The Women in Network Science (WiNS) seminar is an interdisciplinary seminar with the aim to promote and showcase research by women and nonbinary researchers in network science. 

The seminar is open to everyone. Please join the mailing list to receive announcements and zoom links for upcoming seminar talks.

Alice Schwarze, Francisca Ortiz, Echo Liu, and Mari Kawakatsu convene this seminar series. Please get in touch if you are interested in presenting in the seminar or if you would like to nominate speakers.

For all scheduled talks, relevant preprints are available on our ZeroDivZero repository. Recordings of past talks can also be found in the ZeroDivZero repository and on Youtube.

Spring 2022 PROGRAM (Dartmouth College)

Bio: Heather A. Harrington (born 1984) is an applied mathematician interested in dynamical systems, chemical reaction network theory, topological data analysis, and systems biology. She is professor of mathematics, and Royal Society University Research Fellow at the Mathematical Institute, University of Oxford, where she heads the Algebraic Systems Biology group.

Heather Harrington went to Concord-Carlisle High School in Massachusetts. As an applied mathematics student at the University of Massachusetts Amherst she won a Barry M. Goldwater Scholarship, and graduated summa cum laude from in 2006. She completed her Ph.D. in 2010 at Imperial College London. Her dissertation, Mathematical models of cellular decisions, was jointly supervised by Jaroslav Stark and Dorothy Buck. After postdoctoral research in theoretical systems biology at Imperial from 2010 to 2013, she joined the Mathematical Institute at Oxford as Hooke Research Fellow and EPSRC Postdoctoral Research Fellow, and as Junior Research Fellow at St Cross College, Oxford. In 2017, she became an associate professor and Royal Society University Research Fellow at Oxford. In 2020, she became professor of mathematics.

In 2018, Heather Harrington was one of the winners of the Whitehead Prize of the London Mathematical Society. She was a co-winner of the 2019 Adams Prize of the University of Cambridge, which had the topic 'The Mathematics of Networks'. She was awarded the Philip Leverhulme Prize in 2020 for advances in analysis of noisy data.

Abstract: TBA

Website: http://people.maths.ox.ac.uk/harrington/

Bio: Elisa Bellotti is senior lecturer in sociology and member of the Mitchell Centre for Social Network Analysis at the University of Manchester. She has published extensively on applications of social network analysis and mixed methods in substantive sociological fields such as criminal networks, scientific networks, and personal networks. Her recent work focuses on gender aspects of social network formations and outcomes, and health networks. She is the author of Qualitative Networks. Mixed Methods in Sociological Research (Routledge, 2015) and co‐author of Social Network Analysis for Egonets (Sage, 2015).

Abstract: TBA

Website: https://www.research.manchester.ac.uk/portal/elisa.bellotti.html

Bio: Dr Francisca Ortiz Ruiz is a postdoctoral researcher at the Millennium Institute for Care Research, based in Santiago, Chile. She is a Sociologist (Universidad Alberto Hurtado) with a Master’s degree in Sociology (Pontificia Universidad Católica de Chile) and a PhD in Sociology at the University of Manchester, UK. Her research has focused on social support and care networks of the ageing population, social gerontology, mixed methods, social network analysis, and gender inequalities in science. Her work has been published in more than 40 papers, book chapters and other documents, which have been presented at national and international conferences. She is co-editor of two books: “Redes sociales: teoría, métodos y aplicaciones en América Latina” (2022) (“Social Networks: theory, methods and applications in Latinamerica”) and “Tecnopolíticas: aproximaciones a los estudios de ciencia, tecnología y sociedad en Chile” (2018) (“Techno politics: studies of science, technology and society in Chile"). She collaborates in creating open-source R packages, and other outreach initiatives, like Knitting Networks Podcast, Cuidando Podcast and REDES colloquiums. She is part of the editorial committee of REDES journal, AWARI, and Contenido Journal. Currently, she is a member of the “Women in Network Sciences” (WiNS Global network) council and founder/coordinator of the feminist network of social sciences in Chile. 

Abstract: This research seeks to understand care and support networks of older people in Santiago de Chile. More specifically, this study will focus on identifying and describing these networks with an emphasis on gender and age inequalities. We measure the nature of those relationships (kindship, acquaintances, friends, among other categories) to evaluate the circulation of care through them, and how relevant they are. We conduct fieldwork in Santiago (Chile) to collect a diversity of data through documents, surveys, interviews, and a name generator of the care and support networks. In this presentation, I will share two main aspects of the research: (1) the theoretical approach that shows the difference between the concepts of care and social support from a networks perspective and (2) the methodological approach for data collection and analysis. Finally, I will share a first look to the data collected from fieldwork. 

Website: https://sites.google.com/view/franciscaortizruiz/home 

Bio: Julie Digne is a CNRS researcher at LIRIS, University Lyon 1, Lyon, France. She received her master's degree in applied mathematics from ENS Paris-Saclay and her Ph.D. in 2010 from the same institution (for which she received a Ph.D. award from the Fondation Hadamard). In 2018 she defended her Habilitation at University Lyon 1. In 2011 and 2012, she was a post-doc at INRIA Sophia Antipolis in the Geometrica team working with Pierre Alliez and Mathieu Desbrun from Caltech. Her main research interests are geometry processing and shape analysis, mainly when the surfaces are represented as point clouds. These include surface denoising, meshing, scan merging, and surface segmentation, building either from surface PDEs or building point set dedicated machine learning techniques. Her research results are published in leading conferences and journals in computer graphics and applied mathematics. She serves as a member of several international program committees (Siggraph, Siggraph Asia, EG, SGP), and as an associate editor of Computer Graphics Forum and Computers & Graphics. In 2021 she was co-chair of the technical paper program of SGP 2021. 

Abstract: 3D shapes as they are acquired by laser scanner often are given as a set of points which can be noisy or have a limited resolution and missing data. In this talk I’ll explain a series of work leveraging the self-similarity of surfaces to overcome these issues, with tools ranging from nearest neighbor search to sparse optimization. By considering angular oscillations around each point of the surface, one can deduce important properties of the surface, to improve its quality.

Website: https://perso.liris.cnrs.fr/julie.digne/

Bio: My work focuses on quantitative understanding of social systems based on massive data sets. I am a physicist by training, but also well-read in the social science literature. I develop and apply methods from the physics of complex systems, machine learning, and statistical analysis to social systems. In the scientific community I am renown for studies about social complex systems, and about theory and applications of network science. I have pioneered breakthrough publications in top-tier journals like Nature, Science, PNAS, Nature Physics, which have received extensive attention from the scientific community. I am also routinely invited to the leading conferences of Data Science, Network Science, and Computational Social Science.

Abstract: Through "Pathways in Network Science", we aim to shed light on the exciting, diverse, and sometimes challenging career paths of women and nonbinary researchers in network science. In today's seminar, Roberta Sinatra (Associate Professor at ITU Copenhagen) reflects on her path that led her to and through network science.

Website: https://www.robertasinatra.com/

Bio: Trained as an economist and gradually morphed into a sociologist, Paola Tubaro is a member of Centre de Recherche en Economie et Statistique (CREST) in the Greater Paris area. She uses social network science to shed new light on the contemporary transformations of markets and organizations. Her current research focuses on the digital platform economy, the global production networks of the artificial intelligence industry, the role of human labor in the development of automation, and digital inequalities. She is also interested in the ethics of data and artificial intelligence.

From January 2016 until August 2022, she was at a major computer science lab, Laboratoire Interdisciplinaire des Sciences du Numérique, also in the Paris area, where she built bridges between the social sciences and the latest developments in machine learning and artificial intelligence. Before that (December 2009 – December 2015),sheI was at the Faculty of Business of the University of Greenwich, London. She was a Reader (in the British traditional academic career ladder, the equivalent of a tenured Associate Professor) in economic sociology, and the programme leader of the doctoral programme of the faculty of business and economics.

In Britain, she is also a Senior Fellow of the Higher Education Academy, the co-convenor of the Social Networks Analysis Group of British Sociological Association, and a member of the Editorial Board of the journal Sociology. In Italy, she am visiting professor at the department of economics of the University of Insubria, where I teach a Research Design course to first-year PhD students.

Abstract: Through "Pathways in Network Science", we aim to shed light on the exciting, diverse, and sometimes challenging career paths of women and nonbinary researchers in network science. In today's seminar, Paola Tubaro (Research Professor at CRNS) reflects on her path that led her to and through network science.

Website: https://databigandsmall.com/