2018 GraphXD Workshop

Graphs Across Domains


March 27, 2018 to March 29, 2018
9:00am to 5:00pm
190 Doe Library
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BIDS hosted the first annual Graphs Across Domains (GraphXD) Workshop on March 27–29, 2018.

Introduction: Graphs arise in many fields. A sociologist interested in social interactions in a community may record their observations in a graph of individuals connected by friendship. A geneticist interested in how genes are co-expressed may represent their data as a graph of genes with edges connecting genes that are expressed together. An online airline purchasing system may use a graph of airports connected by edges with direct flights between them.

Progress in understanding graphs and developing new graph algorithms in a number of diverse fields is hindered by the fact that researchers who use them typically don’t have the opportunity to communicate with others who work on similar problems in different domains.

Graphs and Complex Networks Across Domains (GraphXD) connects scientists, researchers, and theorists interested in graphs from a variety of fields through seminars and workshops.  This is the inaugural workshop.

Agenda / Program (pdf)

Videos / Abstracts / Slides

March 27, 2018 Links to presentation videos are included below.
9:00–9:30    Breakfast
9:30–10:00    Jarrod Millman, Graphs and complex networks across domains
10:00–10:30    Lauren Ponisio, Understanding the ecology and evolution of communities through networks, part I
10:30–11:00    Marília Gaiarsa, Understanding the ecology and evolution of communities through networks, part II
11:00–11:30    Tea
11:30–12:00    Nick Ryder, A history of spectral graph theory and its applications, part I
12:00–12:30    Aaron Schild, A history of spectral graph theory and its applications, part II
12:30–2:00    Lunch
2:00–3:00    Aric Hagberg, Exploring network structure, dynamics, and function using NetworkX
3:00–3:30    Aydin Buluc, Graph abstractions in computational genomics
3:30–4:00    Tea
4:00–4:30    Katelyn Arnemann, Challenges for graph theory in human neuroscience
4:30–5:00    Kimon Fountoulakis, Variational Perspective on Local Graph Clustering
5:00–7:00    BBQ

March 28, 2018
9:00–9:30    Breakfast
9:30–10:00    Camille Scott, Sequence Assembly Graphs and their Construction
10:00–11:00    Rasmus Kyng, How to Solve Problems on Graphs Using Linear Equations, and How to Solve Linear Equations Using Graphs
11:00–11:30    Tea
11:30–12:00    Ludwig Schmidt, Linear Regression with Graph Constraints
12:00–12:30    Discussion
12:30–2:00    Lunch
2:00–3:00    Planning
3:00–5:00    Self-organized activities

March 29, 2018
9:00–9:30    Breakfast
9:30–12:30    Self-organized activities
12:30–1:30    Lunch
1:30–3:00    Self-organized activities
3:00–4:00    Discussion
3:30–5:00    Reflection

K. Jarrod Millman (Biostatistics, UC Berkeley)
Stéfan van der Walt (Berkeley Institute of Data Science, UC Berkeley)
Nelle Varoquaux (Statistics, UC Berkeley)

For more information about this workshop, read this blog post from organizer Jarrod Millman:
BIDS hosts the inaugural GraphXD workshop
April 13, 2018 |  Jarrod Millman |  BIDS Blog: Data Science Insights


Jarrod Millman

BIDS Alum – Data Science Fellow

Jarrod Millman, a former BIDS Data Science Fellow, is a PhD student in biostatistics  at UC Berkeley.  His research interests include algorithms, scientific computing, and neuroscience.

Stéfan van der Walt

Senior Research Data Scientist

Stéfan van der Walt is a researcher at BIDS, where he leads the Software Working Group. He is the founder of scikit-image and co-author of Elegant SciPy.  Stéfan has been developing scientific open source software for more than a decade, focusing mainly on Python packages such as NumPy & SciPy. Outside work, he enjoys traveling, running, and the great outdoors.

Nelle Varoquaux

BIDS Alum - Data Science Fellow

Nelle Varoquaux was a BIDS Data Science Fellow in the Department of Statistics at UC Berkeley. She received a PhD in computational biology from École des Mines de Paris in 2015. Her research interests include statistical machine learning and scientific computing applied to molecular biology problems, such as inferring the 3D architecture of the genome or data-integration methods to better understand gene regulatory networks.