Akhil Arora is a Prof. of Computer Science at Aarhus University, where he leads the CLAN for AI Research on Language and Networks (or “CLAN” for short). He is a fellow of the Copenhagen Center for Social Data Science, an affiliate of ELLIS Unit Copenhagen, the Pioneer Centre for AI, and a formal collaborator of the Wikimedia Foundation. Akhil's research lies broadly in human-centered and trustworthy AI, including state-of-the-art work on structured and multi-agent reasoning and systems for reliable and cost-efficient LLM inference. Akhil received his PhD in Computer Science from EPFL in Switzerland. In days of yore, he spent close to five years in the industry working with the research labs of Xerox and American Express as a Research Scientist. His work has been published in top-tier AI venues, including ICML, ACL, EMNLP, NAACL, and WWW, has been recognized with accolades such as the 8th most influential paper of SIGMOD 2017 by Paper Digest, and received the 2018 ACM SIGMOD Most Reproducible Paper Award. He is an alumnus of the coveted Heidelberg Laureate Forum and a DAAD AINet fellow on human-centered AI. Akhil is a director of the P1-programs on Green AI and AI & Society.

Jasmina Bogojeska is Full Professor at ZHAW School of Engineering. Previously, she was Senior Principal Data Scientist at Roche Diagnostics, and a machine learning scientist at IBM Research – Zurich working in the areas of machine learning, deep learning and natural language processing (NLP).

She received her M.S. and Ph.D. degrees both in Computer Science from the Max-Planck Institute for Informatics/Saarland University, Germany in 2007 and 2011, respectively. The main focus of her work is application-inspired machine learning where she is developing and applying machine learning solutions to various challenging real-world problems from the areas of healthcare, services research and document analysis. Jasmina has published her research in renowned international journals and presented her work at national and international conferences. She was a recipient of the IMPRS-CS fellowship awarded by the Max-Planck Society in 2005, IBM’s Corporate Research Award in 2017 and several IBM research accomplishment awards.

Katja Hose is a full professor at TU Wien, where she is leading the Data Management and Knowledge-Driven AI (DMKI) Lab. She had prior positions at Aalborg University, the Max Planck Institute for Informatics, and received her PhD from Ilmenau University of Technology.

Her research is rooted in intelligent data management and knowledge engineering and focuses on various aspects of graph data management, knowledge graphs, data integration, and applied machine learning.

Recently, I obtained my PhD in computer science, which was within a joint program with UHasselt and VUB through the Flanders AI Research Program. Currently, I'm working as a PostDoc researcher in the Databases and Artificial Intelligence research unit at TU Wien.

Within my field of study I am mainly interested in (knowledge) graph data management and provenance in databases. At the moment, I'm mostly working on fundamental aspects of SHACL and provenance. However, I am continually figuring out exactly what I find interesting.

As a researcher, I'm naturally curious and interested in how things work. My scope of interest is wide and sparse. At this stage of my life I am still figuring out what unites them — if they unite at all.

I am professor of Computer Science at Université Grenoble Alpes in the LIG laboratory. I am the head of the “Data Analytics, Intelligent and Scalable” DAISY team and the co-director of the M2 MoSIG (Master of Science in Informatics in Grenoble). My previous positions were associate professor at Université Paris-Saclay, visiting researcher at Inria and École Normale Supérieure, researcher at Huawei Hong Kong, and postdoctoral fellow at the University of Hong Kong.

I received my French research direction habilitation (HDR) in 2022 at Université Paris-Saclay, the Ph.D. degree in computer science from Télécom Paris in 2012, and my Dipl.Ing. from Politehnica University in Timisoara, Romania in 2005.

I am a Distinguished Professor of Computer Science at Universite Paris Cite, Senior Fellow of the French University Institute (IUF), a distinction that recognizes research excellence across all scientific disciplines, ACM Senior Member and Distinguished Speaker, Director of LIPADE (Computer Science department), and Director of the Data Intelligence Institute of Paris (diiP), an IdEx-funded (Initiative of Excellence) interdisciplinary institute that includes the Universite Paris Cite, Sciences Po university, the University Sorbonne Paris Nord, and the French Institute for Demographic Studies (INED).

Is My research is on problems related to data science, focusing on online and offline big data analytics and machine learning applications. It has been funded by the EU, IUF, CNRS, Facebook, IBM Research, HP Labs, Safran, Huawei, FMJH (supported by EDF and Thales), Telecom Italia, and PAT, and has led to an IBM Shared University Research Award, 3 Best Paper awards in international conferences, 10 US Patents (3 are part of commercial systems), 1 European Patent, and 2 French Patents.

My PhD focuses on the legal implications of scoring, profiling and more generally evaluating individuals. This involves articulating the existing package of EU laws regarding the matter (GDPR, DSA, DMA etc.) with its upcoming AI regulation. In this context, researching scores and profiles of individuals means crossing insights from pragmatist philosophy, the sociology of valuation and quantification or science and technology studies with classical legal interpretation of the above-mentioned regulations. This will allow me to clarify what it means to evaluate an individual, whether it is legal to do so - counterintuitively, this isn’t obvious! - and if yes, what substantial and processual protection is afforded to them.

Yannis Velegrakis is a professor of Computer Science at Utrecht University, where he holds the chair of Very Large Data Management. He is the head of the Data Intensive Systems group and the leader of the Master's in Data Science. He is also a faculty member at the University of Trento, and a PI with the Archimedes AI and Data Science hub, of the Athena Research Center.

His research areas of expertise include Big Data Management & Analytics, Knowledge Discovery, Graph Management, Highly Heterogeneous Information Integration, Dataset Querying, and Data Quality.

He holds a PhD degree from the University of Toronto and a MSc. and BSc. degree from the University of Crete, all in Computer Science. In the past he has been a researcher at the AT&T Research Labs. He has spent time as a visitor at the IBM Almaden Research Center, the Huawei European Research Center in Munich, the Center of Advanced Studies of the IBM Toronto Lab, the University of California, Santa-Cruz, the University of Paris-Saclay, and the University of Paris-Saclay, He has served in program committees of many national and international conferences and has been a reviewer for numerous international journals in the area of Data Management. He has been a general chair for VLDB2013, ICDE24 and PC Chair for EDBT21.