PROGRAM SCHEDULE

TUTORIALS

 

Detecting and Visualizing Synchronisation in Complex Bipartite Data

Organized by Firas Blibeche

Programming Language Used: Python
Expected Preparation: Participants should have a basic understanding of Python and statistical analysis.
Learning Outcomes: Attendees will learn how to filter the links in the projection of a bipartite graph, in order to keep only those being statistically significant. They will see how this method can be applied to detect and visualize meanigful synchronisation in complex data, in particular for time series analysis. 

Dataset Used, if any: Financial data (S&P500, Spanish investors). Maybe other datasets depending on the advances of my own research.
Duration: 1 hour

Other Relevant Info, if any: Bring your laptop! 

 

Machine learning with unbalance datasets

Organized by Andrea Lo Sasso

Programming Language Used, if any: Python
Expected Preparation: Participants should have a basic understanding
of basic machine learning and statistics.
Learning Outcomes: Attendees will learn how to manage unbalance
dataset using Python and make machine learning prediction with dedicated
algorithms.
Dataset Used, if any: Iris dataset.
Duration: 1 hour
Other Relevant Info, if any: Bring your laptop! 

 

Reproducing emergent phenomena with Neural Cellular Automata

Organized by Elias Najarro
 
Programming Language: Python
Expected Preparation: Participants should have a basic understanding of Python.
Learning Outcomes: Attendees will learn how to train a Neural Cellular Automata in pytorch to reproduce a given macroscopic pattern.
Duration: 1 hour: First 30min introduction to the Neural Cellular Automata and Pytorch. Second part: hands-on tutorial with following a Jupiter notebook.
Other Relevant Info: Bring your laptop and have a face portrait of someone (could be yourself!) in your laptop.
 

Introduction to Agent-based Modeling with NetLogo

Organized by Gülşah Akçakır

Programming Language Used, if any: NetLogo
Expected Preparation: There are no hard prerequisites other than basic understanding of logic and programming in general.

Learning Outcomes: Attendees will learn the fundamentals of the software including agent types (e.g., turtles, patches), main built-in commands, defining procedures from scratch and creating plots/monitors (time permitting).
Dataset Used, if any: TBD
Duration: 1 hour Other Relevant Info, if any: Bring your laptop and download NetLogo at https://ccl.northwestern.edu/netlogo/.

 

Network Visualization and Analysis of the Public Transportation Network of Munich

Organized by Islam Elgamal

Programming Language and Library Used: Python – NetworkX.
Expected Preparation: Participants should have a basic understanding of Python and data structures.
Learning Outcomes: Attendees will learn how to construct and analyze a network given a database of connections. The tools used will help identify key metrics such as central nodes, communities, and network topology.
Dataset Used, if any: Public transportation network of Munich
Duration: 1 hourOther Relevant Info, if any: Bring a laptop with your favorite IDE on it running Python!

 

Graph Neural Networks: The essentials

Organized by Darshan Pandit 

Programming Language Used, if any: Python
Expected Preparation: –
Learning Outcomes: Attendees will gain a thorough understanding of Graph Neural Networks, get introduced to some prominent architectures like GCN, GraphSage, and GAT; and practical applications in complex systems like transportation and community detection.
Dataset Used, if any: Zachary’s Karate Club
Duration: 30 minutes