Media are facing an ever-increasing volume of data - statistical, social, crime, scientific, as well as auxiliary ones such as data coming from social media, various media resources, and others. Media can take advantage of novel techniques to help them process the multiple streams of data so they can extract insights from those data. The purpose of this three-days school is to present methods, tools and techniques for exploring, mining, and visualising large data sets by using the National Center for Supercomputing Applications (NSCA) resources, AI, and NLP/ML techniques.

The school contains an introductory set of lectures aimed at presenting how natural language processing (NLP), artificial intelligence and other machine learning tools can assist media professionals in their daily journalistic and editorial work. Furthermore, participants will be introduced to artificial neural networks and how to use them to successfully search in large scale text repositories based on semantic meaning, Machine Translation, Automatic text translation during NLP tasks, Speech-to-text, Text-to-speech etc.

The school will consist of introductory lectures, presented by computer scientists (and data scientists), as well as hands-on sessions. Moreover, practical insights on a few use cases of interest to the journalists will be introduced.


At the end of the course, the journalists will possess the information and know how to use the following skills:

- Knowledge about the use of NLP tools and their benefits;

- Knowledge about the use of AI, NLP/ML techniques;

- Knowledge and use of Neural Networks utilisation for searching in large scale text repositories based on semantic meaning;

- Work with Machine Translation, Automatic text translation during NLP tasks, Speech-to-text, Text-to-speech etc.

Target audience:

Journalists, students, PhD, and researchers, who are interested in new technologies and methods to process and analyse large amounts of data.


Participants must have basic knowledge in search of news and statistical methods.

Applicants will be selected according to their experience, qualification and interest BASED ON WHAT IS WRITTEN IN THE "Reason for participation" FIELD OF THE REGISTRATION FORM.



About the Tutors:

Dr. Kristina Kapanova (Female) is the executive director of the Bulgarian National Supercomputing Center. She is part of the Socia Petascale HPC system, with funding from EuroHPC. She has worked in Trinity College, Dublin as Research Fellow in AI, working on AI methods for social networks analysis and large scale analysis. She has also led the Masters Course for High Performance Computing Architecture and Systems. She has proposed and implemented several novel neural network training methods and have implemented them in drug design, text analysis and quantum mechancis.

Dr. Dimitar Shterionov (Male) is an assistant professor at the Department of Cognitive Science and Artificial Intelligence at Tilburg University, The Netherlands. He is an experienced researcher in MT and NLP, an expert in a plethora of subtopics of MT such as low-resource MT, quality estimation, automatic post-editing, evaluation of MT quality and usability, speech-to-text and text-to-speech translation, etc. He has also experience with user-centred development of software solutions, as well as cloud-based solutions to MT, e.g., KantanMT. Dimitar has obtained a PhD in computer science engineering from KU Leuven in 2015 on the topic of Probabilistic Logic and Learning. After that he moved to industry and joined the team of KantanMT to work on their cloud-based customizable statistical machine translation system. In 2016, he was appointed head of research at KantanMT. His team developed the first cloud-based, customizable and publically available neural machine translation solution, released in early 2017. In November 2017, Dimitar joined the ADAPT team of Prof Andy Way at Dublin City University, Ireland, as a post-doctoral researcher to work on industry-oriented MT and NLP projects. In January 2020 he was appointed an assistant professor at the Department of Computing at DCU; later this year, in August 2020, he assumed a tenure track position at Tilburg University. In his academic career Dimitar has published over 30 papers in national and international conference proceedings and journals; he has completed 4 large-scale and 5 small-scale projects. In the last 3 years, Dimitar’s research has been focused on Automatic Post-editing, Quality Estimation, MT for low-resource languages, Lexical richness of MT and Cross- lingual Information Retrieval. He also actively participates in cross-field collaborations (e.g. in the medical, arts and legal domains).

Kevin Koidl (Male) - Dr Kevin Koidl is a Research Fellow at the Trinity College Dublin ADAPTCenter in the School of Computer Science and Statistics. His research focuses is on advancing the State of the Art of Social Interaction System (Social Media, Social Networks, IoT Environments, Social Proximity Networks, Ad-hoc Social Networks, Organic Social Graphs and Decentralised Social Networks), Machine Learning (NLP, NER, Text Classification, Deep Learning, Neural Networks, Explainable AI and Trustworthy AI), AI-driven Digital Content Technology (Recommendation Systems, Personalised Information Retrieval, Serendipity and User Modelling), Semantic Reasoning (Linked Data, Semantic Web, LOD and Knowledge Graph), Data Privacy and Ethics (GDPR and Online Privacy) and Heath Informatics. Kevin has attracted Private and Public Irish and International funding and has led EU wide research and commercial projects.