XMANAI partner spotlight – Athena Research Center

Q: What is your organization’s role in XMANAI?

A: Our key role is to contribute to setting up the core layer of data management and AI bundles for XMANAI by implementing (a) the data services to handle data sharing, storage and management (WP2), and (b) the technical solutions relevant to the design of AI models and algorithms (WP3). Also, we are involved in the design and development of explainable AI models for graph data from the manufacturing domain and the demonstrators (WP4), leading T4.1. Based on our experience with large-scale software implementation, we are also involved in WP5, where the XMANAI’s Platform Continuous Integration will take place, while at the early stages of the project, we led the state-of-the-art review of the AI domain (T1.1).

Q: How does XMANAI relate with your or your team’s background and interests?

A: The IMSI Institute of Athena Research Center has a strong focus on Big Data Analytics and Machine Learning. We investigate ML models to make predictions at large scale, involving the learning of new representations from raw data. Key challenges addressed by our team’s activities include the scale and diversity of data, and the missing, noisy or inconsistent information. Results are applicable to several sectors including health, environment and materials, and geospatial applications. Further, explainability of AI systems lies at the core of IMSI’s research targets, focusing, beyond the manufacturing domain, also on fairness-aware explainability methods, explainability methods specific for recommender systems and explainability of AI experimentation pipelines. The development of post-hoc explainability methods, for interpreting the predictions of black-box ML models, is also within our team’s research interests.

Q: What is the novelty of XMANAI and the main benefits envisaged for your organization?

A: XMANAI aims at placing the indisputable power of Explainable AI at the service of industry, helping the manufacturing value chain to shift towards the amplifying AI era by coupling AI “glass box” (explainable) models with complex AI assets (data and models) and secure data sharing infrastructures. The aim is to solve concrete manufacturing problems with high impact. The aspect of explaining domain-specific ML methods for manufacturing is something quite challenging for us, and thus a great opportunity to expand our activities towards this direction, since the project involves a number of industrial-scale challenging demonstrators (FORD ESPANA, WHIRLPOOL, CNH ITALIA and UNIMETRIK).

Q: Which target groups can benefit from XMANAI?

A: In Athena, we are pursuing a research agenda with an emphasis on delivering tangible assets and reusable components and services for data value chains in several domains. Also, through our CORALLIA Unit and our Digital Innovation Hub, we provide start-up and spin-off incubator services, and a one-stop-shop for researchers to gain support, counseling, and financial contribution for commercial exploitation of research results. On the research level, Athena will transfer and adapt XMANAI technologies in other data domains, where it has a strong research activity. On the commercial level, Athena will give the opportunities to the involved researchers to commercialize a full array of services for a data science infrastructure tailored for the manufacturing and process industries.

Q: What do you think is the major challenge in adopting (X)AI digital solutions in practice?

A: The major challenge in adopting AI digital solutions in practice is no more the ML models per se, but the high-quality data required. “Garbage in, garbage out” is rather the first hard lesson that data scientists learn, working with data of low quality (and quantity). Things become worse when the explainability aspect is introduced since false correlations and misleading causations appear due to noisy or biased data. We are strongly in favor of data-centric (X)AI, an approach that came up recently and requires the systematic engineering of data used to build AI solutions, focusing on the creation of high-quality datasets tailored to what we expect from the AI solution to learn.

Theodore Dalamagas

Name: Theodore Dalamagas

Job title: Research Director

Organization: Athena Research Center

Bio: Theodore Dalamagas is Research Director, Vice Director of Information Management Systems Institute at Athena Research Center, co-founder and Chief Technology Officer of Symbiolabs (spinoff of Athena R.C.). He received his Diploma in Electrical Engineering from NTU Athens, Greece, his MSc in Advanced Information Systems from Glasgow University, Scotland, and his PhD from NTUA. He has more than 15 years of R&I experience of running and coordinating EU and national IT projects. His research and technology areas of interest include: scientific databanks and e-research infrastructures, data Web and information retrieval, data interoperability and integration, bioinformatics, data services for waste valorization and environmental footprint reduction. He has published more than 70 articles in international journals and conference proceedings. Google Scholar Citations reports more than 8000 citations to his work and h-index 24.

Name: Giorgos Giannopoulos

Job title: Scientific Associate

Organization: Athena Research Center

Bio: Giorgos Giannopoulos received his Diploma in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), Greece, in 2006 and his PhD in Computer Science from NTUA in 2013. He is currently a Scientific Associate at IMSI and ILSP of Athena Research and Innovation Center. He has performed research on the fields of Information Retrieval, Machine Learning, Data Integration, Geospatial Data Management and Analysis and Semantic Web, while he has been involved in the specification, design and development of cataloguing services for European Research Infrastructures. His current research interests focus on the adaptation and extension of Machine/Deep Learning methods in various disciplines, including: pattern recognition on RES timeseries; next day-level prediction of forest fires; explainability of recommender systems; explanation and prediction of Solar events; disease diagnosis based on medical images; integration and annotation of spatio-textual data; and fact checking. He has published more than 40 papers and contributed to the development of open-source tools in the above areas. He has been involved in several National and European R&D projects in various roles: software engineer, researcher, work package leader, proposal contributor, and coordinator. Google Scholar Citations reports more than 1500 citations to his work and h-index 13.

Giorgos Giannopoulos
Anargiros Tzerefos

Name: Anargiros Tzerefos

Job title: Developer

Organization: Athena Research Center

Bio: Anargiros Tzerefos received his Bachelor in Informatics and Telecommunications from the University of Peloponnese in 2019. Later in 2019, he began working for Information Management Systems Institute of Athena Research Center as a software developer, through which he has participated in many publications. His work mainly revolves around databases, containerized software and NGS (Next Generation Sequencing) data management and has also tutored the databases class in the Department of Informatics & Telecommunication in the University of Peloponnese during the first semester of 2021. He is currently an MSc student in Data Science program which is a collaboration between NSCR Demokritos and the University of Peloponnese.

Name: Vasilis Gkolemis

Job title: Research Assistant

Organization: Athena Research Center

Bio: Vasilis Gkolemis completed his Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki (Greece) in 2017, focusing on Deep Learning with applications in Computer Vision. He later worked as a Research Assistant in EU-funded projects at AUTH. He completed his MSc in Data Science at the University of Edinburgh (UK) in 2020, focusing on Probabilistic Machine Learning. Since then, he has been a developer of the software package ELFI, where he implemented the Likelihood-Free Inference method ROMC as part of his dissertation. He is currently a Research Assistant at IMSI of Athena Research and Innovation Center and a Ph.D. candidate in Explainable AI (XAI) at the Harokopio University of Athens. His scientific interests lie in Machine Learning and Deep Learning, specifically in developing explainability techniques quantifying the uncertainty of the explanations.

Vasilis Gkolemis
Eleni Lavasa

Name: Eleni Lavasa

Job title: Research Assistant

Organization: Athena Research Center

Bio: Eleni Lavasa holds a B.Sc. diploma in Physics, specialized in Astrophysics, Astronomy & Mechanics, from the National & Kapodistrian University of Athens (NKUA). She received her M.Sc. degree in Space Science, Technologies & Applications in 2020, from the University of Peloponnese & the National Observatory of Athens. Since 2020 she has been a Research Assistant at IMSI of Athena Research Center and a PhD candidate for NKUA. Her scientific interest lies mainly in Solar physics & Space Weather, machine learning & deep learning, and explainable AI. Having a strong background in data analysis, in the last few years she has gained experience in the application of XAI methods in both industrial settings and in Space Weather predictive analysis.