Stefano Fiorucci

Stefano Fiorucci

San Giustino, Umbria, Italia
11.326 follower Oltre 500 collegamenti

Informazioni

📬 stefanofiorucci (at) gmail (dot) com

💫 Structural Engineer turned Software Engineer with a passion for exploring the realms of Machine Learning and Natural Language Processing.

🔍 Formerly at 01S for 5 years, I specialized in information extraction and retrieval from unstructured documents, making valuable information accessible to Italian citizens.

💙 Currently, I am proud to be part of deepset, contributing to Haystack, the open-source LLM Framework. I enjoy engaging with a vibrant community of users and contributors.

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Languages: Python, Java
Machine Learning: Pandas, scikit-learn, H2O, fundamentals of Keras, TensorFlow and PyTorch, Ludwig, streamlit
NLP: NLTK, fastText, Hugging Face Transformers/TRL/PEFT, Haystack, BERTopic, Argilla
Information extraction/retrieval: Scrapy, Tika, Tesseract, Camelot, Solr
API: Flask, FastAPI
Bash, Docker

Attività

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Esperienza

  • Grafico deepset
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    Città di Castello (PG), Italia

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    Perugia, Italia

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    Perugia, Umbria, Italia

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    Perugia, Umbria, Italia

Formazione

  • Grafico

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    Received a two-months full scholarship to participate at the School of Artificial Intelligence of Pi School. Selected among some of the brightest Engineers in the field, as a scholarship winner.
    I worked on a project presented by a real client, developing a suite of NLP and information extraction tools for the healthcare domain.

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    The school is organized by the AI Education Foundation, a charity founded by some Google DeepMind researchers.
    The goal is to provide the latest information on the state of the art in the field of Machine Learning and Artificial Intelligence, bringing together MSc students, early-stage PhDs, industry professionals, and world-class researchers.

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    Università degli Studi di Perugia, Edison Data Science Framework

    ▪ Statistics
    ▪ Python programming; basics of R programming
    ▪ Machine learning (Simone Scardapane)
    ▪ Neural networks and Deep learning (Elisa Ricci)
    ▪ Big data tools (Todor Ivanov): Apache Hadoop (HDFS, MapReduce, Sqoop,
    Flume, Hive, Impala), Apache Spark (RDD, Spark SQL, Spark Streaming,
    Dataframes, MLlib)
    ▪ Network and data security; Cloud computing; Visualization Analytics

  • Thesis: Equivalent beam models for the dynamic analysis of tall buildings: estimation of modes through methods based on sub-structures and applications to dynamic analysis in the time domain (Supervisor: Prof. Federico Cluni)

    Main subjects: computational mechanics; dynamics of structures and anti-seismic design; design of prestressed concrete structures; bridge design; design of wooden and glass structures; structural rehabilitation; foundations

Licenze e certificazioni

Esperienze di volontariato

  • Volontario

    Operazione Mato Grosso

    - Presente 23 anni e 4 mesi

    Alleviamento povertà

    Free work to support humanitarian missions in Peru, Ecuador, Brazil and Bolivia.
    6 months experience as a volunteer in Ecuador in 2008.

Pubblicazioni

  • Estimation of the Mechanical Parameters for a Reduced Coupled Flexural–Torsional Beam Model of a Tall Building by a Sub-Structure Approach

    Applied Sciences. 2021; 11(10):4655.

    The use of equivalent beam models to estimate the dynamical characteristics of complex tall buildings has been investigated by several authors. The main reason is the structural response estimation to stochastic loads, such as wind and earthquake, using a reduced number of degrees of freedom, which reduces the computational costs and therefore gives the designer an effective tool to explore a number of possible structural solutions. In this paper, a novel approach to calibrate the mechanical…

    The use of equivalent beam models to estimate the dynamical characteristics of complex tall buildings has been investigated by several authors. The main reason is the structural response estimation to stochastic loads, such as wind and earthquake, using a reduced number of degrees of freedom, which reduces the computational costs and therefore gives the designer an effective tool to explore a number of possible structural solutions. In this paper, a novel approach to calibrate the mechanical and dynamical features of a complete 3D Timoshenko beam, i.e., describing bending, shear and torsional behavior, is proposed. This approach is based on explicitly considering the sub-structures of the tall building. In particular, the frames, shear walls and lattice sub-systems are modeled as equivalent beams, constrained by means of rigid diaphragms at different floors. The overall dynamic features of the tall building are obtained by equating the deformation energy of an equivalent sandwich beam with that of the selected sub-structures. Finally, the 3D Timoshenko equivalent beam parameters are calibrated by minimizing a suitable function of modal natural frequencies and static displacements. The closed form modal solution of the equivalent beam model is used to obtain the response to stochastic loads.

    Altri autori
    Vedi pubblicazione
  • SNK @ DANKMEMES: Leveraging Pretrained Embeddings for Multimodal Meme Detection

    Proceedings of the Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2020)

    In this paper, we describe and present the results of meme detection system, specifically developed and submitted for our participation to the first subtask of DANKMEMES (EVALITA 2020).
    We built simple classifiers, consisting in feed forward neural networks.
    They leverage existing pretrained embeddings, both for text and image representation.
    Our best system (SNK1) achieves good results in meme detection (F1 = 0.8473), ranking 2nd in the competition, at a distance of 0.0028 from the…

    In this paper, we describe and present the results of meme detection system, specifically developed and submitted for our participation to the first subtask of DANKMEMES (EVALITA 2020).
    We built simple classifiers, consisting in feed forward neural networks.
    They leverage existing pretrained embeddings, both for text and image representation.
    Our best system (SNK1) achieves good results in meme detection (F1 = 0.8473), ranking 2nd in the competition, at a distance of 0.0028 from the first classified.

    Vedi pubblicazione
  • An approach based on substructures for the estimation of the response of tall buildings under wind loads using an equivalent beam

    XIV conference of the Italian association for wind engineering (IN-VENTO 2016), Terni, 25-28 settembre 2016

    The overall dynamic behaviour of tall buildings can be estimated by means of equivalent beam models, and several ways of assessing this equivalence are presented in papers by different authors.
    The most appealing benefit of this approach is that the response of the structure to stochastic
    actions, such as wind loads, can be estimated using a model with a reduced number of degrees of
    freedom, consequently decreasing the computational costs and therefore allowing the designer…

    The overall dynamic behaviour of tall buildings can be estimated by means of equivalent beam models, and several ways of assessing this equivalence are presented in papers by different authors.
    The most appealing benefit of this approach is that the response of the structure to stochastic
    actions, such as wind loads, can be estimated using a model with a reduced number of degrees of
    freedom, consequently decreasing the computational costs and therefore allowing the designer to
    explore a greater variety of possible structural solutions (Cluni et al, 2013).

    In this paper, the dynamic mechanical properties of an equivalent beam able to describe the threedimensional response of a tall building are estimated with an approach which extends that proposed in Cluni et al. (2014). The resulting model can fully describe the interaction between bending, shear and torsional behaviour.
    In particular, the approach here proposed explicitly uses the information on the sub-structures of the building to calibrate the mechanical characteristics of the equivalent beam. More in details, the frames, the shear walls and the lattice structures are identified and modelled as equivalent beams connected by rigid diaphragms at different floors. Thereafter, using an energetic approach the dynamic characteristics of the tall building are estimated (as shown in Potzta and Kollar, 2003) and used to calibrate the mechanic characteristics of the equivalent beam by means of the minimization of a suitable function of modal periods and static displacements. The modal shapes of the equivalent beam can be evaluated in closed form, and thereafter the response of the structure to wind actions is estimated with modal analysis and/or stochastic analysis.
    The results obtained with the proposed approach are finally compared both to standard Finite Element approach and to the modal approach with approximated modal shapes presented in Potzta and Kollar (2003).

    Altri autori

Riconoscimenti e premi

  • Randstad AI challenge - VGEN (1st place)

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    The aim of the competition was to train an ML model that would divide IT job offers into specific categories. I did a thorough study of the dataset, which allowed me to effectively clean up the original data. The model that has obtained the best performances is given by the simple combination of TF-IDF and a linear SVM, trained with stochastic gradient descent. I ranked 1st!

    Topics: Text classification; Naive Bayes; SVM; TF-IDF
    Libraries: scikit-learn

  • Multimodal meme detection (2nd place)

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    As part of the DANKMEMES competition (EVALITA 2020), I developed a model for multimodal detection of political memes. The proposed solution is a feed-forward neural network that leverages pretrained embeddings, both for text and image representation. I ranked 2nd, at a small distance from the winner (Tor Vergata University team).

    Topics: Multimodal classification; Word embeddings, Transformers, Image embeddings
    Libraries: Ludwig, fastText, Huggingface’s Transformers

Lingue

  • Italiano

    Conoscenza madrelingua o bilingue

  • Inglese

    Conoscenza professionale

  • Spagnolo

    Conoscenza lavorativa limitata

  • Francese

    Conoscenza base

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