Senior Researcher at ISTI-CNR
PhD in Computer Science from University of Pisa in 1998
Dr. Chiara Renso (Female, Google Scholar H-index is 30, Scopus H-index 21, ORCID 0000-0002-1763-2966) is a senior researcher at the ISTI Institute of CNR (Italy) holding a PhD in Computer Science. She has more than 100 peer reviewed publications in the area of mobility data mining, machine learning and and artificial intelligence methods for mobility data, analysis of geolocated social media, semantic enrichment of trajectory data, privacy in mobility data. The focus is addressing interesting, often yet unsolved problems, related to the enrichment, representation, analysis of semantic rich trajectory data, also considering privacy and ethical aspects. She has been the project coordinator of national, International and European Commission projects. Currently, she is the coordinator of a H2020-MSCA-RISE project called MASTER on multiple aspects trajectories management and analysis. She is an expert evaluator for the European Commission.
She has been local chair of the IEEE ICDM conference in 2008, local co-chair of the SIGIR conference in 2016. She has been co-chair of the workshops: SADM 2008, SADM 2009, SADM 2010 in conjunction with ICDM conference editions. She has been co-chair of three editions of the Workshop on Big Mobility Data Analytics (BMDA) held in conjunction with EDBT 2019, EDBT 2020, EDBT2021 and three editions of FATES on the Web in conjunction with The Web Conference: International Workshop on Fairness, Accountability, Transparency, Ethics and Society on the Web. She has been Workshop Chair of IEEE Mobile Data Management Conference 2020.
She is on the Editorial Board of the International Journal of GIS. She is in the PhD board of the PhD program of Computer Science of the University of Pisa.
She is Coordinator of the project MASTER (H2020-MSCA-RISE-2017 |2018-2023 – contract N. 777695).
MASTER is a EC funded under the H2020 Marie-Slodowska Curie actions as the RISE funding scheme. The objective is to form an international and inter-sectoral network of organisations working on a joint research programme to define new methods to build, manage and analyse multiple aspects semantic trajectories. We propose methods to analyze and infer knowledge from multiple aspects trajectories, considering as vital issues the privacy and big data dimensions.
Recent selected publications
Konstantinos Tserpes, Chiara Renso, Stan Matwin: Multiple-Aspect Analysis of Semantic Trajectories – First International Workshop, MASTER 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Proceedings. Lecture Notes in Computer Science 11889, Springer 2020, ISBN 978-3-030-38080-9
Lucas May Petry, Camila Leite da Silva, Andrea Esuli, Chiara Renso, Vania Bogorny: MARC: a robust method for multiple-aspect trajectory classification via space, time, and semantic embeddings. Int. J. Geogr. Inf. Sci. 34(7): 1428-1450 (2020)
Hung Cao, Monica Wachowicz, Chiara Renso, Emanuele Carlini: Analytics Everywhere: Generating Insights From the Internet of Things. IEEE Access 7: 71749-71769 (2019)
Vinicius Monteiro de Lira, Craig Macdonald, Iadh Ounis, Raffaele Perego, Chiara Renso, Valéria Cesário Times: Event attendance classification in social media. Inf. Process. Manag. 56(3): 687-703 (2019)
Lucas May Petry, Carlos Andres Ferrero, Luis Otávio Alvares, Chiara Renso, Vania Bogorny: Towards semantic-aware multiple-aspect trajectory similarity measuring. Trans. GIS 23(5): 960-975 (2019)
Christine Parent, Stefano Spaccapietra, Chiara Renso, Gennady L. Andrienko, Natalia V. Andrienko, Vania Bogorny, Maria Luisa Damiani, Aris Gkoulalas-Divanis, José Antônio Fernandes de Macêdo, Nikos Pelekis, Yannis Theodoridis, Zhixian Yan: Semantic trajectories modeling and analysis. ACM Comput. Surv. 45(4): 42:1-42:32 (2013)
Full list of publications: DBLP (https://dblp.org/pid/84/4085.html) and Scholar (https://scholar.google.com/citations?user=y-9deuAAAAAJ&hl=en)