About
Education and Work Experience
I am a postdoctoral research scientist on machine learning at the University of Luxembourg , under the supervision of Prof. Jun Pang. I've spent one year working on operations research with Dr. Nikolaos Liakopoulos in the Algorithms and Optimization lab of Amazon Transportation Services. I received my Ph.D. in machine learning for graphs from the Data Science and Mining Team of the Computer Science Laboratory of École Polytechnique, supervised by Prof. Michalis Vazirgiannis and Assistant Prof. Fragkiskos Malliaros . Previously, I was a teaching and research assistant at the 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 relational machine learning, causal inference, and information retrieval. More specifically I am researching machine learning methods for causal inference in large-scale heterogeneous data coming from biomedical or commercial settings. I am also exploring active and online learning methods for experimentation in clinical trials and e-commerce applications. In the recent past, I have worked on combinatorial optimization, forecasting, classification, and epidemic-spreading problems. In terms of applications, I have worked with a variety of data like social, logistics, epidemic, molecular, product, protein, coauthorship, word, and brain graphs. Finally, I am also experienced with NLP in long/short texts and digital signal processing for statistical inference from wearable sensors or neural signals.
Resume
Education
January 2019 - March 2022
PhD in Computer Science
Ecole PolytechniqueAdvisor: Dr. Michalis Vazirgiannis, Dr. Fragkiskos Malliaros
August 2016 - May 2018
Masters in Computer Science
University of HoustonAdvisor: Dr. Ioannis Pavlidis
September 2010 - July 2014
Bachelor in Informatics and Telematics
Harokopio University of AthensAdvisor: Dr. Iraklis Varlamis
Experience
January 2024--Now
Postdoctoral Scientist
University of LuxembourgDescember 2022--June 2023
Applied Scientist
AmazonApril 2022--October 2022
Applied Scientist Intern
AmazonJune 2018--March 2022
Research Engineer
École PolytechniqueAugust 2017--May 2018
Teaching Assistant
University of HoustonStatistical Methods in Research
Software Engineering
August 2016 - May 2017
Research Assistant
September 2014 - July 2016
Research Associate
NCSR Demokritos, Software Knowledge and Engineering LabJune 2013 - September 2013
Software Engineering Intern
NCSR DemokritosResearch
Journals
C. Kosma, G. Nikolentzos, G. Panagopoulos, J.M. Steyaert, M. Vazirgiannis
Neural Ordinary Differential Equations for Modeling Epidemic Spreading
Transactions on Machine Learning Research,2023
Short Description: A graph neural ordinary differential equation model to predict the Susceptible-Infectious-Recovered epidemic model. [pdf] [code]
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]
Conferences
G. Panagopoulos, D. Malitesta, F. D Malliaros, J. Pang
Uplift Modeling Under Limited Supervision
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), 2024
Short Description: A framework based on GNNs and active learning for causal effect prediction on bipartite networks, indicating their potential to diminish the required demand for training labels [pdf] [code]
G. Panagopoulos, N. Tziortziotis, F. Malliaros, M. Vazirgiannis
Maximizing Influence with Graph Neural Networks
International Conference on Social Networks Analysis and Mining (ASONAM),2023
Short Description: A graph neural network to predict the Independent Cascade model and a submodular algorithm based on its representations to perform influence maximization. [pdf] [code]
B. Rozemberczki, P. Scherer, Y. He, G. Panagopoulos, M. Astefanoaei, O. Kiss, F. Beres, N. Collignon, R. Sarkar
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models Best Resource Paper
Conference on Information and Knowledge Management (CIKM resource track),2021
Short Description: A library with temporal graph neural network methods and temporal graph datasets. [pdf] [code]
G Nikolentzos, G Panagopoulos, M Vazirgiannis.
An Empirical Study of the Expressiveness of Graph Kernels and Graph Neural Networks
International Conference on Artificial Neural Networks (ICANN),2021
Short Description: An experimental study to quantify the accuracy of graph kernels and graph neural networks for approximating graph similarity using a well-known similarity function for evaluation. [pdf]
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 nomination
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]
Preprints
R. Bresson, G. Nikolentzos, G. Panagopoulos, M. Chatzianastasis, J. Pang, M. Vazirgiannis
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
2024
Short Description: An extenssive experimental study to quantify the effect of substituting MLPs with KANs for several graph learning tasks [pdf] [code]
Workshops
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
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]
Posters
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]
Resources
Courses
- 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
Textbooks
- 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
Books
- 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