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.

Francisca Ortiz Ruiz, Echo Liu, Elena Candellone, Haily Merritt and Gülşah Akçakır 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.

Previous talks
(Available on Youtube)

Scheduled talks - Fall 2024
Mondays - 11 am Eastern Time - on Zoom 

Bio: Anastasia Bizyaeva is an incoming assistant professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University. She is fascinated by the mathematics of collective behavior and distributed and biologically inspired computation, including human and animal social decision-making, pattern formation in networked and spatiotemporal systems, self-organization in swarms, and reservoir computing.

She started her academic journey in Physics with a minor in Mechanical Engineering at the University of California, Berkeley. After that, she joined the Mechanical and Aerospace Engineering department at Princeton University, working with Professor Naomi Ehrich Leonard on nonlinear dynamics of collective decision-making and belief formation. After her successful Ph.D., she embarked her postdoc journey at the AI Institute in Dynamic Systems at the University of Washington, working with Professors Steve Brunton and J. Nathan Kutz.

Abstract: Motivated by the study of complex social behavior, we introduce a nonlinear dynamic model of belief formation dynamics. According to our model, belief updates of individuals are informed by the complex interplay of external factors, i.e. social network effects, and internal factors, i.e. internal biases, networked relationships between an individual’s belief representations, and nonlinear processing of social information. We rigorously show how groups overcome deadlock to form strong beliefs when it is urgent to do so, how the structure of social relationships and of the underlying belief system shapes social decisions in the group, and how group-level beliefs can be highly sensitive to the personal biases of a small number of individuals. This work provides novel insights into the dynamics of complex social systems in nature and society and motivates a new approach for the design of distributed behavior in engineered networks of social agents.

Bio: Emily Laub is an evolutionary biologist studying the evolution of social behavior, social group formation, and cooperation. She uses paper wasps as a model system for studying social behavior and cognition and utilizes both field and laboratory experiments. Emily completed her PhD at the University of Michigan in the Department of Ecology and Evolut and is currentlionary Biologyy a postdoctoral scholar at the University of California Los Angeles.

Abstract: Although much work suggests that complex social environments select for advanced cognitive abilities, thus far there is little empirical evidence demonstrating that social cognition provides an intraspecific fitness benefit. In this project, we investigated how individual face learning, a social cognitive ability, impacts cooperation and fitness in Paper wasps. We conducted two years of experiments in a large naturalistic enclosure where we assessed wasp individual face learning ability, then followed wasps as they formed social groups, interacted on nests, and collected a final estimate of fitness at the end of the season. We find that individual face learning ability is linked with both reproductive success and prosocial behaviors. Our work empirically demonstrates that cognition is linked with fitness and with behavioral mechanisms that could promote fitness. Further, our work suggests that individual recognition can facilitate cooperation in social groups.

Website: https://scholar.google.com/citations?user=AHD3wqIAAAAJ&hl=en 

Bio: Marilyn is a Postdoctoral Research Assistant in the Network Science Institute at Northeastern University London. She has explored a wide range of scientific questions, including healthy aging, frontotemporal dementia, and the effects of transcranial ultrasound stimulation, a novelty non-invasive neuromodulation technique. Her research has integrated data analysis with multivariate information theory, statistics, machine learning, and whole-brain modeling.

Abstract: The brain interdependencies can be studied from either a structural perspective ("structural connectivity") or by considering statistical interdependencies ("functional connectivity" (FC)). Notably, while structural connectivity is inherently pairwise (involving white-matter fibers projecting from one region to another), FC is not limited to pairwise interactions. Despite this, many FC analyses predominantly concentrate on pairwise statistics and often neglect higher-order interactions. In this study, we show the extent of high-order interactions in fMRI data, exploring two applications: healthy aging and transcranial ultrasound stimulation.

Website: https://github.com/KGatica

Bio: Lorena Ortega is Research Director and Associate Professor at Instituto de Estudios Avanzados en Educación, Universidad de Chile. She is a Sociologist and holds a PhD in Education (University of Oxford) and a Master’s degree in Education Policy (University of Melbourne). Her areas of interest are interpersonal relations in education (i.e. teacher collaboration, teacher-student and peer relations and interactions, and research networks) and the application of quantitative methods to investigate equity issues in education.

Abstract: Despite a more favorable representation of women in highly feminized academic fields, such as education, gender disparities manifest themselves across different dimensions of faculty work and unfold over the course of research careers. In this study, we focus on the role of gender in shaping the careers of Chilean education researchers. Using survival, Poisson regression, and social network analyses, we examined gendered patterns of attrition, research output, first authorship, and coauthorship across researchers* trajectories. Bibliometric data were analyzed for the 5,702 authors who published articles in Scopus-indexed education journals between 2011 and 2021 while affiliated with a Chilean institution. Our results show that, despite similar initial representation, being a female researcher increases attrition hazard by 21.5%, with women at the beginning of their research career being particularly at risk of attrition. We also found a significant gender gap in the number of articles published, with Chilean female education researchers publishing, on average, 20.8% fewer articles than male researchers, a disparity that increases in magnitude with years of research experience. However, there were no significant gender differences in research leadership (i.e., first authorships). In relation to collaboration profiles, female researchers showed significantly fewer coauthorships and were more likely to engage in national collaborations when compared to male researchers, who tended to develop more international (both regional and non-regional) collaboration profiles. Furthermore, coauthorship patterns showed significant levels of gender and research experience homophily. Possible explanations pertaining to structural barriers faced by female researchers and policy implications are discussed.

Bio: Elisa Omodei is an Assistant Professor at the Department of Network and Data Science at the Central European University. She holds a BSc and a MSc in Physics from the University of Padua and Bologna, respectively, and a PhD in Applied Mathematics for the Social Sciences from the École Normale Supérieure (ENS) of Paris. She carried out her postdoctoral training at the Rovira and Virgili University in Tarragona, Spain. She then spent over four years at the United Nations, first at UNICEF's Office of Innovation in New York and then at the UN World Food Programme in Rome. In her research, she explores how complexity and data science can help us address the needs of the most vulnerable populations and monitor the UN Sustainable Development Goals. She also served as Vice-President Secretary of the Complex Systems Society from 2018 to 2021.

Abstract: Social media data donation through data download packages (DDPs) is a promising new way of collecting individual-level digital trace data with informed consent. When linked with survey data, data donation is an even more promising tool that helps answer novel research questions. In this talk, I will show how this approach allowed us to investigate polarization measurement biases that arise when only visible traces accessible through platform APIs are considered, while neglecting invisible traces not recorded via online channels, which can reveal key aspects of political engagement online. In the final part of the talk I will give an overview of how we are currently using these data to also study misinformation consumption.

Website: https://elisaomodei.weebly.com/