Foula Vagena
January 19, 4pm
online (zoom)
Abstract
Graph based data science lets us leverage the power of relationships and structure in data to improve model prediction and answer previously intractable questions. In this tutorial we will first introduce the graph as a versatile data representation and summarize the different analytics tasks that can be performed over graph structured data. We will go on to detail the different ML/AI tasks that become possible by leveraging using the graph structure of data and describe recent relevant algorithms and techniques. The tutorial will conclude with a demonstration of exploratory analysis over graph data followed by an illustrative link prediction example.
Dr Foula Vagena
(Université Paris Cité, diiP)
Zografoula Vagena is a research associate at the Data Intelligence Institute of Paris (diiP) and affiliated with the Université Paris Cité. She has been a data science researcher and practitioner for over ten years. She has worked on different analytics problems including forecasting, image processing, graph analytics, multidimensional data analysis, text processing, recommendation systems, sequential data analysis and optimization within various fields such as transportation, healthcare, retail, finance/insurance and accounting. She has also performed research in the intersection of data management and analytics, and was a primary contributor of the MCDB/SimSQL systems that blended data management with Bayesian statistics. She holds a PhD in data management from the University of California, Riverside.
Click the image to see slide
Other seminars
Shen Liang – Knowledge-guided Data Science
Shen LiangMay 18, 4 PM online (zoom) linkedinAbstract This tutorial presents an overview of knowledge-guided data science, a rising methodology in machine learning which fuses data with domain knowledge. We will present numerous case studies on this methodology...
Shen Liang – Deep Transfer Learning and Multi-task Learning
Shen LiangJune 15, 4 PMonline (zoom) linkedinAbstract This tutorial provides an overview of two important and correlated (in many cases intersectional) topics in deep learning: transfer learning, and multi-task learning. Transfer learning focuses on...
Foula Vagena – Deep Learning for Sequential Data: Models and Applications
Foula VagenaApril 13, 4 PMonline (zoom) Abstract Recurrent neural networks (RNNs) are a family of specialized neural networks for processing sequential data. They can scale to much longer sequences than would be practical for networks without sequence-based...
Foula Vagena – Ensemble Learning: Theory and Techniques
Foula VagenaDecember 15, 4pmonline (zoom) Abstract Ensemble learning is the process by which multiple models, such as classifiers or experts, are combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve...