Professor Dieter Kranzlmüller

Bio: Dieter A. Kranzlmüller is the Chairman of the Board of Directors of the Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities, and a professor of computer science at the Ludwig-Maximilians-Universität Munich. Kranzlmüller has a background in the IT sector, and has held positions at various universities including Reading, Dresden and Lyon. His research interests include grid and cluster computing, system programming, innovative IT infrastructure technologies and high-performance computing. In addition to his research, Kranzlmüller also supports the Center for Digital Technology and Management.

Affiliation: Ludwig Maximilians Universität Munich, Germany

DBLP: Link

Keynote Topic: Integrated Quantum and High Performance Computing


Professor Dirk Draheim

Bio: Dirk Draheim received the PhD from Freie Universität Berlin and the habilitation from University of Mannheim, Germany. Currently, he is full professor of information society technology at Tallinn University of Technology and head of the Information Systems Group, Tallinn University of Technology, Estonia. The Information Systems Group conducts research in large and ultra-large-scale IT systems. He is also an initiator and leader of numerous digital transformation initiatives. Dirk is author of the Springer books "Business Process Technology", "Semantics of the Probabilistic Typed Lambda Calculus" and "Generalized Jeffrey Conditionalization", and co-author of the Springer book "Form-Oriented Analysis".

Affiliation: Head of the Information Systems Group, Tallinn University of Technology, Estonia

DBLP: Link

Website: https://taltech.ee/en/dirk-draheim

Research interests: design and implementation of large-scale information systems

Keynote Topic: Digital Government Ecosystems: Foundations, Architecture, Implementation

Abstract: The so-called digital transformation is perceived as the key enabler for increasing wealth and well-being by politics, media and the citizens alike. In the same vein, digital government steadily receives more and more attention. Digital government gives rise to complex, large-scale state-level system landscapes consisting of many players and technological systems, i.e., digital government ecosystems. In this talk, we systematically approach the state-level architecture of digital government ecosystems. As a case study, we look into the case of e-Estonia and its data exchange layer X-Road. We will discover the primacy of the state's institutional design in the architecture of digital government ecosystems. Based on that insight, we will establish the notion of data governance architecture, which links data assets with accountable organizations. Our investigation results into a digital government architecture framework that can help in large-scale digital government design efforts. With its focus on data, the proposed framework perfectly fits the current discussion on moving from ICT-driven to data-centric digital government.


Professor Václav Snášel

Bio: Václav Snášel’s research and development experience includes over 35 years in the Industry and Academia. He works in a multi-disciplinary environment involving Artificial Intelligence, Bioinformatics, Information Retrieval, Machine Intelligence, Data Science, Nature and Biologically Inspired Computing, and applied to various real-world problems. He studied Numerical Mathematics at Palacky University in Olomouc, a PhD degree obtained at Masaryk University in Brno. From 2001 to 2009, he worked as a researcher at The Institute of Computer Science of the Academy of Sciences of the Czech Republic. Since 2009 he has worked as Head of the research program Knowledge Management at IT4Innovation National Supercomputing Center; from 2010 to 2017, he worked as the Dean of the Faculty of Electrical Engineering and Computer Science, and from 2017 he is Rector of VSB-Technical University of Ostrava. He teaches as a professor at VSB – Technical University of Ostrava.

He has given 18 plenary lectures and conference tutorials in these areas. He has authored/coauthored several refereed journal/conference papers and book chapters. He has published more than 700 papers (520 papers are indexed at Web of Science, 700 indexed at Scopus). He has supervised many PhD students from the Czech Republic, Jordan, Yemen, Slovakia, Ukraine, Russia, India, China, Lybia, and Vietnam. He also supervised postdoc students from the Slovak Republic, Uruguay, and Egypt. He is co-editor of 40 books in Springer. He is the founder of successful conference series: Euro-China conference (Shen Zhen 2014, Ostrava 2015, Fujian 2016, Malaga 2017, Xi’an 2018) and Afro-Euro Conference (Addis Ababa 2014, Paris 2015, Marrakesh 2016).

Affiliation: Rector of VSB - Technical University of Ostrava, Czech Republic

DBLP: Link

Keynote Topic: In-Memory Computing Architectures for Big data and Machine Learning Applications

Abstract: Traditional computing hardware is working to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial Intelligence algorithms such as Deep Neural Networks and Big Data. In order to get hardware solutions to meet the low-latency and high-throughput computational needs of these algorithms, Non-Von Neumann computing architectures such as In-memory Computing (IMC) have been extensively researched and experimented with over the last five years. This study analyses and reviews works designed to accelerate Machine Learning. We investigate different architectural aspects and directions and provide our comparative evaluations. We further discuss IMC research's challenges and limitations and present possible directions.


Prof. Tai M. Chung

Affiliation: Sungkyunkwan University, Korea

DBLP: Link

Keynote Topic: Security Issues for Digital Therapeutics.


Professor Manuel Clavel

Bio: Academic Career: Manuel Clavel received his Bachelor's degree in Philosophy from the Universidad de Navarra in 1992, and his Ph.D. from the same university in 1998. Currently, he is Deputy Director and Associate Research Professor at the IMDEA Software Institute, as well as Associate Professor at the Universidad Complutense de Madrid. During his doctoral studies, he was an International Fellow at the Computer Science Laboratory of SRI International (1994 - 1997) and a Visiting Scholar at the Computer Science Department of Stanford University (1995 - 1997). His Ph.D. dissertation was published by the Center for the Study of Language and Information at Stanford University.

Affiliation: Eastern International University, Vietnam

DBLP: Link

Research interests: Rigorous, tool-supported model-driven software development, including: modeling languages, model quality assurance, and code-generation. Related interests include specification languages, automated deduction, and theorem proving.

Keynote Topic: Data Protection and Privacy Laws. What are the Basic Technical Challenges ahead?

Abstract: Over the past years, countries all over the world (including Vietnam) have issued new data protection and privacy regulations. In this talk we will discuss some of the main technical challenges that software developers must face in order to comply with the new data protection and privacy regulations. To make the discussion concrete and practical, we will use as our case study a typical data-centric application and the European General Data Protection Regulation (GDPR).


Professor Ahto Buldas

Bio: AHTO BULDAS is professor of cryptography at Tallinn University of Technology. Ahto studied computer science at Tallinn University of Technology (1985-1991) and holds an MSc on simulation techniques for Boolean circuits (1992) and a PhD on computational algebraic graph theory (1999). Ahto’s research interests are related to applied cryptography. His time-stamping related research started in 1997 and he has published papers in the conferences Crypto, Asiacrypt and PKC. Ahto participated in the development of the Estonian Digital Signature Act and the Estonina eID card (1996-2002). His current research interests also includes the security and efficiency aspects of digital currencies, Ahto Buldas is a co-founder of Guardtime and also of Cybernetica AS.

Affiliation: Co-Founder and Chief Scientist at Guardtime, Chair of the OpenKSI foundation
Tallinn University of Technology, Estonia

DBLP: Link

Recognition: Arnold Humal’s Prize issued by the Estonian Mathematical Society (1995), Young Scientist Award from the President’s Cultural Foundation (2002), White Star IV Class Order (2015)

Keynote Topic: Secure and Efficient Implementation of Electronic Money

Abstract: During the last years, central banks have discussed possible use of central bank digital currencies (CBDC) — electronic cash. Besides the financial and economic factors also the security and scalability of technical implementation of CBDC have been studied. Blockchain technology provides high level of security independent of the technical infrastructure and enables central banks to outsource most of the CBDC operations to private sector while still having full control over the total amount of CBDC in circulation. Scalability has been the biggest technical concern of using blockchain based CBDC.

Nation wide deployment of electronic cash requires service rate of ten to hundred thousands transactions per second while the blockchain money solutions like Bitcoin only offer the rate of few dozen transactions per second. The key of filling the scalability gap is the possibility of decomposing (sharding) the blockchain. The efficiency of decomposition highly depends on the need for inter-component communication. For example, if two accounts are in different components, then paying from one account to another requires two simultaneous operations in both components: debiting one account and crediting the other. This is technically challenging as it requires solving the atomic commit problem, which has no deterministic time solutions if possible message loss is considered. On the other hand, if we imagine a single coin given by one person to another, the only parameter that changes is the ownership of the coin. Such operation is atomic. Hence, if an electronic money solution uses coins and bills to represent money and is sharded so that some coins and bills belong to one shard and others to another shard, then every single coin payment is uni shard and does not require inter shard communication.

In this work, we first present a sharded private blockchain based CBDC solution and analyze its efficiency and security. In the second part of the work, we study how the possibility of efficient sharding depends on the choice of the money scheme (accounts, coins, etc.)


Professor Johann Eder

Bio: Johann Eder is full professor for Information and Communication Systems in the Department of Informatics-Systems of the Alpen-Adria Universität Klagenfurt, Austria. From 2005-2013 he served as Vice President of the Austrian Science Funds (FWF). He held positions at the Universities of Linz, Hamburg and Vienna and was visiting scholar at AT&T Shannon Labs, NJ, USA, the University of California Santa Barbara, CA, USA, and the New Jersey Institute of Technology, NJ, USA.

Johann Eder published more than 190 papers in peer reviewed international journals, conference proceedings, and edited books. He chaired resp. served in numerous program committees for international conferences and as editor and referee for international journals.

Affiliation: University of Klagenfurt, Austria

DBLP: Link

Research interests: The research interests of Johann Eder are information systems engineering, business process management, and data management for medical research. A particular focus of his work is the evolution of information systems and the modelling and management of temporal information and temporal constraints. Another focus is the application of information technology for medical research in particular information systems for biobanking. He successfully directed numerous competitively funded research projects on workflow management systems, temporal data warehousing, process modelling with temporal constraints, application interoperability and evolution, information systems modelling, information systems for medical research, etc.

Keynote Topic: Managing the Quality of Data and Metadata for Biobanks

Abstract: Medical research requires biological material and data of documented trustworthy policy for delivering relevant and reproducible results. The management of the quality of biological samples for medical research received high attention in recent years resulting is well documented and audited standard operating procedures and standards for the documentation of various quality characteristics. We need similar efforts to establish systems, policies, procedures for assuring well documented quality characteristics of data and metadata. We review on the typical characteristics for data and for metadata and point to precise definitions of these properties. We present and discuss the requirements for managing these qualities and propose a process and the necessary tasks for biobanks to establish such a a holistic system for data quality management. The complex nature of biobanks as data producers, data providers, data mediators and data archives dealing with data from various sources and the highly sensitive nature of personal health data for quality data make them a most interesting use case for data quality management supporting both known and unknown future demands.


Professor Truyen Tran

Bio: Dr. Truyen Tran is Associate Professor, Head of AI, Health and Science at Deakin University where he leads a research team on the next generation of deep learning and applications to computer vision, computational science, biomedicine and software analytics. He publishes regularly at top AI/ML/KDD venues such as NeurIPS, ICML, ICLR, CVPR, UAI, AAAI, IJCAI and KDD. Tran has received multiple recognitions, awards and prizes including Best Paper Runner Up at UAI (2009), Geelong Tech Award (2013), CRESP Best Paper of the Year (2014), Third Prize on Kaggle Galaxy-Zoo Challenge (2014), Title of Kaggle Master (2014), Best Student Papers Runner Up at PAKDD (2015) and ADMA (2016), and Distinguished Paper at ACM SIGSOFT (2015). He obtained a Bachelor of Science from University of Melbourne and a PhD in Computer Science from Curtin University in 2001 and 2008, respectively.

Affiliation: Applied Artificial Intelligence Institute & School of IT, Deakin University

DBLP: Link

Keynote Topic: Deep Analytics via Learning to Reason

Abstract: Deep learning, enabled by powerful compute, and fuelled by massive data, has delivered unprecedented data analytics capabilities. However, major limitations remain. Chiefly among those is that deep neural networks tend to exploit the surface statistics in the data, creating short-cuts from the input to the output, without really deeply understanding of the data. As a result, these networks fail miserably to generalize to novel combinations. This is because the networks perform shallow pattern matching but not deliberate reasoning – the capacity to deliberately deduce new knowledge out of the contextualized data. Second, machine learning is often trained to do just one task at a time, making it impossible to re-define tasks on the fly as needed in a complex operating environment. This talk presents our recent developments to extend the capacity of neural networks to remove these limitations. Our main focus is on learning to reason from data, that is, learning to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary querying using natural languages without the need of predefining a narrow set of tasks.


Professor Phan Thanh An

Affiliation: Ho Chi Minh City University of Technology, Vietnam

Website: Homepage

DBLP: Link

Keynote Topic: Computational Geometry for Autonomous Robots


Professor Josef Küng

Affiliation: Johannes Kepler University Linz, Austria

Website: Homepage

DBLP: Link

Keynote Topic: Security Aspects in Information Systems.


Professor Sadok Ben Yahia

Bio: Sadok BEN YAHIA is full Professor at the Southern Denmark University (SDU) since September 2023. Before joining SDU, he was full professor at Technology University of Tallinn (TalTech) since January 2019. He obtained his HDR in Computer Sciences from the University of Montpellier (France) in April 2009 and since January 2019. He is the head of the Data Science Group in the IT School, and his research interests mainly focus on data-driven approaches for near-real-time Big Data analytics, e.g., urban mobility in smart cities (e.g., information aggregation & dissemination, traffic congestion prediction), Recommendation System. and fake content fighting.

Affiliation: Southern Denmark University (SDU), Denmark

DBLP: Link

Keynote Topic: Resilient Zero-touch Framework for Sustainable Urban Mobility

Abstract:The transportation sector is responsible for 23% of energy-related CO2 emissions. Decarbonizing transportation is challenging, as it is still 92% dependent on non-renewable resources. However, current transport decarbonization-related policies are insufficient to decrease CO2 emissions to the expected level. Therefore, strategic approaches to reducing emissions from urban transport are critical to addressing the challenges of climate change.

In this talk, we present our recent research activities on a framework to build the next level of innovative data-driven traffic light strategies as the most impactful action to reduce CO2 emissions within the context of urban mobility for Connected and Autonomous Cars. This Framework is committed to embracing the next generation of Edge-AI, benefiting from the ease of implementation and increased computation power toward more composable, distributed, and federated intelligence, as well as security by design frameworks. Powerful eye-bird-view multimodal data fusion approaches feed AI models for accurate CO2 and urban noise level predictions, that feed to dashboards for awareness purposes. Advanced reinforcement learning techniques make use of urban noise predictions to implement the best traffic light strategy in real time. We will also discuss the challenges to achieve resilience by proactively detecting misbehaving entities within Vehicle-to-Everything settings.