Assistant Professor

Hello! I am Ioanna, an Assistant Professor at Stockholm University! I live in Stockholm, have a great dog called Orion, and I am passionate about computer science, yoga, dance and extreme sports.

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.


Stockholm University

Assistant Professor

Jan 2023 - Now

Stockholm University

Postdoctoral Research Fellow

Apr 2021 - Dec 2022

University of Pisa

Postdoctoral Research Fellow

Nov 2018 - Feb 2021

University of Pisa

Ph.D. Candidate

Nov 2014 - Oct 2018


Research Associate

Apr 2016 - Feb 2021


Software Developer & Project support-administration

Oct 2013 - Jul 2014


  • Bachelor and Master students Supervisor
    • AY 2022-2023, 2021-2022
    • Supervising Bachelor and Master students at Stockholm University, Sweden.
  • Theoretical Foundations and Programming
    • AY 2020-2021, 2019-2020, 2018-2019
    • Co-teaching for the Bachelor’s degree (Laurea) in “Digital Humanities”, at the University of Pisa, Italy.
  • 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
    • AY 2019-2020
    • Teaching Assistant for the Master’s degree (Laurea magistrale) in “Data Science and Business Informatics” and “Digital Humanities”, at the University of Pisa, Italy, under professor Dino Pedreschi.
  • Advanced Programming
    • AY 2015-2016
    • Teaching Assistant for the Master’s degree (Laurea magistrale) in “Computer science”, at the University of Pisa, Italy, under professor Giuseppe Attardi.

Former Master and Bachelor students

Stockholm University, Sweden
  • 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.
  • 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: arXiv 2022.
  • Daniel Azzopardi, MSc in AI, “Predicting Sepsis Onset with Deep Federated Learning”, 2022.
  • Theodoros Marinos, MSc in AI, “Early Stopping for Training Deep Learning Models in Federated Learning”, 2022.

Research Visits

NETSI/MOBS Lab, Northeastern University

Sept 2017 - Dec 2017

Data Science Lab, University of Piraeus

Jul 2017 - Aug 2017

University College of London (UCL)

Mar 2017

German Research Centre for Artificial Intelligence (DFKI)

Oct 2016


University of Pisa

Ph.D. in Computer Science

“Big Data Analytics for Nowcasting and Forecasting Social Phenomena”

Nov 2014 - Oct 2018

National Technical University of Athens

Diploma (5-year study program – MSc Equivalent)

in Electrical and Computer Engineering

“Tag Recommendation for Images of Flickr based on their Spatial Location”

Jan 2008 - Apr 2014

Recent Publications

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

Estimating countries’ peace index through the lens of the world news as monitored by GDELT

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

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