About Us

  • Logo of Artificial Intelligence Laboratory @ University of Udine

The Artificial Intelligence Laboratory of Udine (AILAB Udine) was founded in 1984 by Professor Carlo Tasso with the aim of producing research in the areas of Neural Networks, Artificial Intelligence, Machine Learning, Knowledge-based Systems, User Modeling, Personalization of Web sites and portals, Intelligent Interfaces for information retrieval and information filtering, Enterprise information portals for knowledge management, E-learning and information sharing. The laboratory has a 30 years long history of innovation and active support to local activities. Professor Carlo Tasso and Professor Giuseppe Serra co-lead the Laboratory .

Prospective PHD Students

If you are a prospective student interested in Artificial Intelligence, Machine Learning and Deep Learning Research at the University of Udine, please read about our Ph.D. admissions process and contact us (Giuseppe Serra – giuseppe.serra@uniud.it). If you are applying to our Ph.D. course in Computer Science and are interested in our research, please state this in your statement of purpose.

Atomistic Graph Neural Networks for metals: Application to bcc iron

The useful, macroscopic characteristics of materials depend on their microscopic structure. Which is basically a very regular graph. At AILAB we applied Graph Neural Networks to the analysis of materials at atomic scale. After an extended testing phase, we obtained good results in the prediction of a set of physical properties such as Energy-Volume relation, Bain path curve, and evaluation of point and surface defects. This is a new research topic in Deep Learning, and will become more and more important in the near future thanks to its flexibility. Have a look at our arxiv paper Research Group: Lorenzo Cian (AILAB-Udine) Giuseppe Lancioni (AILAB-Udine) Lei Zhang (University of Groningen) Mirco Ianese (AILAB-Udine) Nicolas Novelli(AILAB-Udine) Giuseppe Serra (AILAB-Udine) Francesco Maresca (University of Groningen)

ADE Extraction at EACL21

Thanks to our continuous research on Adverse Drug Event Extraction, we will be at EACL 2021 (19th – 23rd April, 2021) with our latest paper: “BERT Prescriptions to Avoid Unwanted Headaches: A Comparison of Transformer Architectures for Adverse Drug Event Detection“ We explore the capabilities of wide variety of BERT-based architectures on the task of ADE extraction from social media texts.In particular we focus on the use of in-domain knowledge during pretraining, answering the question on whether (and which extent) it can actually help in this scenario, and giving some useful “prescriptions” for future research on this field. Research Group– Beatrice Portelli (AILAB-Udine) – Edoardo Lenzi (AILAB-Udine) – Simone Scaboro (AILAB-Udine) – Giuseppe Serra (AILAB-Udine) – Emmanuele Chersoni (Hong Kong Polytechnic University) – Enrico Santus (Bayer) Featured pages– ADE extraction project at AILAB-Udine– ADE at AILAB-Udine: top results on SMM4H’19

Adverse Drug Event extraction: top results on SMM4H’19 Shared Task

Pharmacovigilance monitors the drugs in the market to ensure that unexpected effects (Adverse Drug Events or ADEs) are immediately identified and actions are taken to minimize their harm. Patients have started reporting such ADEs on social media, health forums and similar outlets instead of using formal reporting methods. Given the need to monitor these sources for pharmacovigilance purposes, systems for the automatic extraction of ADE are becoming an important research topic in the NLP community and recent Shared Tasks on the topic of ADE extraction have attracted numerous focused contributions. Our research group has been working on an architecture for automatic ADE extraction from social media texts, with a focus on maintaining high performances on different text typologies (from short and noisy tweets to long and more formal medical forum posts). Our latest experiments lead us to reach the top of the leaderboard in one of the most relevant and active Shared Tasks in this field: SMM4H’19 (Social Media Mining […]

Data augmentation techniques for the Video Question Answering task

Data augmentation techniques for the Video Question Answering task

Video Question Answering (VideoQA) is a task that requires a model to analyze and understand both the visual content given by the input video and the textual part given by the question, and the interaction between them in order to produce a meaningful answer. In our work we focus on the Egocentric VideoQA task, which exploits first-person videos, because of the importance of such task which can have impact on many different fields, such as those pertaining the social assistance and the industrial training. Recently, an Egocentric VideoQA dataset, called EgoVQA, has been released. Given its small size, models tend to overfit quickly. To alleviate this problem, we propose several augmentation techniques which give us a +5.5% improvement on the final accuracy over the considered baseline.