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Panagiotis Papapetrou, PhD
Professor and Department Vice Head

Stockholm University, Sweden
Faculty of Social Sciences
Department of Computer and Systems Sciences

NOD Building, Borgarfjordsgatan 12
Postbox 7003, 164 07 Kista, Stockholm

Phone: +46 8 16 16 97

E-mail: panagiotis [at] dsv [dot] su [dot] se

Currently

Professor and Vice Head of the Department of Computer and Systems Sciences at Stockholm University, Sweden
Member of the Data Science Group at Stockholm University
Adjunct Professor at the Department of Computer Science at Aalto University, Finland
Board member of the Swedish Artificial Intelligence Society (SAIS)

Research Interests

My research focus is on algorithmic data mining on large and complex data. Specifically I am interested in the following topics:

time series classification and forecasting
interpretable and explainable machine learning
searching and mining large and complex sequences
learning from electronic health records

Learn more about my research by visiting my publications page, DBLP, or Google Scholar.

Read more about the research activities and current achievements of the Artificial Intelligence and Data Science research subject and the data science research group at DSV.

Teaching

Spring Term 2024:
- ML: Machine Learning
Past Courses at Stockholm University:
- DAMI: Data Mining (Fall 2013-2022)
- ML: Machine Learning (Spring 2022-2023)
- DSHI: Data Science for Health Informatics (Spring 2018-2019)
- HIPI/PROHI: Projects in Health Informatics (Fall 2013, Spring 2014-2019)
- 5HI021: Current Research and Trends in Health Informatics (Fall 2018-2021)
- BI: Business Intelligence (Fall 2014-2015)
Past Courses at other universities:
- Algorithmic Methods of Data Mining, Aalto University, Finland (2010-2011)
- Introduction to Computer Science, Boston University, USA (2008-2009)

Brief Bio

April 2017 – present: Professor, Department of Computer and Systems Sciences, Faculty of Social Sciences, Stockholm University, Sweden
December 2013 – March 2017: Associate Professor, Department of Computer and Systems Sciences, Faculty of Social Sciences, Stockholm University, Sweden
September 2013 – November 2013: Senior Lecturer (tenured), Department of Computer and Systems Sciences, Faculty of Social Sciences, Stockholm University, Sweden
September 2012 – November 2013: Lecturer and director of the IT Applications Programme, Department of Computer Science and Information Systems, School of Business-Economics-Informatics, Birkbeck, University of London, UK
September 2009 – August 2012: Postdoctoral Researcher at the Department of Computer Science, Aalto University, Finland
June 2009: Received Ph.D. in Computer Science
September 2006: Received M.A. in Computer Science
January 2004: Admitted to the MA/PhD Program of the Department of Computer Science at Boston University, USA
June 2003: Received B.Sc. in Computer Science
September 1999: Admitted to the Department of Computer Science, University of Ioannina, Greece
September 1982: Moved to the city of Ioannina, my home-town (Northwest of Greece)
July 1981: Was brought to the world in the city of Patra, Greece

Education

July 2009: Ph.D. in Computer Science, Department of Computer Science, Boston University, USA
Thesis Title: “Embedding-based Subsequence Matching in Large Sequence Databases”
September 2006: M.A. in Computer Science, Department of Computer Science, Boston University, USA
Thesis Title: “Constraint-based Mining of Frequent Arrangements of Temporal Intervals”
July 2003: B.Sc. in Computer Science, Department of Computer Science, University of Ioannina, Greece
B.Sc. Thesis Title: “Discovering Aggregate Usage Profiles in the Web using Clustering Methods”

Paper accepted at MACH journal 2024: "Glacier: Guided Locally Constrained Counterfactual Explanations for Time Series Classification"

Paper accepted at MACH journal 2024: "iJUICE: integer JUstIfied Counterfactual Explanations"

Paper accepted at DAMI journal (ECML/PKDD journal track 2023): "Z-Time: efficient and effective interpretable multivariate time series classification"

Paper accepted at ICDM 2023: "Counterfactual Explanations for Time Series Forecasting"

Paper accepted at IDA 2023: "Explaining Black Box Reinforcement Learning Agents Through Counterfactual Policies"

Paper accepted at Discovery Science 2022: "JUICE: JUstIfied Counterfactual Explanations"

Paper accepted at ECML/PKDD 2022: "Finding local groupings of time series"

Paper accepted at ECML/PKDD 2022-DEMO track: "Demonstrator on Counterfactual Explanations for Differentially Private Support Vector Machines"

Paper accepted at CBMS 2022 for: "FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction"

Paper accepted at AIME 2022 for: "EpidRLearn: Learning intervention strategies for epidemics with Reinforcement Learning"

Best paper award at AIME 2021 for: "Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients"

Paper accepted at the DAMI journal: "SMILE: A feature-based temporal abstraction framework for event-interval sequence classification"

Paper accepted at the journal of Artificial Intelligence in Medicine: "Exploiting Complex Medical Data with Interpretable Deep Learning for Adverse Drug Event Prediction"

Paper accepted at KDD 2020: "Z-Miner: an efficient method for mining frequent arrangements of event intervals"

Paper accepted at ECML/PKDD 2020: "Z-Embedding: a spectral representation of event intervals for efficient clustering and classification"

Paper accepted at ECML/PKDD 2020: "Prediction of Global Navigation Satellite System Positioning Errors with Guarantees"

Paper accepted at CBMS 2020: "A Clustering Framework for Patient Phenotyping with Application to Adverse Drug Events"

Paper accepted at CBMS 2020: "Clinical Predictive Keyboard using Statistical and Neural Language Modeling"

Paper accepted at CBMS 2020: "Mining disproportional frequent arrangements of event intervals for investigating adverse drug events"

Paper accepted at CBMS 2020: "Evaluating Local Interpretable Model-Agnostic Explanations on Clinical Machine Learning Classification Models"

Paper accepted at KAIS: "Locally and globally explainable time series tweaking"