Emanuele Di Buccio | Italy

Emanuele Di Buccio, PhD, is an Assistant Professor at the Department of Information Engineering at the University of Padova. He holds a Master’s degree in Computer Engineering and a PhD in Information Engineering. His research interests include information access and retrieval, data science, and computational social sciences. He is particularly interested in information retrieval models and architectures, modeling and experimenting with words and themes/topics, and methodologies to support expert users in multidisciplinary fields. He participated in several international research projects such as "SAPIR: Search in Audio Visual Content Using Peer-to-peer IR" (Contract n. IST- 045128), "PROMISE" (Network of Excellence - FP7, Contract n. 25819), "QONTEXT" (FP7-PEOPLE- 2009-IRSES, Contract n. 247590); recently he was involved in "QUARTZ: Quantum Information Access and Retrieval Theory", an Innovative Training Network funded by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie (Grant Agreement No 721321), where he contributed to Work Package WP1 (Geometry and Deep Learning of Vector Spaces for Multimodal Information Access and Retrieval), WP3 (Quantum Probability and Ranking in Information Access and Retrieval), and WP6 (Training), e.g., as the chair of the QUARTZ Winter School.
 

Project at IAS-STS: Monitoring Science and Technology Issues in Online Media

The research activity will focus on methodologies to support the study of the discourse on Science and Technology issues in Online Media. The focus will be on unstructured longitudinal corpora, such as those constituted by articles published in online Newspapers or online Social Media platforms. The activity will include the investigation of methods to analyze documents in German, also relying on the experience gained in the TIPS Project (https://www.tipsproject.eu/tips/), an interdisciplinary research project of the Pa.S.T.I.S. Research Unit of the University of Padova. TIPS aims to develop, experiment, and implement automatic procedures for collecting, classifying, and analyzing digital content available on the Web to monitor science and technology topics and their evolution.
 

Selected Publications:

Neresini, F., Giardullo, P., Di Buccio, E., Morsello, B., Cammozzo, A., Sciandra, A., Boscolo,M. (2023). When scientific experts come to be media stars: An evolutionary model tested byanalysing coronavirus media coverage across Italian newspapers. PLOS ONE, 18(4),e0284841. https://doi.org/10.1371/journal.pone.0284841

Wang, B., Di Buccio, E., Melucci, M. (2021). Word2Fun: Modelling Words as Functions for Diachronic Word Representation. Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 2861–2872.

Di Buccio, E., Melucci, M. (2019). Searching for Information with Meet and Join Operators. In Quantum-Like Models for Information Retrieval and Decision-Making, 145-168. https://doi.org/10.1007/978-3-030-25913-6_8

Di Buccio, E. (2018). Utilizing sources of evidence in relevance feedback through geometry. Theoretical Computer Science, 752, 5–20. https://doi.org/10.1016/j.tcs.2018.05.027

Costa, A., Di Buccio, E., Melucci, M., Nannicini, G. (2018). Efficient Parameter Estimation forInformation Retrieval Using Black-Box Optimization. IEEE Transactions on Knowledge andData Engineering, 30(7), 1240-1253. https://doi.org/10.1109/TKDE.2017.2761749