Title
Neural Abstractive Summarization: Methods and Applications

Abstract
This talk focuses on recent work of our team on abstractive summarization. After a brief introduction to modern neural summarization, I will present our divide-and-conquer approach for dealing with long documents and its application to summarizing scientific articles. I will then discuss Bayesian active summarization, our approach to combining active learning with state-of-the-art summarization models. Next, I will share our efforts towards controlling the output of summarization models given a particular context, such as a topic. Finally, I will present applications in healthcare and finance.

Biodata
Grigorios Tsoumakas received a degree in Informatics from the Aristotle University of Thessaloniki (AUTH) in 1999, an MSc in Artificial Intelligence from the University of Edinburgh in 2000 and a PhD in Informatics from AUTH in 2005. He is an Associate Professor of Machine Learning and Knowledge Discovery at the School of Informatics of AUTH since 2020, where he has also served as Assistant Professor (2013 – 2020) and Lecturer (2007 – 2013). His research expertise focuses on supervised machine learning (ensemble methods, multi-target prediction) and document intelligence (semantic indexing, keyphrase extraction, summarization). He has published more than 140 articles and according to Google Scholar his work has received more than 15,000 citations and his h-index is 45. His honors include receiving the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 10-Year Test of Time Award in 2017 and the Marco Ramoni best paper award at the 19th International Conference on Artificial Intelligence in Medicine (AIME) 2021. Dr. Tsoumakas is a member of IEEE and a senior member of ACM. He is an advocate of applied research that matters and has worked as a machine learning engineer, researcher and consultant in several national, international, and private sector funded R&D projects. He is academic co-founder of Medoid AI, a spin-off company of AUTH established in 2019.

Title
Facilitating causality in science with cognitive knowledge graphs

Abstract
Knowledge graphs are meanwhile established representation forms for heterogeneous data and knowledge. However, knowledge graphs are still relatively simple structures with limited possibilities to represent and efficiently manage more complex structures, provenance, evolution or different levels of granularity. In this talk we will introduce the concept of cognitive knowledge graphs, which use richer atomic base entities - graphlets as constituents. With the Open Research Knowledge Graph (ORKG), we present an example of such a cognitive knowledge graph, which closely intertwines human and machine intelligence with the aim of semantically representing research contributions. As a result, the ORKG provides a digital research infrastructure enabling researchers and other stakeholders to obtain better oversight over the millions of annual research publications and better machine assistance for analysing the wealth of scholarly knowledge.

Biodata
Following stations at the universities of Dresden, Ekaterinburg, Leipzig, Pennsylvania, Bonn and the Fraunhofer Society, Prof. Auer was appointed Professor of Data Science and Digital Libraries at Leibniz Universität Hannover and Director of the TIB in 2017. Prof. Auer has made important contributions to semantic technologies, knowledge engineering and information systems. He is the author (resp. co-author) of over 200 peer-reviewed scientific publications. He has received several awards, including an ERC Consolidator Grant from the European Research Council, a SWSA ten-year award, the ESWC 7-year Best Paper Award, and the OpenCourseware Innovation Award. He has led several large collaborative research projects, such as the EU H2020 flagship project BigDataEurope. He is co-founder of high potential research and community projects such as the Wikipedia semantification project DBpedia, the Open Research Knowledge Graph ORKG.org and the innovative technology start-up eccenca.com. Prof. Auer was founding director of the Big Data Value Association, led the semantic data representation in the Industrial/International Data Space, is an expert for industry, European Commission, W3C, the German National Research Data Infrastructure (NFDI) and the European Open Science Cloud (EOSC).

Title
Bias in Data and Algorithms: Problems, Solutions and Stakeholders

Abstract
Mitigating bias in algorithmic processes and systems is a critical issue drawing increasing attention across research communities within the computer and information sciences. Given the complexity of the problem and the involvement of multiple stakeholders – not only developers, but also end-users and third parties – there is a need to understand the landscape of the sources of bias, as well as the solutions being proposed to address them. In this talk, I present insights from our recent survey of 300+ articles across four domains (Machine Learning, Information Retrieval, Human-Computer Interaction, and Recommender Systems) in which a critical mass of work relating to algorithmic bias has been produced, with the aim of providing a “fish-eye view” of the field. In the second part of the talk, I will discuss examples of our ongoing work on auditing proprietary computer vision systems for social biases, positioning this work vis-à-vis the aforementioned framework as well as the emerging science of machine behavior.

Biodata
Jahna Otterbacher received her doctorate in Information from the University of Michigan (Ann Arbor, USA). She is currently Associate Professor and Vice Dean of the School of Pure and Applied Sciences at the Open University of Cyprus (OUC). At OUC, she leads the Cyprus Center for Algorithmic Transparency (CyCAT), which conducts interdisciplinary research focused on promoting technical and educational solutions for promoting algorithmic transparency and literacy. Concurrent to this, Jahna co-leads the Fairness and Ethics in AI-Human Interaction (fAIre) group at CYENS, a new center of excellence and innovation in Nicosia, Cyprus, in collaboration with two international Advanced Partners, UCL and MPI. Her research has been funded by the EU’s Horizon 2020 Research and Innovation Program (under Grant Agreements No. 739578 (RISE) & No. 810105 (CyCAT)), as well as the Cyprus Research and Innovation Foundation (under grants EXCELLENCE/0918/0086 (DESCANT) and EXCELLENCE/0421/0360 (KeepA(I)n)).

Title
Semantic Data Mining and its Applications in Smart Industry

Abstract
Recently novel research directions and applications have emerged through the use of large-scale sensor data as well as advanced analysis methods.
While there is a multitude of powerful processing and analytics methods in data mining (DM) and machine learning (ML), there are also specific challenges relating to the characteristics of the data, e.g. challenges of Big Data.
Most importantly, aspects such as the interpretability and explainability of the applied data mining models and their results, motivate to apply domain/background knowledge to data mining and machine learning approaches.
In the context sensor data, processing requires not only the proper selection and curation of potentially relevant data, but also dimensionality reduction and feature engineering.
Furthermore, when applying the models for knowledge discovery and/or decision support, their transparency and explainability is often crucial, otherwise limiting acceptance and trust in their adoption in sensitive applications of AI.
Using background/domain knowledge enables semantic enrichment and ultimately semantic interpretation, leading to a knowledge-based analysis approach, which we call semantic data mining (SDM).
In this talk we will briefly outline main intuitions behind SDM and present some original results from recent projects in the area of Industry 4.0.

Biodata
Grzegorz J. Nalepa (GJN.re) is a full professor at the Jagiellonian University, formerly at the AGH University of Science and Technology, in Krakow, Poland.
He is an engineer with degrees in computer science - artificial intelligence, and philosophy. He also works as an independent expert and consultant in the area of AI (KnowAI.eu). He coordinates the GEIST.re research group in AI. He co-authored over two hundred research papers in international conferences and journals. He has been involved in tens of projects, including R+D projects with number of companies. He authored a book "Modeling with Rules using Semantic Knowledge Engineering" (Springer 2018). In 2020 he founded to Jagiellonian Human-Centered AI Laboratory (JAHCAI). His recent interests include applications of AI in Industry 4.0 and business, explainable AI, affective computing, context awareness, as well as intersection of AI with law.