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 2023-2024, 2022-2023, 2021-2022
  • Supervising Master students at Stockholm University, Sweden.
• Data Mining
  • AY 2022-2023
  • Master’s level course at Stockholm University, Sweden.
• Data Science for Health Informatics/Design
  • AY 2022-2023
  • Master’s level course in the “Health Informatics” and “Design for Creative and Immersive Technology” programmes 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

• 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

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

Counterfactual Explanations for Time Series Forecasting

Zhendong Wang, Ioanna Miliou, Isak Samsten, Panagiotis Papapetrou

IEEE International Conference on Data Mining (ICDM), 2023

ORANGE: Opposite-label soRting for tANGent Explanations in heterogeneous spaces

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

IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023

Early prediction of the risk of ICU mortality with Deep Federated Learning

Korbinian Randl, Núria Lladós Armengol, Lena Mondrejevski, Ioanna Miliou

IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 2023

JUICE: JUstIfied Counterfactual Explanations

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

International Conference on Discovery Science, 2022

Predicting Drug Treatment for Hospitalized Patients with Heart Failure

Linyi Zhou, Ioanna Miliou

PharML 2022: Machine Learning for Pharma and Healthcare Applications, 2022 (Workshop at ECML PKDD 2022)

FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction

Lena Mondrejevski, Ioanna Miliou, Annaclaudia Montanino, David Pitts, Jaakko Hollmén, Panagiotis Papapetrou

IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), 2022

Impact of Dimensionality on Nowcasting Seasonal Influenza with Environmental Factors

Stefany Guarnizo, Ioanna Miliou, Panagiotis Papapetrou

International Symposium on Intelligent Data Analysis (IDA), 2022

Understanding peace through the world news

Vasiliki Voukelatou, Ioanna Miliou, Fosca Giannotti, Luca Pappalardo

EPJ Data Science, 2022

Sentiment Nowcasting During the COVID-19 Pandemic

Ioanna Miliou, John Pavlopoulos, Panagiotis Papapetrou

International Conference on Discovery Science, 2021

Predicting seasonal influenza using supermarket retail records

Ioanna Miliou, Xinyue Xiong, Salvatore Rinzivillo, Qian Zhang, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi, Alessandro Vespignani

PLOS Computational Biology, 2021

Measuring objective and subjective well-being: dimensions and data sources

Vasiliki Voukelatou, Lorenzo Gabrielli, Ioanna Miliou, Stefano Cresci, Rajesh Sharma, Maurizio Tesconi, Luca Pappalardo

International Journal of Data Science and Analytics, 2021

Artificial Intelligence (AI): new developments and innovations applied to e-commerce

Dino Pedreschi, Ioanna Miliou

Study for the committee on the Internal Market and Consumer Protection, Policy Department for Economic, Scientific and Quality of Life Policies, European Parliament, 2020

Vasiliki Voukelatou, Luca Pappalardo, Ioanna Miliou, Lorenzo Gabrielli, Fosca Giannotti

International Conference on Data Science and Advanced Analytics (DSAA), 2020

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