I am a member of the Data Science Research Group at the Department of Computer and Systems Sciences (DSV) of Stockholm University. I hold a Ph.D. in Computer Science from the University of Pisa, Italy, and a diploma in Electrical and Computer Engineering from the National Technical University of Athens, Greece.

My research interests lie in the fields of Data Science for Social Good, Nowcasting, and Forecasting, with the use of Big Data Analytics, Data Mining, and Machine Learning. Using Big Data deriving from everyday life as external proxies, it is possible to nowcast and forecast the evolution of phenomena whose study relies only on historical data or data that come with a significant lag. I work mainly on epidemics, healthcare, peace, and sentiment.

Learn more about my research by checking my publications. If you are interested in a thesis, please check my former topics and don’t hesitate to contact me.

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Bio

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Teaching

• Master students Supervisor
  • AY 2024-2025, 2023-2024, 2022-2023, 2021-2022
  • Supervising Master students at Stockholm University, Sweden.
• Foundations of Data Science
  • AY 2024-2025
  • Master’s level course at Stockholm University, Sweden.
• Data Mining
  • AY 2023-2024, 2024-2025
  • Master’s level course at Stockholm University, Sweden.
• Data Science for Health Informatics/Design
  • AY 2022-2023
  • Master’s level course at Stockholm University, Sweden.
• Theoretical Foundations and Programming
  • AY 2020-2021, 2019-2020, 2018-2019
  • Bachelor’s level course in the “Digital Humanities” programme (Laurea) at the University of Pisa, Italy (co-teaching).
• Introduction to Computational Thinking and Programming
  • AY 2020-2021, 2019-2020
  • Teaching at High School Liceo Scientifico Statale Ulisse Dini, Pisa, Italy.
• Social Network Analysis (Teaching Assistant)
  • AY 2019-2020
  • Master’s level course in the “Data Science and Business Informatics” and “Digital Humanities” programmes (Laurea Magistrale) at the University of Pisa, Italy, under Professor Dino Pedreschi.
• Advanced Programming (Teaching Assistant)
  • AY 2015-2016
  • Master’s level course in the “Computer Science” programme (Laurea Magistrale) at the University of Pisa, Italy, under Professor Giuseppe Attardi.

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Former Master students

Stockholm University, Sweden

• Monireh Kargar Sharif Abad, MSc in Computer and Systems Sciences, “Explainable Predictive Maintenance using Survival Analysis: A Remaining Useful Life prediction of heavy vehicle components”, 2023. Related publication: HAII5.0 2024.
• Juanyi Zhang, MSc in Health Informatics, “Multimodal Learning Models to predict stage of Ovarian Cancer “, 2024.
• Filippos – Apostolos Papachristou, MSc in AI, “Personalization in Deep Federated Learning for Healthcare”, 2024.
• Gerasimos Loutsopoulos, MSc in AI, “Decoding Brain Signals with Convolutional Neural Networks: A Visual Stimuli Electroencephalography Experiment”, 2024
• Nataliya Polyakova, MSc in AI, “Counterfactual Explanations for North Sea Fish Species Population Prediction Using Machine Learning and Genetic Algorithms “, 2024.
• Damir Mandakovic, MSc in AI, “Counterfactual Explanations for Deep Forecasting Models: An Application on Fisheries Catch”, 2024.
• Erik Hector, MSc in Computer and Systems Sciences, “Predicting the stock market using machine learning and social media sentiment”, 2023.
• Anthony Ezeh, MSc in Computer and Systems Sciences, “Method for developing machine learning-based movie recommender system”, 2023.
• Nataliya Polyakova, MSc in Decision Analysis and Data Science, “Industry sales forecasting based on price elasticity”, 2023.
• Panagiotis Polyviou, MSc in Decision Analysis and Data Science, “Financial Time Series Forecasting with Machine Learning”, 2023.
• Robert Iain Salter, MSc in Decision Analysis and Data Science, “Behavioural scorecard modeling using explainable machine learning models”, 2023.
• Liwei Zhao, MSc in Decision Analysis and Data Science, “Explaining patterns of admitted intensive care unit patients admitted by unsupervised machine learning”, 2023.
• Aakruti Mishra and Navaneeth Puthiyandi, MSc in AI, “Predicting Chronic Kidney Disease/Acute Kidney Injury using multimodal Machine Learning approach”, 2023.
• Nikolaos Stavrou, MSc in Decision analysis and Data science, “Machine Learning techniques to predict the country and the type of next terrorist attack in European Union”, 2022.
• Korbinian Robert Randl and Núria Lladós Armengol, MSc in AI, “Early prediction of the risk of ICU mortality with Deep Federated Learning”, 2022. Related publication: CBMS 2023.
• Daniel Azzopardi, MSc in AI, “Predicting Sepsis Onset with Deep Federated Learning”, 2022. Related publication: PharML 2023.
• Theodoros Marinos, MSc in AI, “Early Stopping for Training Deep Learning Models in Federated Learning”, 2022.
• Linyi Zhou, MSc in Health Informatics, “Predicting drug treatment for hospitalized patients with heart failure: a machine learning approach using the MIMIC III database”, 2022. Related publication: PharML 2022.

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Research Visits

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Education

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Recent Publications

MASICU: A Multimodal Attention-based classifier for Sepsis mortality prediction in the ICU

Lena Mondrejevski, Franco Rugolon, Ioanna Miliou, Panagiotis Papapetrou

2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS), 2024

Comet: Constrained counterfactual explanations for patient glucose multivariate forecasting

Zhendong Wang, Isak Samsten, Ioanna Miliou, Panagiotis Papapetrou

2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS), 2024

M-ClustEHR: A multimodal clustering approach for electronic health records

Maria Bampa, Ioanna Miliou, Braslav Jovanovic, Panagiotis Papapetrou

Artificial Intelligence in Medicine, 2024

Ijuice: integer JUstIfied counterfactual explanations

Alejandro Kuratomi, Ioanna Miliou, Zed Lee, Tony Lindgren, Panagiotis Papapetrou

Machine Learning, 2024

Glacier: guided locally constrained counterfactual explanations for time series classification

Zhendong Wang, Isak Samsten, Ioanna Miliou, Rami Mochaourab, Panagiotis Papapetrou

Machine Learning, 2024

SHAP-Driven Explainability in Survival Analysis for Predictive Maintenance Applications

Monireh Kargar-Sharif-Abad, Zahra Kharazian, Ioanna Miliou, Tony Lindgren

HAII5.0 2024: Embracing Human-Aware AI in Industry 5.0, 2024 (Workshop at ECAI 2024)

See here for all publications.

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Contacts

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