Focusing the Macroscope: How We Can Use Data to Understand Behavior
Joana Gonçalves de Sá,
Universidade Nova de Lisboa –
Individual decisions can have a large impact on society as a whole. This is obvious for political decisions, but still true for small, daily decisions made by common citizens. Individuals decide how to vote, whether to stay at home when they feel sick, to drive or to take the bus. In isolation, these individual decisions have a negligible social outcome, but collectively they determine the results of an election and the start of an epidemic. For many years, studying these processes was limited to observing the outcomes or to analyzing small samples. New data sources and data analysis tools have created a “macroscope” and made it possible to start studying the behavior of large numbers of individuals, enabling the emergence of large-scale quantitative social research. At the Data Science and Policy (DS&P) research group we are interested in understanding these decision-making events, expecting that this deeper knowledge will lead to a better understanding of human nature, and to improved public decisions.
In the past, we have been focusing mainly on three types of problems, strongly dependent on both the behaviors of individuals (in what we call bottom-up collective processes), and of decision-makers (the top-down decisions). The first is related with what we usually identify as political debate and deliberation and we have computationally analyzed the past 40 years of debates in the Portuguese Parliament. The second is disease dynamics, of both infections and non-infectious diseases, and we try to improve nowcasting and forecasting of several diseases and reduce antibiotic over-prescription. The third is much more fundamental and it comes from the realization that the Digital Era is offering us a giant mirror, a macroscope, that will allow us to understand human behavior at a completely new scale. By using both social networks and the spread of fake news as case studies, we are trying to identify underlying principles, both mathematical and behavioral, that can be generalized to different contexts.
In parallel, and recognizing that these tools might also have a very negative impact on society, we try to raise public awareness of these risks and involve citizens in the definition of appropriate ethical guidelines and legislation.
During the talk I will briefly describe some of these past projects and offer examples of how we can use data science to study psychology and human behavior. At the end, I will present new ideas in distributed computing and how it can help us in privacy protection.
Joana Gonçalves de Sá is an Associate Professor at Nova School of Business and Economics, Universidade Nova de Lisboa and the leader of the Data Science and Policy research group. Before that, she was a Principal Investigator at the Instituto Gulbenkian de Ciência (IGC), Portugal, where she remains as the Coordinator of the Science for Society Initiative and as the Director of the Graduate Program Science for Development (PGCD), aiming at improving science in Africa.
Her current research uses complex systems and data analytics tools to study problems at the interface between Biomedicine, Computation, Policy, Social Sciences, and Mathematics. These include epidemiology, critical thinking, network dynamics, political discourse, and their applications to human-behavior, with a large ethical and societal focus. She is also the President of the General Assembly of the Citizens Forum, an NGO that aims at improving the quality of the democratic discussion, through citizen assemblies.
Joana has a degree in Physics Engineering from Instituto Superior Técnico – University of Lisbon, and a PhD in Systems Biology from NOVA – ITQB, having developed her thesis at Harvard University, USA. In 2019, she was the recipient of an ERC Starting Grant to study human behavior using the online spread of “fake news” as a model system.
Date: 2019-Dec-20 Time: 16:00:00 Room: Room 0.19 Pavilhão informática II, IST Alameda
For more information:
11th Lisbon Machine Learning Summer School
LxMLS 2021 will take place July 7th to July 15th in online format (via zoom and slack). It is organized jointly by Instituto Superior Técnico (IST), a leading Engineering and Science school in Portugal, the Instituto de Telecomunicações, the Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC-ID), Unbabel and Cleverly.
Click here for information about past editions (LxMLS 2011, LxMLS 2012, LxMLS 2013, LxMLS 2014, LxMLS 2015, LxMLS 2016, LxMLS 2017, LxMLS 2018, LxMLS 2019, LxMLS 2020) and to watch the videos of the lectures (2016, 2017, 2018, 2020).
Call for Participation
* Application Deadline: May 15, 2021
* Decision: June 1, 2021
* Early Registration: June 15 – July 1, 2021
* Summer School: July 7 – 15, 2021
Topics and Intended Audience
The school will cover a range of Machine Learning (ML) topics, from theory to practice, that are important in solving Natural Language Processing (NLP) problems that arise in the analysis and use of Web data.
Our target audience is:
- Researchers and graduate students in the fields of NLP and Computational Linguistics;
- Computer scientists who have interests in statistics and machine learning;
- Industry practitioners who desire a more in depth understanding of these subjects.
Features of LxMLS:
- No deep previous knowledge of ML or NLP is required, but the attendants are assumed to have some basic background on mathematics and programming
- Lecturers are leading researchers in machine learning and natural language processing (see speakers)
- Days are divided into morning lectures and afternoon lab sessions and practical talks (see schedule)
- The Labs guide will be provided one month in advance. Last year’s guide can be found here
- A day zero is scheduled to review basic concepts and introduce the necessary tools for implementation exercises
- Both basic (e.g linear classifiers) and advanced topics (e.g. deep learning, reinforcement learning) will be covered
Due to the current COVID-19 pandemic, the 11th Lisbon Machine Learning School will be held online (via zoom and slack). Similar to last year, we are excited for the opportunity to create a virtual school, where you will be able to attend all the lectures, and participate in the Q&As and labs remotely. We will also provide the tools for students to engage with each other remotely. The lectures will also be streamed to YouTube, and will become freely available later in our YouTube channel. The Q&A, labs and social activities will remain restricted to the accepted students only.
List of Confirmed Speakers
LUIS PEDRO COELHO Fudan University | China
MÁRIO FIGUEIREDO Instituto de Telecomunicações & Instituto Superior Técnico | Portugal
ANDRE MARTINS Instituto de Telecomunicações & Unbabel | Portugal
IRYNA GULEYVICH Technical University Darmstat | Germany
NOAH SMITH University of Washington & Allen Institute for Artificial Intelligence | USA
SLAV PETROV Google Inc. | USA
XAVIER CARRERAS dMetrics | USA
GRAHAM NEUBIG Carnegie Mellon University | USA
BHIKSHA RAJ Carnegie Mellon University | USA
CHRIS DYER Google Deep Mind | UK
ELIAS BARENBOIM Columbia University | USA
ADELE RIBEIRO Columbia University | USA
STEFAN RIEZLER Institut für Computerlinguistik, Universität Heidelberg | Germany
BARBARA PLANK IT University of Copenhagen | Denmark
SASHA RUSH Cornell Tech | USA
Please visit the webpage for up to date information: http://lxmls.it.pt/2021
To apply, please fill the form in https://lisbonmls.wufoo.com/forms/application-form-lxmls-2021/
Any questions should be directed to: firstname.lastname@example.org.
International European Conference on Parallel and Distributed Computing
The 27th International European Conference on Parallel and Distributed Computing (Euro-Par 2021) will take from August 30 to September 3 2021 in Lisbon.
Euro-Par is the prime European conference covering all aspects of parallel and distributed processing, ranging from theory to practice, from small to the largest parallel and distributed systems and infrastructures, from fundamental computational problems to full-fledged applications, from architecture, compiler, language and interface design and implementation, to tools, support infrastructures, and application performance aspects.
The 2021 edition of Euro-Par will be organized as a collaboration between INESC-ID and Instituto Superior Técnico (IST).
– Abstract Submission: February 5, 2021
– Paper Submission Deadline: February 12, 2021
– Author Notification: April 30, 2021
– Camera-Ready Papers: June 6, 2021
More information is available here.