- Matteo Amodio, degree obtained: Laura Magistrale, University of Udine. “Evaluating Language Models for Football Possession Analytics: Team Assignment from Transcripts”, 2025.
- Eleonora Cupin, degree obtained: Laura Magistrale, University of Bologna, Forlì Campus. “Breaking gender bias in Machine Translation: Expanding the GeNTE corpus and exploring LLMs (inclusive) capabilities”, 2025.
- Manjinde Thind, degree obtained: Laura Magistrale, University of Bologna, Forlì Campus. “mGeNTE: A Multilingual Resource for Gender-Neutral Language and Translation”, 2025.
- Silvia Alma Piazzolla. degree obtained: Laura Magistrale – LM, Università di Trento (Italy). Dissertation: “Gender bias in commercial MT systems: a comparative evaluation on the MuST-SHE benchmark”, 2022
- Andrea Piergentili , degree obtained: Laurea Magistrale – LM, Lingue e Culture per la Comunicazione e Cooperazione Internazionale, Università dell’Aquila (Italy). Dissertation: “Creation of a multilingual benchmark for the evaluation of gender neutral automatic translation”, 2022
- Francesca Onorato, degree obtained: Laurea in Computer Science, University of Trento (Italy), Dissertation “Segmentation Strategies for End-to-End Simultaneous Speech Translation”, 2021
- Antonio Vespoli, degree obtained: Laurea in Computer Science, University of Trento (Italy), Dissertation: “Data-to-Text: Generating Forecast Bulletins from Raw Data”, 2020
- Andrea Alfieri, degree obtained: Master of Arts – MA, Specialized Translation, U. Bologna-Forlì, 2020
- Nicholas Ruiz, degree obtained: European Master Erasmus Mundus Language & Communication Technologies, Free University of Bozen-Bolzano, 2011.
- Diego Pineda, degree obtained: European Master in Language and Speech, Universitat Politècnica de Catalunya, 2006. Dissertation: “A Punctuation Detector in Speech Recognition”. Now with SOGETI, Barcelona, Spain.
- Florian Hönig, degree obatined: Master in Electronic Engineering, Erlangen-Nuremberg University, 2005. Dissertation: “Modifications of Perceptual Linear Prediction and the Mel-Frequency Cepstrum”. Now PhD student at Erlangen-Nuremberg University, Germany.
- Michele Vescovi, degree obtained: degree in informatics, U. Trento, Mar 2003. Dissertation: “Algoritmi per la segmentazione audio basati sul criterio di informazione bayesiano: analisi, implementazione e sperimentazione”. Now PhD Student at U. of Trento.
- Erwin Leeuwis, degree obtained: degree in informatics, U. Twente, Feb 2003. Dissertation: “A Language Model for an ASR System for Lectures”. Now at Capgemini, Utrecht Area, Netherlands.
- Nicola Bertoldi, degree obtained: Laurea degree in mathematics, U. Trento, 2000. Dissertation: “Analysis and implementation of Statistical Models for POS tagging”. Now staff researcher at FBK-irst.
- Christian Girardi, degree obtained: degree in informatics, U. Trento, 2000. Dissertation: “Sviluppo di un’interfaccia grafica per un sistema di comunicazione vocale multilingua”. Now at FBK-irst.
Our #PickOfTheWeek, selected by @BeatriceSavoldi:
"The AI Observatory: A Public Measure of Real-World AI Use" by @ShayneRedford, @AnkaReuel, @zoeykii et al.
A crucial framework tracking how AI models are deployed, adopted, and evaluated beyond lab benchmarks.
Pick of the week @fbk_mt
📚"The AI Observatory: A Public Measure of Real-World AI Use"
A study and taxonomy to explore real conversations across multiple datasets.
https://www.dataprovenance.org/ai_observatory.pdf
Can algorithmic gender prediction ever be valid?
Check out this week's top pick by @lina_conti: "Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements" by @evandongyx & @ang3linawang.
Pick of the week by @evandongyx & @ang3linawang:
https://arxiv.org/pdf/2608.13444
Predicting gender from images or names can reveal discrimination. But the practice itself harms trans people. This paper works through when that tradeoff might be justified and how to do it responsibly.
Our pick of the week by
@dhairya_su47605
: "Task-Circuit Quantization: Leveraging Knowledge Localization and Interpretability for Compression" by @hanqi_xiao, @yilin_sung, @EliasEskin and @mohitban47
#Quantization #Interpretibility
#PickoftheWeek @fbk_mt
Super cool paper on leavaraging Interpretability for Compression!
https://arxiv.org/pdf/2504.07389
Our pick of the week:
"Large Language Diffusion Model" by Shen Nie, Fengqi Zhu, @ZebinYou, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, @LiChongxuan
It is very cool to see how the researcher combine diffusion model and transformer blocks to train