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 .
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 ScienceEcole Polytechnique
Advisor: Dr. Michalis Vazirgiannis, Dr. Fragkiskos Malliaros
August 2016 - May 2018
Masters in Computer ScienceUniversity of Houston
Advisor: Dr. Ioannis Pavlidis
September 2010 - July 2014
Bachelor in Informatics and TelematicsHarokopio University of Athens
Advisor: Dr. Iraklis Varlamis
Research EngineerÉcole Polytechnique
August 2017--May 2018
Teaching AssistantUniversity of Houston
Statistical Methods in Research
May 2017--August 2017
Research InternNCSR 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 AssistantUniversity 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 AssociateNCSR 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.
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 InternNCSR Demokritos
Web crawling and database management for bibliographic data.
Used: Delphi, Java , MySQL
G. Panagopoulos, F. Malliaros and M. Vazirgiannis
IEEE Transactions On Knowledge and Data Engineering (2020) Impact Factor: 4.935
G. Panagopoulos and I. Pavlidis
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
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
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.
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
G. Panagopoulos, C. Xypolopoulos, K. Skianis, C. Giatsidis, J. Tang, M. Vazirgiannis.
International Conference on Complex Networks and Their Applications (Complex Networks), 2019
G. Panagopoulos, F. Malliaros, M. Vazirgiannis
International Conference on Complex Networks and Their Applications (Complex Networks), 2018
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
Knowledge, Information and Creativity Support Systems (KICSS), 2014
A Koukourikos, P Karampiperis, G. Panagopoulos
Cognition and Exploratory Learning in Digital Age (CELDA), 2014
G. Panagopoulos, H. Jalalzai
Graph Neural Networks with Extreme Nodes Discrimination
Deep Learning on Graphs: Methods and Applications, KDD 2020
P. Boniol, G. Panagopoulos, C. Xypolopoulos, R. El Hamdani, D. Restrepo Amariles, M. Vazirgiannis
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
Workshop on Assistive Technologies for Decision making in Healthcare, PETRA 2017
G. Panagopoulos, P. Karampiperis, A. Koukourikos, S. Konstantinidis
Workshop on Deep Content Analytics Techniques for Personalized and Intelligent Services, UMAP 2015
International Conference on Network Science, 2018
- Machine Learning from Stanford
- Machine learning from Mathematical Monk
- Data Science (computer science-oriented) from University of Washington
- Data Science (statistics-oriented)from Johns Hopkins University
- Social and Economic Networks: Models and Analysis from Stanford
- Brain Computer Interfaces from Christian Kothe
- Neural Networks from Huggo Larochelle
- 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