Julia Olivieri
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PhD candidate
Stanford University, ICME |
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Julia is a third-year PhD student in ICME (the Institute of Computational and Mathematical Engineering) at Stanford. I graduated from Oberlin College in 2016 with majors in Mathematics and Biology. Julia is currently working with ProfessorJulia Salzman to develop robust statistical tools for analyzing alternative splicing from single cell RNA sequencing data. With Roozbeh Dehghannasiri, I have developed the SICILIAN pipeline to filter out low quality spliced alignments and allow for the discovery of high-confidence unannotated splicing, and developed the Splicing Z Score (SZS) method to analyze alternative splicing differences even from droplet-based RNA-seq data.
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Graph theory provides a way to study relationships between data points, and is applied to everything from deep learning models to social networks. Over this three-workshop series we will progress from introductory explanations of what a graph is, through the most common algorithms performed on graphs, and end with an investigation of the attributes of large-scale graphs using real data.
Workshop: Graph Theory for Data Science, Part III: Characterizing graphs in the real world
August 25, 2021; 9:30-10:15 am PST
August 25, 2021; 9:30-10:15 am PST
Many of the systems we study today can be represented as graphs, from social media networks to phylogenetic trees to airplane flight paths. In this workshop we will explore real-world examples of graphs, discussing how to extract graphs from real data, data structures for storing graphs, and measures to characterize graphs. We will work with real examples of graph data to create a table of values that summarize different example graphs, exploring values such as the centrality, assortativity, and diameter of each graph. Python code will be provided so that attendees can get hands-on experience analyzing graph data.
Workshop: Graph Theory for Data Science, Part II: Graph Algorithms: Traversing the tree and beyond
June 30, 2021; 9:30-10:15 am PST
June 30, 2021; 9:30-10:15 am PST
Graph-based algorithms are essential for everything from tracking relationships in social networks to finding the shortest driving distance on Google Maps. In this workshop we will explore some of the most useful graph algorithms, from both the breadth-first and depth-first methods for searching graphs, to Kruskal’s algorithm for finding a minimum spanning tree of a weighted graph, to approximation methods for solving the traveling salesman problem. We will use hands-on examples in python to explore the computational complexity and accuracy of these algorithms, and discuss their broader applications.
Workshop: Graph Theory for Data Science, Part I: What is a graph and What Can We Do With It?
May 26, 2021; 9:30-10:15 am PST
May 26, 2021; 9:30-10:15 am PST
Graphs are structures that represent pairwise connections, and are used for everything from finding the shortest route between two locations to google’s page rank algorithm. Are you interested in learning about graph theory but don’t know where to start? In this workshop we will introduce graphs, develop comfort with their associated terminology, and investigate real-world applications with a focus on intuitive explanations and examples.
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