Speakers – IDEAS 2023 https://conferences.sigappfr.org/ideas2023 The 27th International Database Engineered Applications Symposium Tue, 02 May 2023 06:17:38 +0000 en-US hourly 1 https://wordpress.org/?v=6.4.1 https://conferences.sigappfr.org/ideas2023/wp-content/uploads/2022/12/cropped-IDEAS-new-logo-32x32.jpg Speakers – IDEAS 2023 https://conferences.sigappfr.org/ideas2023 32 32 Eirini Ntoutsi https://conferences.sigappfr.org/ideas2023/speaker/eirini-ntoutsi/ Tue, 02 May 2023 06:12:37 +0000 https://conferences.sigappfr.org/ideas2023/?post_type=speaker&p=4190

Eirini Ntoutsi

Full professor at the Bundeswehr University Munich

Eirini Ntoutsi is a full professor for Open Source Intelligence at the Bundeswehr University Munich (UniBw-M) and Research Institute for Cyber-Defence and Smart Data (CODE) where she is leading the AIML lab (aiml-research.github.io), since August 2022. Prior to that, she was a full professor for Artificial Intelligence at the Free University Berlin (FUB). Before that she was an associate professor of Intelligent Systems at the Leibniz University of Hanover (LUH); she remains a member of the L3S Research Center. Prior to joining LUH, she was a post-doctoral researcher at the Ludwig-Maximilians-University (LMU) in Munich, Germany in the group of Prof. H.-P. Kriegel. She joined the group as a post-doctoral fellow of the Alexander von Humboldt Foundation. She holds a PhD in Data Mining/Machine Learning from the University of Piraeus, Greece and a master and diploma in Computer Engineering and Informatics from the University of Patras, Greece. Her research interests lie in the areas of Artificial Intelligence (AI) and Machine Learning (ML) where she develops intelligent algorithms that learn from data continuously following the cumulative nature of human learning, while ensuring that what is being learned helps driving positive societal impact.

Abstract: How to make AI more fair and unbiased

AI-driven decision-making has already penetrated into almost all spheres of human life, from content recommendation and healthcare to predictive policing and autonomous driving, deeply affecting everyone, anywhere, anytime. The discriminative impact of AI-driven decision-making on certain population groups has been already observed in a variety of cases leading to an ever-increasing public concern about the impact of AI in our lives. The domain of fairness-aware machine learning focuses on methods and algorithms for understanding, mitigating, and accounting for bias in AI/ML models. Most of the work in this field focuses on limiting learning settings, typically binary classification with a binary protected attribute. The reality is however more complex, for example, discrimination can occur based on more than one protected attribute, the class distribution might be imbalanced, the population characteristics might change, and more than one learning task might need to be solved at the same time.
In this talk, I will talk about fairness in supervised learning covering the basic binary class mono-discrimination setting as well as works towards more realistic challenges like discrimination for multiple protected attributes, discrimination under class imbalance and multi-task discrimination.

]]> Panagiota Fatourou https://conferences.sigappfr.org/ideas2023/speaker/panagiota-fatourou/ Thu, 09 Feb 2023 12:48:00 +0000 https://conferences.sigappfr.org/ideas2023/?post_type=speaker&p=4129

Panagiota Fatourou

Professor at the Department of Computer Science of the University of Crete, Greece

Panagiota Fatourou is a Professor at the Department of Computer Science of the University of Crete and the Institute of Computer Science (ICS) of the Foundation for Research and Technology (FORTH). She has worked as a visiting Professor at the School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne in Switzerland, and as a visiting researcher at the University of York and the University of Toronto in Canada. She has been a Marie-Curie Individual Fellow at Université Paris Cité, and a postdoc at Max-Planck Institut für Informatik, Saarbrücken, Germany, and at the Computer Science Department of the University of Toronto. Her research interests focus on parallel and distributed computing.P. Fatourou has served as the chair of the ACM Europe Council (October 2019 – June 2021). Since July 2015, she is an elected member of the Council, currently serving as the Past Chair.  She has served as the editor of the Distributed Computing Column of the Bulletin of the European Association for Theoretical Computer Science, and as the General Chair of the ACM Symposium on Principles of Distributed Computing(PODC 2013). She has also served as a member-at-large of the steering committees of PODC and OPODIS. She has been the PC co-chair of the 20^th  International Conference on Principles of Distributed Systems (OPODIS 2016), and of the 19th International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS 2017). She has also been an ACM Distinguished Speaker and a Featured ACM Member.

Abstract: Parallel and Distributed Data Series Processing

Processing of large collections of real-world data series is nowadays one of the most challenging and critical problems for a wide range of diverse application domains, including finance, seismology and other earth sciences, astrophysics, neuroscience, engineering, etc.  Due to the unprecedented growth in size that data series collections experience nowadays, traditional, serial-execution data series indexing technologies are rendered inadequate. Thus, one of the most pressing issues in data series processing is achieving enhanced performance and high scalability. This tutorial focuses on major techniques for building distributed and concurrent data series indexing solutions, which are designed to inherently take advantage of modern hardware, in order to accelerate data series processing times for both on-disk and in-memory data. In particular, we will study a collection of data series indices that utilize the entire computational power of modern clusters (multiple nodes, multi-core and SIMD architecture of each node, as well as Graphics Processing Units (GPUs)) to tackle the performance and scalability goals in data series processing.

]]> Ernesto Damiani https://conferences.sigappfr.org/ideas2023/speaker/ernesto-damiani/ Fri, 09 Dec 2022 16:46:30 +0000 https://conferences.sigappfr.org/ideas2023/?post_type=speaker&p=3822

Ernesto Damiani

Senior Director of Robotics and Intelligent Systems Institute at Khalifa University, UAE

Dr. Ernesto Damiani is the Senior Director of Robotics and Intelligent Systems Institute at Khalifa University. He is also a Professor in the Electrical and Computer Engineering department and Director of the Khalifa University Center for Cyber Physical Systems (C2PS).

Dr. Damiani is the Chair of the Information Security Program and a Research Professor in EBTIC. He is on extended leave from the Department of Computer Science, Università degli Studi di Milano, Italy, where he leads the SESAR research lab. He is also the President of the Italian Consortium of Computer Science Universities (CINI). Ernesto’s research interests include secure service-oriented architectures, privacy-preserving Big Data analytics and Cyber-Physical Systems security.

Dr. Damiani holds and has held visiting positions at a number of international institutions, including George Mason University in Virginia, US; Tokyo Denki University, Japan; LaTrobe University in Melbourne, Australia; and the Institut National des Sciences Appliquées (INSA) at Lyon, France. He is a Fellow of the Japanese Society for the Progress of Science.

He has been Principal Investigator in a number of large-scale research projects funded by the European Commission in the context of the Seventh Framework Program and Horizon 2020, the Italian Ministry of Research, and by private companies such as British Telecom, Cisco Systems, SAP, Telecom Italia, Siemens Networks (now Nokia Siemens) and many others.

Dr. Damiani serves in the editorial board of several international journals; among others, he is the EIC of the International Journal on Big Data and of the International Journal of Knowledge and Learning. He is Associate Editor of IEEE Transactions on Service Computing and of the IEEE Transactions on Fuzzy Systems. He is also a senior member of the IEEE and served as Vice-Chair of the IEEE Technical Committee on Industrial Informatics. In 2008, Ernesto was nominated IEEE Senior Member and ACM Distinguished Scientist and received the Chester Sall Award from the IEEE Industrial Electronics Society. Ernesto Damiani’s work has more than 16,100 citations on Google Scholar and more than 6,300 citations on Scopus, with an h-index of 34. With 494 publications listed on DBLP, he is considered among the most prolific European computer scientists.

Abstract: Blockchain Based Federated Learning for Collaborative Research: The MUSA approach

Research is increasingly based on Federated Learning (FL), where peripheral nodes train local ML models and share their parameters with a center node, where a global ML model is put together and shared again with the periphery. Today, Federated Learning is gaining acceptance in key domains like transportation, supply chain management and healthcare. Still, dysfunctional behavior and/or freeriding on the part of peripheral nodes has been shown to endanger the collaborative training effort. Based on the approach of the MUSA NNRP project to delivering big data pipelines over 5G, the talk presents FL pilot applications, summarizes attacks against FL and discusses how introducing a distributed ledger can help to alleviate them. The talk also discusses how DLT-as-a-service can support the overall lifecycle of FL models, including local models training.

]]> Ioannis Tsamardinos https://conferences.sigappfr.org/ideas2023/speaker/ioannis-tsamardinos/ Tue, 22 Nov 2022 15:52:25 +0000 https://conferences.sigappfr.org/ideas2023/?post_type=speaker&p=3715

Ioannis Tsamardinos

Professor at the Computer Science Department at the University of Crete, Greece

Ioannis Tsamardinos, Ph.D., is a Professor at the Computer Science Department of the University of Crete, CEO, and co-founder of JADBio, a University start-up. He obtained his Ph.D. from the Intelligent Systems Program at the University of Pittsburgh in 2001. Prof. Tsamardinos’ main research directions include machine learning, bioinformatics, and artificial intelligence. More specifically his computer science work emphasizes automated machine learning, feature selection, and causal discovery. Prof. Tsamardinos has over 140 publications in international journals, conferences, and books. Distinctions with colleagues and students a Gold Medal in the Student Paper Competition in MEDINFO 2004, the Outstanding Student Paper Award in AIPS 2000, the NASA Group Achievement Award for participation in the Remote Agent team, and others. Statistics on recognition of work include more than 10000 citations (1000+ a year), and an h-index of 40 (as estimated by Google Scholar). Ioannis has been awarded the European and Greek national grants of excellence, the ERC Consolidator, and the ARISTEIA II grants respectively.

Abstract: Automated Machine Learning for Knowledge Discovery

Automated Machine Learning, or AutoML, is a newly emerging field in Machine Learning. It promises to automate predictive modeling, democratize machine learning to non-experts, boost the productivity of experts, ensure the statistical validity of the modeling process, and even surpass human experts in quality. AutoML should not only strive to produce a high-quality model, but all information, explanations, interpretations, and decision support a human expert would.  In this talk, we’ll present the challenges of AutoML and the design choices we made to construct the Just Add Data Bio, or JADBio for short, AutoML platform. JADBio is particularly suited for very high dimensional data with millions of features, and low-sample datasets that present statistical estimation challenges. Particularly, JADBio focuses on Knowledge Discovery in the form of Feature Selection and identifying one or more minimal-size subsets that lead to the optimal model. Feature Selection is often the primary goal of the analysis as a first step to understanding the causal relations in our data. We’ll also discuss ongoing efforts to construct an Automated Causal Discovery engine that strives to take AutoML a step further and return the best possible Causal Model that fits the data.

]]> Marios Dikaiakos https://conferences.sigappfr.org/ideas2023/speaker/marios-dikaiakos/ Tue, 22 Nov 2022 14:18:40 +0000 https://conferences.sigappfr.org/ideas2023/?post_type=speaker&p=3711

Marios D. Dikaiakos

Professor of Computer Science at the University of Cyprus, Cyprus

Marios D. Dikaiakos is Professor of Computer Science at the University of Cyprus. He is the Founding Director of the Laboratory for Internet Computing. He also served as founding Director of the Center for Entrepreneurship of the University (2015-2021), and Head of the Computer Science Department (2010-2014). He has also worked or held short-term visiting positions and taught at the University of Washington in Seattle, USA; the University of Crete, Greece; Rutgers University, USA; the National Technical University of Athens, Greece, and Université de Paris-Cité, France. Dikaiakos received his Ph.D. in Computer Science from Princeton University (1994), an M.A. degree from Princeton (1991), and a Dipl.-Ing. degree from the National Technical University of Athens. His research interests include Internet Computing and Parallel and Distributed Systems, with recent activities focusing on Cloud and Edge Computing, Big Data, and Online Social Networks Analysis.

Abstract: From Fake News to Foreign Information Manipulation and Interference: Considerations on Threats, Mechanisms & Mitigation

The nexus of Fake News, Hate Speech, and Polarization is a grave problem in online social media platforms. Evidence suggests that this nexus is not merely an unintended byproduct of online social media dynamics but is rather the outcome of computational propaganda campaigns, which spread inaccurate or deceptive content on social media platforms in an automated manner and at a massive scale and velocity. Democratic institutions worldwide, are increasingly worried about hybrid warfare activities conducted by foreign state and non-state actors, who weaponize online social media to manipulate public opinion, undermine institutional independence and stability, erode public trust and citizens’ participation in governance processes, polarize society, and incite civil unrest. These activities represent a complex, global challenge that is constantly evolving and poses a severe threat to the security and sovereignty of democratic nations.

To counter the threat of computational propaganda, multi-disciplinary efforts are underway to understand, identify, analyze, and mitigate social media manipulation. In this talk, we review the key mechanisms that facilitate the exploitation of online social media as instruments of misinformation, the tactics employed in disinformation campaigns, and their impact on societies and democratic processes. We discuss countermeasures proposed to mitigate the spread of computational propaganda, and present approaches we proposed to analyze polarization, to identify and cope with fake news, and to identify and map hate speech. Finally, we discuss open challenges and research opportunities that arise from the emergence of recent technological breakthroughs in AI.

]]> Schahram Dustdar https://conferences.sigappfr.org/ideas2023/speaker/schahram-dustdar/ Tue, 22 Nov 2022 10:45:51 +0000 https://conferences.sigappfr.org/ideas2023/?post_type=speaker&p=3706

Schahram Dustdar

Head of the Research Division of Distributed Systems at the TU Wien, Austria

Schahram Dustdar is a Full Professor of Computer Science at the TU Wien, heading the Research Division of Distributed Systems, Austria. He holds several honorary positions: University of California (USC) Los Angeles; Monash University in Melbourne, Shanghai University, Macquarie University in Sydney, University Pompeu Fabra, Barcelona, Spain. From Dec 2016 until Jan 2017 he was a Visiting Professor at the University of Sevilla, Spain and from January until June 2017 he was a Visiting Professor at UC Berkeley, USA.

From 1999 – 2007 he worked as the co-founder and chief scientist of Caramba Labs Software AG in Vienna (acquired by ProjectNetWorld AG), a venture capital co-funded software company focused on software for collaborative processes in teams. He is co-founder of edorer.com (an EdTech company based in the US) and co-founder and chief scientist of Sinoaus.net, a Nanjing, China based R&D organization focusing on IoT and Edge Intelligence.

He was founding co-Editor-in-Chief of ACM Transactions on Internet of Things (ACM TIoT) as well as Editor-in-Chief of Computing (Springer). He is an Associate Editor of IEEE Transactions on Services Computing, IEEE Transactions on Cloud Computing, ACM Computing Surveys, ACM Transactions on the Web, and ACM Transactions on Internet Technology, as well as on the editorial board of IEEE Internet Computing and IEEE Computer. Dustdar is recipient of multiple awards: IEEE TCSVC Outstanding Leadership Award (2018), IEEE TCSC Award for Excellence in Scalable Computing (2019), ACM Distinguished Scientist (2009), ACM Distinguished Speaker (2021), IBM Faculty Award (2012). He is an elected member of the Academia Europaea: The Academy of Europe, where he currently is chairman of the Informatics Section, as well as an IEEE Fellow (2016) and an Asia-Pacific Artificial Intelligence Association (AAIA) Fellow (2021) and the AAIA president (2021).

Abstract: Learning and reasoning for distributed computing continuum ecosystems

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