The Team.
This section presents our DAMA Research Group and provides concise biographical summaries of its members.
DaMA was founded in 2016
The Data Mining and Analytics (DaMA) research group was established in 2016 by Prof. Christos Tjortjis at the School of Science and Technology, International Hellenic University.
Today, the group includes 4 Postdoctoral researchers, 5 PhD candidates, and 7 MSc students.
When a Team is Above All Nothing is Impossible!
Group Lead
Christos Tjortjis
Professor
Christos is Professor in Knowledge Discovery and Software Engineering systems at the International Hellenic University, School of Science & Technology. He was Chair of the Department of Science and Technology and Dean of the School of Science and Technology, International Hellenic University (2021-23) and Vice Dean (2020-21). He is Director for the MSc in Smart Cities and Communities. Formerly he was an adjunct Associate Professor at the University of Ioannina, Dept. of Computer Science & Engineering, an adjunct Assistant Professor at the University of Western Macedonia, Dept. Engineering Informatics and Telecommunications, a tenured Lecturer at the University of Manchester, Schools of Computer Science and Informatics, as well as UMIST, Department of Computation
Studies
He holds a DEng (Hons) in Computer Engineering and Informatics (5-year studies-integrated Master) from the Department of Computer Engineering & Informatics at the University of Patras, and a BSc (Hons) in Law (4-year studies) from the Department of Law at the Democritus University of Thrace, in Greece. He also holds an MPhil in Computation from UMIST and a PhD in Informatics from the University of Manchester, U.K.
Research
His focal research area is data science, data mining, decision support, software engineering and smart cities, and his aim is to advance the use of data mining in domains such as medicine, sports, social media, energy and novel types of heterogeneous data. His research interests are in the areas of data mining and analytics, and software management, where he has published widely. His research objectives include bridging the gap between theories and applications of data mining, as well as establishing novel ways of retrieving information from data, text, social media and source code.
Publications
He published over 65 papers in international refereed journals and over 70 international refereed conferences, focusing on data mining algorithms, such as clustering, classification and association rules, and data mining applications in domains, such as software engineering, medicine, biology, sports analytics, social media, energy and smart cities. His work has been published in journals including IEEE Transactions on Knowledge and Data Engineering, Int’l Journal of Neural Systems, WIREs Data Mining and Knowledge Discovery, Integrated Computer-Aided Engineering, Smart Cities, Multimedia Tools and Applications, Information Systems, Methods of Information in Medicine, Advances in Data Analysis and Classification, Software Quality Journal, Information Systems Frontiers, Energies, Sustainable Computing: Informatics and Systems, Computing, Data & Knowledge Engineering, Int’l Journal of Data Mining and Bioinformatics, Applied Artificial Intelligence, and international referred conferences such as PVLDB, IEEE ICTAI, IEEE COMPSAC, IEEE WETICE, IEEE SMC, KSEM, IEEE ITAB, IEEE APSEC, IEEE IWPC, CSMR and IDEAL A list of publications can be found here.
Research projects
Christos led a number of research projects on software quality assurance using data mining, as well as on strategic maintenance and evolution of software. He was also involved as a principal investigator or co-investigator in a number of software and knowledge management projects. A list of research projects he has been involved with can be found here.
Research Supervision
Christos supervises 4 post-doctoral researchers as well as 5 PhD, and 7 MSc students.
Postdoctoral Researchers
Paraskevas Koukaras
Postdoctoral Researcher
Paraskevas is an adjunct lecturer and postdoctoral researcher in data science and software engineering at the School of Science & Technology, International Hellenic University (IHU). He is also a postdoctoral research associate at the Information Technologies Institute (ITI) in the Center for Research and Technology Hellas (CERTH). He is an active member of the Hellenic Artificial Intelligence Society and a member of the IHU Data Mining & Analytics (DAMA) research group. Apart from these duties, he has served as a reviewer for several journals and conferences related to artificial intelligence.
Paraskevas currently conducts postdoctoral research in Data Science at IHU (2025–, “Learning Methods for Next-Generation Digital Intelligence”) and previously (2022–2025) in “Data Mining and Knowledge Management for Smart Systems.” He holds a PhD in Data Science (IHU, 2018–2021, thesis: “Interdisciplinary data science methods using machine learning for enhanced knowledge acquisition”), an MSc in ICT Systems (IHU, 2015–2017, thesis: “Knowledge discovery in heterogeneous information networks”), and a BEng in Informatics Engineering (ATEI Thessaloniki, 2007–2013).
In addition, for the past 3.5 years, he has taught data-oriented and software engineering topics in MSc programmes, including Data Mining and Software Development Methodologies, and supervises MSc research projects in data science and related areas at IHU.
He has authored more than 50 journal articles, peer-reviewed conference papers, and book chapters since 2020 in the research areas of data mining, computing, information systems, energy and smart cities, and applied artificial intelligence. Indicatively, amongst these are Computing, Information Systems, International Journal of Neural Systems, Energies, Smart Cities, Algorithms, and Telecom.
His 7+ years of R&D project involvement are concerned with machine/deep learning and data mining for decision-making support, including but not limited to deployments in smart cities, energy management, social media mining, and healthcare. His other projects include energy generation and load forecasting, day-ahead scheduling of microgrids, and demand-response planning, along with electric vehicle charging intelligence, to name a few, and building “smart readiness” assessments—often combining prior experience with IoT-driven learning methods. During this period, Paraskevas has contributed to European and national R&D initiatives under different roles (currently as Project and R&D Manager) that translate research into practical tools and services.
Dr. Aristeidis Mystakidis is a postdoctoral researcher in Data Science and Artificial Intelligence. He holds a Ph.D. degree in “Modeling Novel Data-Driven Machine Learning Approaches for Traffic Congestion & Energy Forecasting” (2021–2025) from the International Hellenic University (IHU), School of Science and Technology, where he is also a member of the Data Mining and Analytics Research Group (DaMA).
He also serves as a postdoc AI Research Associate and Data Science Team Leader at the Information Technologies Institute (ITI) of the Centre for Research and Technology Hellas (CERTH).
He holds a Master of Engineering (MEng) in Electrical and Computer Engineering from the Democritus University of Thrace, a Master of Business Administration (MBA) with a specialization in Digital Marketing and Operational Research from the University of Macedonia, and a Master of Science (MSc) in Mobile and Web Computing from the International Hellenic University.
With over eight years of professional and research experience, he has developed a multidisciplinary background spanning academia, industry, and applied research. He worked as an IT Data Engineer at INTRACOM Constructions – Intrakat at SKG Airport (2017–2018) and later as a Data Scientist/Software Engineer at Emisia SA (2018–2021), collaborating directly with the European Environment Agency on projects related to air pollution, transport, and vehicle emissions.
Since 2021, he has focused his research at CERTH/ITI on time series forecasting, energy systems, computer vision, and cybersecurity, while actively leading and contributing to EU-funded research projects and industrial collaborations, including strategic partnerships with Samsung Electronics. In parallel, he serves as a Lab Associate at IHU (courses: Data Mining, Advanced Databases), as a Lecturer at Mediterranean College (course: Data Mining and Foundations of AI), and as a Data Scientist at Pragma-IoT, working on computer vision, foundation models, multimedia AI, model optimization/quantization/distillation, and object detection/recognition pipelines for Samsung Research.
Since 2020, his research output includes more than 25 peer-reviewed publications in high-impact journals and conferences (Springer, IEEE, MDPI, Taylor & Francis). He also serves as a reviewer for several leading journals and acts as a Guest Editor for special issues.
His main research interests include machine learning, deep learning, and data-driven AI, with emphasis on time series forecasting (energy, traffic, EV charging), computer vision, foundation models for multimodal data, cybersecurity analytics, optical character recognition, and AI applications in healthcare and smart cities.
Aristeidis Mystakidis
Postdoctoral Researcher
George Papageorgiou
Postdoctoral Researcher
PhD in Improved Data Science Methods for Real World Business Applications at the International Hellenic University, School of Science and Technology. He also holds an MSc in Data Science from International Hellenic University and a BSc in Accounting and Finance from the University of Macedonia. His research interests lie in Data Science and applied optimization methods for business applications, supported by strong expertise in Artificial Intelligence, advanced Machine Learning, algorithmic optimization, and predictive and data analytics. He also has extensive practical experience in AI applications, big data, and applied machine learning.
Dr. Vangelis Sarlis is a Senior Manager, Project & Product Management Leader, and Data Scientist (PhD, MBA, MSc, BSc) with more than 15 years of professional experience across IT, business, and research domains. With 11+ years in Project Management and 6+ years in Product Management, Business Analysis, and QA, he combines a strong technical background with strategic leadership.
He has successfully delivered data-driven, digital transformation, and business innovation projects in industries such as Telecoms, ERP/CRM, Retail, Logistics, Insurance, Finance, FinTech, InsurTech, Education, Software, and the Public Sector. His portfolio includes major engagements with organizations like the European Commission, Deloitte, Cosmote, Vodafone, Wind, ERGO, PeopleCert, and various Ministries.
Highlights of his career include:
- European Commission: Program Manager for Data Analytics, AI, IoT, Machine Learning, and Data Mining projects.
- Sports Analytics: Founder and Data Scientist, applying advanced analytics in sports performance and management.
- Retail/FMCG & Telecoms: Leading M&A-driven IT/business transformations, digitalization, and demand management teams.
- Public Sector: Designing and delivering IT/business transformations for Ministries of Labor and Health, including ICU prediction with ML during the pandemic.
- Startups: Head of Project/Product Management in InsurTech/FinTech, driving product strategy, business development, and agile delivery.
He is currently running his Postdoctoral in Data Science for Sports Analytics and Business Application (IHU) and holds a PhD in Data Science for Sports Analytics (IHU), an MBA (ALBA), an MSc in Computing & IT (Sunderland, UK), and a BSc in Physics (Aristotle University).
His expertise spans Project/Program Management (PM2, PMP, Prince2, SixSigma, Agile, Lean, SCRUM, Kanban), Data Science & Analytics (Python, R, KNIME, Power BI, Tableau, SAP, Qlik), and Business/IT Strategy.
With a proven record in innovation, data-driven decision-making, and cross-sector leadership, he thrives in complex, multicultural environments, bridging business and technology to deliver measurable impact.
Vangelis Sarlis
Postdoctoral Researcher
PhD Candidates
Vaia Apostolou
PhD Candidate
Vaia Apostolou is Senior Manager, Technical Product Owner in Data Science & AI at Pfizer’s AIDA Department (Artificial Intelligence and Data Analytics). She leads the identification, design, and rollout of enterprise-wide reusable AI/GenAI components, currently focused on Commercial & Clinical Data. A former SRE-Developer in cloud transformation, she blends product strategy with hands-on engineering and Responsible/Ethical AI guardrails to deliver governed, observable, and compliant systems at scale.
Her role centers on owning both outcomes and architecture: she defines and delivers product KPIs with stakeholders and leads the end-to-end technical architecture of AI/GenAI products—spanning data pipelines, RAG/agents, evaluation & observability, and runtime guardrails. She embeds bias, toxicity, faithfulness, and relevance/recall/precision metrics into CI/CD and live monitoring, applying SRE, IaC, and automation to ensure reliable, scalable, and cost-efficient production. Prior to Pfizer, she helped drive growth and engagement at DECA Games via KPI forecasting and experimentation, supported evidence-based policy and long-horizon planning at Greece’s Ministry of Labour & Social Affairs through predictive analytics, and strengthened risk/compliance reporting at HSBC.
She is pursuing a PhD in AI in Medicine at the International Hellenic University and also holds an MSc in Banking & Finance from the International Hellenic University, and a BSc in Business/Management from the University of Macedonia. Her technical toolkit includes cloud services (AWS), Python, SQL and PostgreSQL, Infrastructure as Code (Terraform), JavaScript/TypeScript, Java, CI/CD, containers, and observability with Grafana and CloudWatch—applied across Agentic/Gen AI, Responsible AI guardrails and evaluation, SRE, and data/ML orchestration.
Vaia’s research focuses on AI applications for cancer detection and diagnosis, spanning imaging, EHR, biomarkers, and multimodal fusion, with an emphasis on trustworthy evaluation and practical pathways to clinical utility in regulated settings.
Dr. Spyridon Konstantopoulos
PhD Candidate
Spyridon is a Physicist with an MSc degree in Automation Systems, a PhD title in Industrial Systems Monitoring and Control and a Post-Doc in the same field. His academic record includes scientific publications, conference presentations and successful research proposals. In parallel to academia and since 2013 he is continuously working as a systems expert in big industries including aerospace and pharma. He is passionate about scientific research and bridging it with real world applications. To that end, he is exploring new scientific directions by participating in DAMA team with the capacity of a PhD Candidate in Data Science. His focus is the exploitation of Machine Learning for medical applications.
Maria Vasileiou
PhD Candidate
Maria Vasileiou is a Software Engineer and PhD candidate in Data Science in Software Engineering at the International Hellenic University. She holds an MSc in Data Science from the same institution, with a dissertation on data mining in software management, and a Diploma in Electrical and Computer Engineering from the Democritus University of Thrace, specializing in electronics and microelectronics.
She works at Netcompany, where she designs and develops scalable full-stack applications using Java, Spring Boot, React, and RESTful APIs in agile environments. She also gained early industry experience in web development during her internship.
Alongside her studies, Maria has been actively involved in volunteer work, contributing to organizations such as IEEE, EESTEC, and IAS. Within these roles, she played a key part in local IT teams, focusing on website implementation and maintenance.
Her research has been published in international venues, including IEEE and Springer, with an emphasis on applying machine learning to software defect detection. Her technical expertise spans Java, Spring Boot, React, Python, SQL, and TypeScript, as well as hardware systems such as SystemVerilog and Arduino.
Her research interests focus on data science and software management, particularly exploring data-driven approaches to software quality and defect detection in order to advance innovative solutions in the field.
Anestis Kousis
PhD Candidate
Data Science for Smart Cities
Anestis Kousis is a Ph.D. candidate in Data Science for Smart Cities, in the International Hellenic University, School of Science and Technology. He has received his MSc in Education Sciences (Mathematics, Science and Information and Communication Technologies (ICT): Teaching and Learning) from Aristotle University of Thessaloniki (2017) and his BSc in Computer Science from Hellenic Open University (2015). He is a freelancer with more than 16 years of professional experience in Customer Services, as a part of FULGOR –Hellenic Cables S.A., Ful.Ge.Ca L.T.D., and General Cables-Orfanides Bros S.A. He has been working as an adult trainer for various educational institutions, such as IEK Alfa, Door Training and Consulting, Municipality of Pavlos Melas and Ministry of Education of Greece, teaching several subjects, such as Software Engineering, Databases, Data Analysis and Internet of Things.
Dimitris Rousidis
PhD Candidate
Forecasting with Social Media Data
Dimitris Rousidis is an IT Instructor in various higher education institutes and lifelong learning seminars and an enrolled PhD candidate with the International Hellenic University (IHU). His current research is associated with social media analytics and the improvement of forecasting algorithms. He holds a Bachelor’s Degree in Physics from Aristotle University of Thessaloniki, a Bachelor’s in Library Science and Information Systems from IHU, a Master’s Degree in Computation from UMIST. He worked as a tutor in Databases modules (Oracle and NoSQL oriented) in all three years of the Computing course, at the DEI College, Thessaloniki, which is collaborating with the Northampton University, UK. He has worked as an IT lab assistant in courses, such as advanced databases and spreadsheets, digital signal processing, automation of library administration and web design at the two major Technological Educational Institutes of Northern Greece (Thessaloniki and Serres). For the past 13 years he has been working as a life-long educator in Second Chance Schools, Vocational Training Centres and Lifelong Learning Centres and Vocational Training Institutes. He is a Certified Valorisation Manager with the European Certification & Qualification Association (ECQA).
MSc Candidates
Athanasios Batzelios
George Chrisovelidis
Christos Kourtzanidis
Anastasia Krapi
Nikolaos Mavriopoulos
Panagiotis Patsi
Theodoros (Theodore) Stamoulos
Alumni
Y. Al-Dara – Data Analytics for the Automatic Generation of Electricity Usage Recommendations
K. Apostolou – Sports Analytics algorithms for performance prediction
G. Asderis – Sentiment analysis on twitter data
A. Avramidou – Building CO2 emissions prediction using Machine Learning/ Data mining
D. Beleveslis – Heuristic Approach for Content Based Recommendation System Based on Feature Weighting and LSH
P. Belogianni – Recommendation systems
C. Charisiadis – Data Mining on Source Code
V. Chazan-Pantzalis – Sports Analytics Algorithms for Performance Prediction
V. Chouliara – Fake News Detection
K. Christantonis – Data mining for smart cities
C. Dontaki – Sentiment Analysis on English and Greek Twitter Data towards vaccinations
D. Gerakas – Basketball Analytics for Prediction of Performance during the last minutes of a game
O. Geromichalou – Traffic Prediction
S.M. Ghafari – Association rule mining
G. Giaglis – Programmatic Automation & Yield Optimization on the Ad Exchange
N. Giannakoulas – Sports Analytics Performance Prediction
D. Gkaimanis – Stock Market Prediction using Double-DQN and Sentiment Analysis
D. Iatropoulos – Sports Analytics Using Data Mining: NBA Player Quarter-by-Quarter Performance Exploration
C. Kaimakamis – Sports Analytics algorithms for performance prediction
D. Kalliantasis – Data mining for evaluating startups and forecasting stock fluctuations
E. Kapoteli – Sentiment Analysis Related to COVID-19 Vaccines
M. Karagkiozidou – Sentiment analysis on twitter data
P. Karatakis – Data Mining for Software Management: Automatic marking of complex Rust code using software metrics
D.P. Kasseropoulos – Influencer/ fake news detection in social media
L. Konstantopoulos – An automated tool for evaluating social media influencers
P. Koudoumas – Sports Analytics algorithms for performance prediction
S. Liapis – Big Data mining for smart cities
C. Markopoulou – Sport Analytics Algorithms for Football Performance Prediction
S. Myrotheou – Sports Analytics for statistical analysis and predictions in Formula 1
O. Nalmpantis – Movie Recommender System
I. Nasiara – The Impact of Twitter Sentiment on Ryanair’s Business Performance
C. Nousi – Stock market prediction using data mining
L. Oikonomou – Mining Twitter data to Predict the USA 2016 election winner
V. Papanikolaou – Data mining for smart cities: Predicting energy consumption in public buildings
I. Papikas – Human Rating System
A. Paraskevopoulos – Product recommendation system
M. Patsiarikas – Predicting S&P 500 Daily Prices: Integrating Macroeconomic Factors with Technical and Sentiment Indicators Using Machine Learning Models
I. Schoinas – Product Recommendation system
F. Shaban – Electricity consumption prediction using data mining
B.S. Syuqran Naim – Big Data mining for smart cities
N. Stasinos – Data Science in Law
K. Stathakis – Using Data Mining to Analyze Temporal Trends in the Reporting of Method-Related Keywords
D. Tasios – Mining Traffic Data
T.-I. Theodorou – Traffic Prediction Techniques under Abnormal Traffic Conditions
K.V. Tompra – Enhancing preventive healthcare: Identifying high-risk patients for cardiovascular diseases
F. Touparis – Predicting stocks movement using social media analytics
I. Tourpeslis – Data mining for smart cities
O. Trasanidis – Decision making tool for smart cities
N. Tsalikidis – Data Mining/ML for Smart House infrastructure
V. Tsarapatsanis – Fake news detection
E. Tsiara – Forecasting with predictive social media analytics
V. Tsichli – Predicting Stock Market Movements Using Social Media and Machine Learning
M. Vlachos-Giovanopoulos – Forecasting with predictive social media analytics
S. Yakhchi – Big data mining