George Panagopoulos

George Panagopoulos

Data Mining Researcher

About Me

Education and Work Experience

I am a PhD student in the Data Science and Mining Team of the Computer Science Laboratory of École Polytechnique, advised by Prof. Michalis Vazirgiannis and Assistant Prof. Fragkiskos Malliaros . Previously, I was a teaching and research assistant in University of Houston, where I obtained my M.Sc. in computer science advised by Prof. Ioannis Pavlidis. Before that, I was working as a research associate for 2 years in the Institute of Informatics and Telecommunications of NCSR Demokritos. I hold a B.Sc. in Informatics and Telematics from Harokopio University of Athens, where I completed my diploma thesis with Assistant Prof. Iraklis Varlamis .

Scientific Interests

My interests lie in the intersection of machine learning and network science. More specifically, I research machine learning methods for temporal networks and spreading processes. I am also keen on exploring unsupervised learning, forecasting, and multi-task learning techniques. In terms of applications and modalities, I have worked with a variety of data, ranging from coauthorship graphs, brain networks and essays, to wearable sensors and EEG signals. Finally, I am a Brain Computer Interface enthusiast and am eager to run tutorials or experiments and get my hands dirty with brain data, whenever I have the chance.



January 2019 - Now

PhD in Computer Science

Ecole Polytechnique

Advisor: Dr. Michalis Vazirgiannis, Dr. Fragkiskos Malliaros

August 2016 - May 2018

Masters in Computer Science

University of Houston

Advisor: Dr. Ioannis Pavlidis

September 2010 - July 2014

Bachelor in Informatics and Telematics

Harokopio University of Athens

Advisor: Dr. Iraklis Varlamis


June 2018--Present

Research Engineer

École Polytechnique
August 2017--May 2018

Teaching Assistant

University of Houston

Statistical Methods in Research

Software Engineering

May 2017--August 2017

Research Intern

NCSR Demokritos, Software Knowledge and Engineering Lab

Prediction of distraction for drivers based on their physiological and driving signals using Hidden Markov Models.
Used: R, Python

August 2016 - May 2017

Research Assistant

University of Houston, Computational Physiology Lab

Curated, combined, synchronized and preprocessed data from a driving simulation experiment and an on road driving experiment. The data came from 5 different sensors and consisted of multiple formats.
Used: R, Python

September 2014 - July 2016

Research Associate

NCSR Demokritos, Software Knowledge and Engineering Lab
  • Brain Computer Interfaces

    • Designed and run experiments and tutorials for graduate and undergraduate students, in Demokritos Summer School 2015 & 2016.

      Used: Python, Jupyter Notebooks, Emotiv Epoc+, MATLAB, C++ [ code 2015 ] [ code 2016 ]

    • Presented an interactive program to familiarize school children age 9 -15 with Brain Computer Interfaces. Overall, 121 schools attended in the span of 7 months, with over 2500 children taking part in it.

    • Presented ‘MindPong’, a pong game based on Emotiv Epoc+ brain computer interface implemented by our team, in Athens Science Festival 2016.

  • User Modeling & Natural Language Processing

    • Implemented computational services as part of a profiling server that retrieves essays and derives creativity profiles for its users based on computational creativity metrics and a matrix factorization technique. It supports three languages.

      Used: Java, Wordnet, Weka, MATLAB, Web Services, MySQL [ code ]

    • Implemented a set of web services that use natural language processing, unsupervised machine learning and semantic engineering to model the creativity in essays written by children. It supports three languages and facilitates android games that enhance creativity of children.

      Used: Java, Web Services, MySQL, Web Crawling, Facebook & Twitter API [ code ]

June 2013 - September 2013

Software Engineering Intern

NCSR Demokritos

Web crawling and database management for bibliographic data.
Used: Delphi, Java , MySQL



G. Panagopoulos, F. Malliaros and M. Vazirgiannis

Multi-task Learning for Influence Estimation and Maximization

IEEE Transactions On Knowledge and Data Engineering (2020) Impact Factor: 4.935

Short Description: A multi-task influence learnng model and an algorithm that uses the learnt representations to perform efficient influence maximization. [code] [video]

G. Panagopoulos and I. Pavlidis

Forecasting Markers of Habitual Driving Behaviors Associated With Crash Risk

IEEE Transactions On Intelligent Transportation Systems (2019) Impact Factor: 4.051

Short Description: An extreme gradient boosting model that takes as input the physiological signals of a driver and vehicle indications and provides short-term predictions of distracted or aggressive driving. [code] [presentation]

I. Pavlidis, D. Zavlin, A. Khatri, A. Wesley, G. Panagopoulos and A. Echo

Absence of Stressful Conditions Accelerates Dexterous Skill Acquisition in Surgery

Nature Scientific Reports (2019) Impact Factor: 4.122

Short Description: Analysis of an experiment that dealt with the skill acquisition of surgigal trainees, using statistical inference and visualization of physiological recordings and questionairs. [data]

G. Panagopoulos, G. Tsatsaronis and I. Varlamis

Detecting Rising Stars in Dynamic Collaborative Networks

Elsevier Journal of Informetrics (2017) Impact Factor: 3.879

Short Description: Identify young scientists with high potential, using social network analysis and unsupervised machine learning. [code]


G. Panagopoulos, G. Nikoletzos, M. Vazirgiannis.

Transfer Graph Neural Networks for Pandemic Forecasting

AAAI International Conference on Artificial Intelligence (AAAI),2021

Short Description: A graph neural network that uses temporal mobility networks and the recent history of the epidemic to predict the number of COVID-19 cases per day in NUTS3 geographical regions of different EU countries. [pdf] [code]

G. Panagopoulos, F. Malliaros, M. Vazirgiannis.

Influence Maximization using Influence and Susceptibility Embeddings Best paper runner up

AAAI International Conference on Web and Social Media (ICWSM), 2020

Short Description: An influence maximization method based on influence representation learning from diffusion cascades. [pdf] [code] [video]

G. Panagopoulos, C. Xypolopoulos, K. Skianis, C. Giatsidis, J. Tang, M. Vazirgiannis.

Scientometrics for Success and Influence in the Microsoft Academic Graph

International Conference on Complex Networks and Their Applications (Complex Networks), 2019

Short Description: An online application with visualizations on scientific success, using field-based h-index and large scale d-core decomposition on the MAG. [code] [app] [presentation]

G. Panagopoulos, F. Malliaros, M. Vazirgiannis

DiffuGreedy: An Influence Maximization Algorithm based on Diffusion Cascades

International Conference on Complex Networks and Their Applications (Complex Networks), 2018

Short Description: We propose a new algorithm to perform influence maximization utilizing diffusion cascades that have taken place over the network. [pdf][code]

G. Panagopoulos

Multi-Task Learning for Commercial Brain Computer Interfaces

IEEE BioInformatics and BioEngineering (BIBE), 2017

Short Description: Apply and compare conventional and multi-task machine learning algorithms used in Brain Computer Interface literature in two open datasets of mental monitoring experiments which utilized Neurosky EEG device. [pdf] [code] [presentation] [poster]

P. Karampiperis, A. Koukourikos G. Panagopoulos

From Computational Creativity Metrics to the Principal Components of Human Creativity

Knowledge, Information and Creativity Support Systems (KICSS), 2014

Short Description: Model the creativity of children playing serious games in school, with natural language processing and semantic analysis. [pdf] [code]

A Koukourikos, P Karampiperis, G. Panagopoulos

Creative Stories: A Storytelling Game fostering Creativity

Cognition and Exploratory Learning in Digital Age (CELDA), 2014

Short Description: A serious game using computational semantic lateral thinking techniques to motivate children write more creative essays. [pdf] [code] [presentation]


G. Panagopoulos, H. Jalalzai

Graph Neural Networks with Extreme Nodes Discrimination

Deep Learning on Graphs: Methods and Applications, KDD 2020

Short Description: An experimental analysis on the use of extreme value theory in semi-supervised classification using graph neural networks. [pdf] [code]

P. Boniol, G. Panagopoulos, C. Xypolopoulos, R. El Hamdani, D. Restrepo Amariles, M. Vazirgiannis

Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France

Workshop on Natural Legal Language Processing, KDD 2020

Short Description: Text mining and analysis of entities in legal documents of French appeal court decisions. [pdf]

G. Panagopoulos, C. Palmer

A Specialized Interactive Data Application for EEG Based Sleep Studies

Workshop on Assistive Technologies for Decision making in Healthcare, PETRA 2017

Short Description: A web data application using signal processing techniques and visualizations to assist sleep EEG analysis in a study with anxious children. [pdf] [code] [presentation]

G. Panagopoulos, P. Karampiperis, A. Koukourikos, S. Konstantinidis

Creativity Profiling Server: Modelling the Principal Components of Human Creativity over Texts

Workshop on Deep Content Analytics Techniques for Personalized and Intelligent Services, UMAP 2015

Short Description: A server modeling a user's creativity based on textual exhibits, employing computational creativity metrics and unsupervised learning. [pdf] [code] [presentation]


G. Panagopoulos

Network Inference from Neural Activation Time Series: A comparative review

International Conference on Network Science, 2018

Short Description: Apply and compare different network inference techniques to Kaggle connectomics small dataset. [pdf] [code] [poster]




  • Kevin Murphy, "Machine Learning: a Probabilistic Perspective", MIT press
  • Jon Kleinberg and Éva Tardos, "Algorithm Design", Pearson Education
  • Simon Haykin, "Neural Networks, A Comprehensive Foundation", Pearson
  • Deepayan Chakrabarti and Christos Faloutsos, "Graph Mining: Laws, Tools, and Case Studies", Morgan & Claypool Publishers
  • Albert-László Barabási and Pósfai Márton, "Network science", Cambridge university press


  • Roger Penrose, "The Emperor's New Mind", Oxford University Press
  • Marvin Minski, "The Society of Mind", Simon & Schuster
  • Martin Davis, "Engines of Logic: Mathematicians and the Origin of the Computer", Norton
  • Doxiadis Apostolos and Christos Papadimitriou, "LOGICOMIX: an epic search for truth", Bloomsbury Publishing

Friends and Colleagues