Source: http://nicolasfiorini.info/
Timestamp: 2019-04-21 07:01:43+00:00

Document:
Data Scientist in Information Retrieval.
I am currently working as a Postdoctoral Visiting Fellow in Zhiyong Lu‘s Biomedical Text Mining group at the National Center for Biotechnology Information of the National Institutes of Health, Bethesda, USA. I also collaborate during my free time with Moreno Mitrović on topics related to computational linguistics. In the past – still very recently to me – I was a PhD student in the LGI2P lab of IMT Mines Alès, France. I happened to work with the MAB team of the LIRMM at Montpellier, on phylogenetics and evolution. On these topics, I also worked with the Ensembl team of the European Bioinformatics Institute on genomic alignments.
As a researcher at NCBI, I am privileged to be able to work on PubMed and PubMed Labs search engines. My work focuses on improving the current systems by providing more accurate query answers through a better understanding of queries, documents and relevance. This work has already impacted the experience of the millions of users PubMed welcomes every day.
Sayers, E., Agarwala, R., Bolton, E., Brister, J. et al..	Database Resources of the National Center for Biotechnology Information. Nucleic Acid Research.
Fiorini, N., Leaman, R., Lipman, D.J., Lu, Z.	How user intelligence is improving PubMed. Nature Biotechnology.
Fiorini, N., Canese, K., Bryzgunov, R., Radetska, I. et al.	PubMed Labs: An experimental system for improving biomedical literature search. Database (Oxford).
Fiorini, N., Canese, K., Starchenko, G., Kireev, E. et al.	Best Match: new relevance search for PubMed. PLoS Biology.
The new relevance search in PubMed. Allen Institute for Artificial Intelligence.
Fiorini, N., Lu, Z.	Personalized neural language models for real-world query auto completion. Proceedings of the 2018 conference of NAACL-HLT, ACL.
PubMed search and PubMed Labs. Elsevier.
Mohan, S., Fiorini, N., Kim, S., Lu, Z.	A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval. Proceedings of the 27th WWW conference.
Fellows Award for Research Excellence. NIH.
Anekalla, K.R., Courneya, J.P., Fiorini, N., Lever, J. et al.	PubRunner: A light-weight framework for updating text mining results. F1000 research, Hackathon collection.
Fiorini, N., Lipman, D.J., Lu, Z.	Towards PubMed 2.0. eLife.
Special Act or Service Group Award for the development of PubMed Labs. NLM.
Kim, S., Fiorini, N., Wilbur, W.J, Lu, Z.	Bridging the gap: incorporating a semantic similarity measure for effectively mapping PubMed queries to documents. Journal of Biomedical Informatics, Elsevier.
Mohan, S., Fiorini, N., Kim, S., Lu, Z.	Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Limited Click Logs. Proceedings of the 2017 ACL Workshop on BioNLP.
PubMed Labs: A Sandbox Toward PubMed 2.0. Pi Day, NIH.
Fiorini, N., Harispe, S., Ranwez, S., Montmain, J. et al.	Fast and reliable inference of semantic clusters. Knowledge-Based Systems, Elsevier.
Medjkoune, M., Harispe, S., Montmain, J., Cariou, S., Fanlo, J.-L., Fiorini, N.	Towards a Non-oriented Approach for the Evaluation of Odor Quality. Proceedings of IPMU 2016, Springer.
Intelligent machines that help humans face huge data. University of Graz.
Fiorini, N., Ranwez, S., Harispe, S., Montmain, J., and Ranwez, V.	USI at BioASQ 2015: a Semantic Similarity-Based Approach for Semantic Indexing. CLEF, CEUR Workshop Proceedings.
Fiorini, N., Ranwez, S. Montmain, J., Ranwez, V.	USI: a fast and accurate approach for conceptual document annotation. BMC Bioinformatics.
Fiorini, N., Harispe, S., Ranwez, S. Montmain, J., Ranwez, V.	Annotation sémantique de clusters. Proceedings of ROADEF.
Fiorini, N., Lefort, V., Chevenet, F., Berry, V. et al.	CompPhy: a web-based Collaborative Platform for Comparing Phylogenies. BMC Evolutionary Biology.
Fiorini, N., Ranwez, S., Ranwez, V. and Montmain, J.	Indexation conceptuelle par propagation. Application à un corpus d’articles scientifiques liés au cancer. Proceedings of CORIA.
Fiorini, N., Ranwez, S., Montmain, J. and Ranwez, V.	Coping with imprecision during a semi-automatic conceptual indexing process. Proceedings of IPMU 2014, Springer.

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