On March 23, 2021, 10:00–3:00 PM Pacific, BIDS GraphXD 2021 convened data science experts from a variety of fields to promote interdisciplinary collaboration and training for researchers, scientists, and theorists interested in using graphs and network analysis for applications across domains. This year’s program featured presentations from distinguished speakers and tutorials focused on NetworkX, the fundamental network analysis tool for creating and manipulating graphs and networks in Python. Presentations and tutorials highlighted this framework’s utility and diversity in a variety of applications. All researchers, scientists, and theorists interested in using graphs and network analysis were welcome and encouraged to attend this event. Watch the BIDS GraphXD 2021 presentation and tutorial videos on YouTube.
This event will be presented on the platform Discord, an open access virtual platform to encourage ongoing participation and engagement across a global community of researchers. Participants can engage with this platform at any time to chat with colleagues and to build research collaborations. First, sign into the BIDS XD Community on Discord. If you’re new to the BIDS XD Community or the Discord platform, we encourage you to take a few minutes to sign in to get acclimated in the days leading up to the event. Then, on March 23, participants may explore the GraphXD “channels” and participate in a variety of ways:
— View the presentations and tutorials in presentations. This broadcast channel can become a pop-out window for wider-screen viewing while participants
— Ask questions and chat with presenters and other participants in q-and-a, and
— Engage in face-to-face discussions with presenters and others in the lounges (dijkstra, edmonds, hopcroft, kosaraju), where most of our speakers will be available to take your questions directly during the social/networking session at 2:00-3:00 PM Pacific.
PROGRAM SUMMARY (Pacific Time).
The FULL PROGRAM includes presentation abstracts and full venue instructions.
The PRESENTATION AND TUTORIAL VIDEOS are now available on YouTube.
10:00 AM — Session 1: Graphs and Network Analysis
- Welcome to BIDS and GraphXD — David Mongeau, BIDS Executive Director
- Exploring network structure, dynamics, and function with NetworkX — K. Jarrod Millman, Graduate Student, Division of Biostatistics, School of Public Health, UC Berkeley
- The Inequality of Intersectionality: An Empirical Example of Using "Small" Networks for Historical Insights — Laura K. Nelson, Assistant Professor of Sociology, Northeastern University
- Network Measures for Complex Contagions — Douglas Guilbeault, Assistant Professor, Management of Organizations, Berkeley Haas School of Business
- Individual-level ecological networks and intraspecific variation — Lauren Ponisio, Assistant Professor, Institute of Ecology and Evolution, Data Science Initiative, University of Oregon
12:00 PM — Session 2: NetworkX Basics Tutorial
- Introduction to Network Analysis with NetworkX — Mridul Seth, Research Software Engineer, GESIS Leibniz Institute for the Social Sciences; and Ross Barnowski, Scientific Software Developer, BIDS
1:00 PM — Session 3: NetworkX Application Tutorial
- Network Analysis of Ancient Sumerian Texts — Adam Anderson, BIDS Research Program Training Manager, Niek Veldhuis, Professor of Assyriology, Department of Near Eastern Studies, UC Berkeley; and the Sumerian Networks Project Data Science Discovery Team
2:00 – 3:00 PM — Session 4: GraphXD Community
- For this social and networking hour, participants are invited to navigate to the Discord lounge areas — dijkstra, edmonds, hopcroft, kosaraju — to engage directly in conversations and discussion with the speakers and other participants. Most of our speakers and presenters will be available during this session. Explore the lounges to find or to initiate a discussion that resonates with your interests and research.
- Adam Anderson, BIDS Research Training Program Manager
- Alexandre de Siqueira, BIDS Researcher
- Marsha Fenner, BIDS Communications/Program Manager
BIDS GraphXD (Graphs Across Domains) is a cross-domain initiative that promotes interdisciplinary collaboration and training for researchers, scientists, and theorists interested in using graphs and network analysis for applications in a variety of fields across STEAM including (but not limited to) anthropology, art, biology, computer science, economics, history, linguistics, mathematics, physics, and sociology. To get more involved in the BIDS XD community online, sign up for the BIDS XD Community on Discord.
Adam G. Anderson advised graduate students in the Computational Social Science Training Program, and helped manage BIDS's cross-domain (XD) initiatives. He was also a lecturer in Digital Humanities and Data Science, and an academic coordinator for Digital Humanities at Berkeley, where he co-authored and designed the Theory and Methods curriculum for the DigHum Minor and Certificate Program. He was also a co-coordinator for the Digital Humanities Working Group (DHWG) and the Computational Text Analysis Working Group (CTAWG), as well as the topic area lead in Network Analysis and Text Analysis at the D-Lab. His work brings together the fields of computational linguistics, archaeology and Assyriology / Sumerology to quantify the social and economic landscapes emerging during the Bronze Age in the ancient Near East. His research interests include network analysis, archival studies, geospatial mapping and language modeling (NLP). He applies these mixed methods to large datasets of ancient texts and archaeological records, in order to better understand the lives of individuals and groups within ancient societies, and to relate these findings within the context of our lives today. He holds a PhD in Near Eastern Languages and Civilizations from Harvard University, an MA (zwischenprüfung) in Assyriology from Ludwig-Maximilians University, and a BA in Linguistics from Brigham Young University.
Alex is a postdoctoral researcher at BIDS, working on open source algorithms for processing computed tomography (CT) 3D images. He received his MS and PhD from the State University of São Paulo, Brazil, applying image processing tools to tackle challenges in materials science and geochronology. A core developer of scikit-image, he is an open source and free software enthusiast since his first contact with Linux, in 2000, contributing to several projects and events in Latin America and Europe. Alex also worked as a postdoctoral fellow at the State University of Campinas, Brazil, and the TU Bergakademie Freiberg, Germany, where he created pytracks and wrote Octave - Your first steps on scientific programming (in Brazilian Portuguese).
Marsha Fenner works to connect researchers, facilitate interdisciplinary collaboration and implement training, education, and outreach programs that enhance and expand BIDS' and Berkeley’s vibrant and diverse data science community. She focuses on developing new programs and events that engage the entire data science community by strengthening partnerships and integrating research efforts among a wide array of disciplines and departments across campus and beyond. Marsha has managed communications, training/education/outreach programs and administrative operations for scientific programs and research initiatives at UC Berkeley and Lawrence Berkeley National Laboratory, including the Innovative Genomics Institute, the DOE Joint Genome Institute and the LBL Advanced Light Source. She holds an MA in philosophy and comparative religious studies, and a BA in classics, philosophy and mathematics.
David Mongeau is the Executive Director of BIDS. With the Director and Faculty Council, he sets strategic direction and oversees the institute’s research, training, and outreach. David also leads the institute’s industry and foundation relations and its engagement with other UC and global research institutes – all toward the overarching mission at BIDS to create and deploy data science methods, practices, and technologies to enable discovery.
Previously, David co-led the data analytics institute at Ohio State; worked at Battelle, where he championed its proposal for an AI and cybersecurity company, now Covail; and worked for many years at Bell Labs – starting on the team that introduced the first C++ compiler and UNIX System V and leaving after building a global business and technology consulting practice, now part of Nokia Bell Labs Consulting.
David earned his undergraduate degree at Carnegie Mellon University, and later earned a graduate degree at Rensselaer Polytechnic Institute and an MBA from Purdue University. Many of his interests lie beyond data science, embracing the humanities and arts.
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.
Former BIDS Data Science Fellow Laura K. Nelson is an Assistant Professor of Sociology in the College of Social Sciences and Humanities at Northeastern University. Laura uses computational methods and open source tools - principally automated text analysis - to study social movements, culture, gender, institutions, and organizations. She is particularly interested in developing computational tools that can bolster the way social scientists do inductive and theory-driven research. She received her PhD in sociology from the University of California, Berkeley, and she also holds an MA from UC Berkeley and a BA from the University of Wisconsin, Madison. While at UC Berkeley, she was a postdoctoral fellow with Digital Humanities @ Berkeley, developing a course for undergraduates on computational text analysis in the humanities and social sciences.
Douglas Guilbeault is an Assistant Professor in the Management of Organizations Group at the Haas School of Business. He studies how people learn, challenge, develop, and invent categories by communicating in social networks. This investigation extends to the analysis of how organizations mediate and augment social computation by enabling new forms of communication, coordination, and creativity. This investigation further extends into how the social construction of meaning can be shaped by various sources of influence, such as political messaging and the design of social media platforms. His work on these topics has appeared in a number of journals, including Nature Communications, The Proceedings of the National Academy of the Sciences, Cognition, Policy and Internet, and The Journal of International Affairs, as well as in popular news outlets, such as The Atlantic and Wired. Guilbeault’s work has received top research awards from The International Conference on Computational Social Science, The Cognitive Science Society, and The International Communication Association. In addition, he was a recipient of Stanford’s “The Art of Science” award for the piece “Changing Views in Data Science over 50 Years” produced in collaboration with the research collective, comp-syn. Guilbeault teaches People Analytics at Haas, focusing on how organizations are using novel algorithmic methods to address (and sometimes inadvertently create) fundamental problems in management.
Lauren Ponisio was a postdoctoral scholar in the Environmental Science, Policy, and Management department and a BIDS Data Science Fellow. She is currently an Assistant Professor at UC Riverside, where the Ponisio Lab studies the mechanisms operating in complex ecological communities in order to restore biodiversity in degraded ecosystems.
As a native of the Central Valley, Lauren has a personal connection to issues concerning the sustainability of agriculture, and a primary goal of my research is to make agricultural systems better for humans and wildlife. She thus also investigate strategies for designing agricultural systems to promote biodiversity conservation and the links between conservation strategies and improving livelihoods.
Lastly, she is working on the development team of NIMBLE, a system for building and sharing analysis methods for statistical models, especially for hierarchical models and computationally intensive methods. Statistics is the primary way scientists identify patterns within their data, and thus, advances in fields applying statistics are often facilitated by computational methods.
Ross Barnowski is a postdoctoral scholar working towards the development of open, interactive tools for learning and teaching NumPy. Ross has been involved in open source software for reproducible, collaborative science since 2015, primarily in his role as an educator. He holds a PhD in nuclear engineering from UC Berkeley and previously served as an assistant research scientist and lecturer in the department of nuclear engineering, teaching the graduate-level lab in nuclear instrumentation. His post-graduate research focused on systems integration and software for 3D gamma-ray imaging systems including high-resolution instruments for applications in nuclear security and molecular imaging. He has also worked on hand-portable radiation imaging systems for real-time 3D radiation mapping with applications in environmental monitoring and nuclear contamination remediation. These systems have been used to investigate gamma-ray imaging for environmental monitoring in real-world scenarios, including the area around the Fukushima Daiichi nuclear power plant as well as Chernobyl, Ukraine.
Niek Veldhuis is Professor of Assyriology (cuneiform studies) in the Department of Near Eastern Studies. He received his PhD at the Rijksuniversiteit Groningen (The Netherlands) in 1997, and came to Berkeley in 2002. His primary interests are in the intellectual history of ancient Mesopotamia (History of the Mesopotamian Lexical Tradition, 2014) and Sumerian literature (Religion, Literature and Scholarship: The Sumerian Composition Nanše and the Birds, 2004). He is director of the NEH-supported Digital Corpus of Cuneiform Lexical Texts and is a member of the international Oracc Steering Committee, providing tools and standards for digital publication of cuneiform texts to scholars worldwide. Today, his main research focus is on developing computational text analysis scripts (primarily in Jupyter Notebooks) for cuneiform datasets.