1. Leopoldo Bertossi (Skema Business School Canada Inc., Montreal) : From Database Repairs to Causality in Databases and Beyond

  2. Marie-Christine Rousset (LIG, UGA, Grenoble) : Detecting and Explaining Privacy Risks on Temporal Data

  3. Maria-Esther Vidal (L3S, Hannovre, Allemagne) : Data Management Challenges in Healthcare Analytics



  • Leopoldo Bertossi (Skema Business School Canada Inc., Montreal)

Titre : From Database Repairs to Causality in Databases and Beyond

Résumé : The presenter, together with collaborators, introduced the notion of database repair some time ago. This area, including consistent query answering, has been extensively investigated. More recently, the presenter together with Babak Salimi unveiled a connection between database repairs and actual causality in databases, with actual causality as introduced by Helpern and Pearl. This connection allowed us to establish several new results on the computation and complexity of causes for query answers and their causal responsibility scores that reflect their causal weight. More recently, similar ideas have been applied by the presenter and coauthors to explanations for outcomes from classification with machine learning models. We will give an overview of all these connections and results.

Biographie :  Leopoldo Bertossi is a Professor at the Skema Business School Canada Inc., R&D Lab for Business AI, in Montreal, Canada. Until August 2022 he was a Full Professor at the Faculty of Engineering and Sciences, "Universidad Adolfo Ibanez" (UAI, Santiago, Chile), where he was the Director of the Graduate Programs in Data Science. He is a Senior Fellow of the UAI. His is an Emeritus Professor of the School of Computer Science, Carleton University (Ottawa, Canada), a Senior Researcher at the "Millennium Institute for Foundational Research on Data" (IMFD, Chile), and a Principal Investigator and responsible for Nuero-Symbolic AI at the newly created "National Center for Artificial Intelligence Research" (CENIA, Chile). He has been the President of the "Chilean Computer Science Society" (SCCC). He has PhD in Mathematics from the "Pontificia Universidad Catolica de Chile" (PUC, Chile, 1988). His broad research interests are related to Data Science and Artificial Intelligence. His research interests have been concentrated on explainable AI, knowledge representation, data management, computational logic, ontologies, uncertainty management and reasoning, causality, and machine learning.


  • Marie-Christine Rousset (LIG, UGA, Grenoble)

Titre : Detecting and Explaining Privacy Risks on Temporal Data

Résumé :  Personal data are increasingly disseminated over the Web through mobile devices and smart environments, and are exploited for developing more and more sophisticated services and applications. All these advances come with serious risks for privacy breaches that may reveal private information wanted to remain undisclosed by data producers. It is therefore of utmost importance to help them to identify privacy risks raised by requests of service providers for utility purposes. In this talk, I will focus on the temporal aspect for privacy protection since many applications handle dynamic data (e.g., electrical consumption, time series, mobility data) for which temporal data are considered as sensitive and aggregates on time are important for data analytics. I will present a formal approach for detecting incompatibility between privacy and utility queries expressed as temporal aggregate conjunctive queries. The distinguishing point of our approach is to be data-independent and to come with an explanation based on the query expressions only. This explanation is intended to help data producers understand the detected privacy breaches and guide their choice of the appropriate technique to correct it.

Biographie :  Marie-Christine Rousset est Professeur d’Informatique à l’Université Grenoble Alpes où elle est membre de l’équipe SLIDE du LIG. Elle développe ses thèmes de recherche, à la croisée de l’Intelligence Artificielle et des Bases de Données. Elle est l’auteur d’une centaine d’articles publiés dans des revues ou des actes de conférences internationales et a participé à l’écriture de plusieurs livres. Elle est membre de nombreux comités de programmes et de comités éditoriaux de revues, et a présidé plusieurs comités de programmes de conférences nationales ou internationales. Elle est EurAI Fellow et est co-responsable de la chaire « Explainable and Responsible AI » dans le nouvel institut d’IA de Grenoble (MIAI).

  • Maria-Esther Vidal (L3S, Hannovre, Allemagne) 

Titre : Big Data Analytics for Healthcare

Résumé : Data have drastically grown in the last decade and are expected to grow faster in the following years. Specifically, in the healthcare domain, a wide variety of methods, e.g., liquid biopsies, medical images, or genome sequencing, produce large volumes of data from where new biomarkers can be discovered. The outcomes of big data analysis correspond to building blocks for precise diagnostics and effective treatments. However, healthcare data may suffer from diverse complexity issues – volume, variety, and veracity– which demand novel techniques for data management and knowledge discovery to ensure accurate insights and conscientious decisions. In this talk, we will discuss data integration and query processing methods for tackling the challenges imposed by the complexity issues of big data and their impact on analytics. In particular, knowledge graphs will be positioned as data structures enabling the integration of heterogeneous health data and merging data with ontologies describing their meaning. We will show the benefits of exploiting knowledge graphs to uncover patterns and associations among entities. Specifically, we will illustrate these methods in the data analytics tasks of understanding the role of familial cancers in lung cancer patients and the effects of drug-drug interactions on a treatment’s effectiveness.  These two problems have been tackled in the context of the EU project CLARIFY, where data management and analytics provide the basis for supporting oncologists in identifying patient-specific risks of developing adverse secondary effects and toxicities from cancer treatments.

Biographie :  Prof. Dr. Maria-Esther Vidal is a professor at the Institute of Data Science at the Leibniz University of Hannover (LUH). She leads the Scientific Data Management group at the TIB-Leibniz Information Center of Science and Technology and the Data Science Institute at LUH. She is also a member of the L3S Research Center at LUH. Furthermore, she has received the Stifterverband Science Award on Responsible Research in Germany 2020, and the Leibniz Best Minds: Programme for Women Professors has partially supported her research since 2021. Her interests include Big data and knowledge management, knowledge representation, and the semantic web. She has published more than 180 peer-reviewed papers in Semantic Web, Databases, Bioinformatics, and Artificial Intelligence. She has co-authored one monograph and co-edited books and journal special issues. She has been part of various editorial boards, general chair, co-chair, senior member, and reviewer of several scientific events and journals. She is leading data management tasks in national and international projects (e.g., EU H2020 CLARIFY, IASIS, and BigMedilytics) and is a PI of MSCA-ETN projects (e.g., WDAqua and NoBIAS). She has been a visiting professor at universities (e.g., Uni Maryland, UPM Madrid, UPC, KIT Karlsruhe, and Uni Nantes).



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