynamic: They grow and shrink, split and merge, as well as there are processes taking place on them like spreading, pocket transfer, etc. As long as the time scale of the change of the network is much slower than that of the processes a static network picture is ade- quate. When these scales get closer to each other, a different, dynamic approach is necessary. There is a class of networks, in which the connections between the nodes are only temporarily present - these are the temporal networks. Examples are communication networks, networks based on proximity or the networks of - financial transactions. Here we briefly review the characteristics of such temporal networks with special emphasis on the motifs, i.e., small, typical spatio-temporal units. We also discuss the effect of time distributions of events on spreading in temporal networks.

Slides (1) available (PDF)
Slides (2) available (PDF)

Aristides Gionis

Polarization on Social Media Aristides Gionis

Which topics spark the most heated debates on social media? How do social media users interact when they disagree? Can we nudge them towards constructive disagreement? Answering these questions helps us understand large-scale societal mechanisms - including phenomena such as partisan sharing, echo chambers, and filter bubbles, which might be detrimental to the democratic process. It also allows us to develop tools and techniques that can be used by journalists to understand which issues divide the public; social scientists to understand how polarization is manifested in social interactions; and web users to improve their experience online. This tutorial presents a systematic review of polarization as manifested online, and in particular on social media. In the beginning, we clarify the concept of polarization and the related nomenclature, drawing from the social and political sciences. Subse- quently, we review algorithmic techniques for detection, quantification, and mitigation of polarization. We conclude the tutorial by presenting open challenges and promising research directions for Web Scientists.

Slides available (PDF)

Coffee break

Aristides Gionis

Polarization on Social Media Aristides Gionis

Which topics spark the most heated debates on social media? How do social media users interact when they disagree? Can we nudge them towards constructive disagreement? Answering these questions helps us understand large-scale societal mechanisms - including phenomena such as partisan sharing, echo chambers, and filter bubbles, which might be detrimental to the democratic process. It also allows us to develop tools and techniques that can be used by journalists to understand which issues divide the public; social scientists to understand how polarization is manifested in social interactions; and web users to improve their experience online. This tutorial presents a systematic review of polarization as manifested online, and in particular on social media. In the beginning, we clarify the concept of polarization and the related nomenclature, drawing from the social and political sciences. Subse- quently, we review algorithmic techniques for detection, quantification, and mitigation of polarization. We conclude the tutorial by presenting open challenges and promising research directions for Web Scientists.

Slides available (PDF)

Lunch

Kalina Bontcheva

Misinformation and Information DisorderKalina Bontcheva

This tutorial introduces a theoretical framework for studying the information disorder. It then presents the latest studies and state-of-the-art approaches for analysing online disinformation campaigns, detecting online abuse, and quantifying their impact on the target audiences. The material is structured under six key questions: What is being spread? Who is spreading it? When it spreads? Where it spreads? Why it spreads? How it spreads? It concludes discussing outstanding challenges and hot topics for future work.

Slides available (PDF)

Coffee break

Kalina Bontcheva

Misinformation and Information Disorder Kalina Bontcheva

This tutorial introduces a theoretical framework for studying the information disorder. It then presents the latest studies and state-of-the-art approaches for analysing online disinformation campaigns, detecting online abuse, and quantifying their impact on the target audiences. The material is structured under six key questions: What is being spread? Who is spreading it? When it spreads? Where it spreads? Why it spreads? How it spreads? It concludes discussing outstanding challenges and hot topics for future work.

Slides available (PDF)

John Shawe-Taylor

The Frontiers of Machine Learning John Shawe-Taylor

Slides available (PDF)

Coffee break

John Shawe-Taylor

The Frontiers of Machine Learning John Shawe-Taylor

Slides available (PDF)

Lunch

Nello Cristianini

Social and ethical implications of AI Nello Cristianini

We have deployed a version of Artificial Intelligence at the centre of the global data infrastructure, in a way that cannot be avoided. Consequential decisions are constantly being made by AI agents about us, some minor such as recommending a video, some major, such as mortgage or health or even judiciary decisions. Many workers now are coordinated by intelligent algorithms, for example in the delivery and care-sharing economies. What are the consequences for society and the individual, of decisions that are often not explained, cannot be easily questioned, and have been suspected of being biased? As we cannot avoid this technology, we need to develop ways to live with it. Maximising accuracy or predictive performance is no longer enough, when the object of these decisions are humans. A new set of considerations is becoming important in the design of AI systems. Fixing these problem might be difficult, if we consider that some of the very shortcuts that gave us a cheap path to AI are implicated in some of the main problems that we are observing.

Slides available (PDF)

Coffee break

Trip to the beach

Social Dinner at InVilla, via Litoranea 18, Marina di Pisa

Stefano Leonardi

Algorithm and Mechanism Design for Online Markets Stefano Leonardi

Online markets currently form an important share of the global economy. The Internet hosts classical markets (real-estate, stocks, e-commerce) as well allowing new data driven markets with previously unknown features (web-based advertisement, viral marketing, digital goods, crowdsourcing, sharing economy). Algorithms play a central role in the design of economic mechanisms involved in online markets. For example, algorithms electronic auctions, trade stocks, adjusts prices dynamically, and harvest big data to provide economic information. In these lectures we present some of the main challenges of algorithm and mechanism design in online markets. We also present recent results and challenging open problems on two specific research areas: Two-sided Markets and Online Labour Marketpalces.

Slides (1) available (PDF)
Slides (2) available (PDF)

Coffee break

Stefano Leonardi

Algorithm and Mechanism Design for Online Markets Stefano Leonardi

Online markets currently form an important share of the global economy. The Internet hosts classical markets (real-estate, stocks, e-commerce) as well allowing new data driven markets with previously unknown features (web-based advertisement, viral marketing, digital goods, crowdsourcing, sharing economy). Algorithms play a central role in the design of economic mechanisms involved in online markets. For example, algorithms electronic auctions, trade stocks, adjusts prices dynamically, and harvest big data to provide economic information. In these lectures we present some of the main challenges of algorithm and mechanism design in online markets. We also present recent results and challenging open problems on two specific research areas: Two-sided Markets and Online Labour Marketpalces.

Slides (1) available (PDF)
Slides (2) available (PDF)

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.

Slides available (PDF)

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.

Slides available (PDF)

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

XAI - Science and technology for the explanation of AI decision making Dino Pedreschi

Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for the lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. The future of AI lies in enabling people to collaborate with machines to solve complex problems. Like any efficient collaboration, this requires good communication, trust, clarity and understanding. Explainable AI addresses such challenges and for years different AI communities have studied such topic, leading to different definitions, evaluation protocols, motivations, and results. This lecture provides a reasoned introduction to the work of Explainable AI (XAI) to date, and surveys the literature with a focus on machine learning and symbolic AI related approaches. We motivate the needs of XAI in real-world and large-scale application, while presenting state-of-the-art techniques and best practices, as well as discussing the many open challenges.

Slides available (PDF)

Fosca Giannotti

XAI - Science and technology for the explanation of AI decision making Fosca Giannotti

Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for the lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. The future of AI lies in enabling people to collaborate with machines to solve complex problems. Like any efficient collaboration, this requires good communication, trust, clarity and understanding. Explainable AI addresses such challenges and for years different AI communities have studied such topic, leading to different definitions, evaluation protocols, motivations, and results. This lecture provides a reasoned introduction to the work of Explainable AI (XAI) to date, and surveys the literature with a focus on machine learning and symbolic AI related approaches. We motivate the needs of XAI in real-world and large-scale application, while presenting state-of-the-art techniques and best practices, as well as discussing the many open challenges.

Slides available (PDF)

Coffee break

Informatica50: Pisa e l'Intelligenza Artificiale panel (in Italiano) moderato da Franco Turini e Fosca Giannotti

L'informatica pisana ha contribuito anche alla nascita della ricerca in Intelligenza Artificiale dando vita a molteplici scuole che hanno gemmato anche in molti altri atenei italiani definendo l'agenda di ricerca e l'innovazione della formazione superiore su temi fondazionali quali: i linguaggi per l'IA, la programmazione logica, la rappresentazione della conoscenza, l'elaborazione del linguaggio naturale, l'apprendimento automatico e la scienza dei dati. Intervengono: Franco Sirovich Luigia Carlucci Aiello, Gianfranco Prini, Giuseppe Attardi, Maria Simi, Roberto Barbuti, Dino Pedreschi, Alessio Micheli.

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

F.A.Q

Steering Committee

Pedreschi

Dino Pedreschi

Full Professor
at University of Pisa

Leonardi

Stefano Leonardi

Full Professor
at Sapienza University of Rome

Anand

Avishek Anand

Assistant Professor
at Leibniz University of Hannover

Chiaromonte

Francesca Chiaromonte

Full Professor
at Sant'Anna School of Advanced Studies

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

Lamperti

Francesco Lamperti

Assistant Professor
at Sant'Anna School of Advanced Studies

Registration

.

Data Science Summer School 2019
€ 500

  • Regular Seating
  • Coffee Breaks
  • Lunches
  • Social Dinner
  • Deadline: June 16th 2019 extended to June 27th 2019

Contact Us

If you need further information, please contact us.