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 Ruiz, 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.

Previous talks
(Available on Youtube)

Abstract: All human and animal behavior from seeing, hearing, running, and falling in love, is the result of complex dynamics in a web of intricate networks in the brain. How the nervous systems’ neurons and synapses are organized into specific network architectures so that neuronal activity and dynamics can give rise to simple and complex behavior is still a mystery. A similar problem exists in the case of artificial neural networks: there is no systematic approach to designing artificial network architectures that generalize well on specific tasks. For artificial and biological networks alike, we are interested in understanding the network building blocks that allow switching or time-dependent dynamics to emerge in ever-changing environments and tasks. We approach this problem from several perspectives: 1) we show how a biologically inspired microcircuit with several specific features (multiple inhibitory cell types, a comparatively smaller neuron population recurrently connected to the network that acts in a switch-like manner, and a disinhibitory network motif) is a minimally complex architecture that can switch between visual processing of the static context and the moving context; 2) we find a fast and flexible artificial network with a biologically-inspired network motif that generalizes across contexts when classifying visual stimuli shown sequentially and set on different background statistics; 3) we devise a method that can potentially identify low-dimensional dynamics and their associated governing equations in realistic noisy, non-autonomous or switching systems, with the goal of applying the method to neural network activity across different organisms and during different behaviors. We plan on further using this method to infer what network mechanisms and architectures may give rise to the discovered parsimonious dynamical system, which underlies otherwise complex and intractable neural dynamics.

Website: https://dorisvoina.com/

Bio: Dr. Ortega holds a Sociology degree from Pontificia Universidad Católica de Chile, a Master degree in Educational Policy (International) from the University of Melbourne, Australia, and PhD in Education from the University of Oxford, United Kingdom. She is an Assistant Professor at Instituto de Estudios Avanzados en Educación and a Researcher at CIAE.

Her research focuses on the application of advanced quantitative methods (multilevel modelling, social network analysis, etc.) to address substantive issues around equity and effectiveness in education. Her teaching has focused on the areas of sociology of education, education policy and quantitative research methods.

Prior to her current position, she worked as an Associate Researcher at Centro de Justicia Educacional UC, as a Postoctoral Researcher at the Department of Sociology of the University of Tübingen, Germany, as an Analyst for the PISA Program at OECD, France, and as a Researcher at the University of Oxford, UK.

Lorena is also a Fellow of the College for Interdisciplinary Educational Research (CIDER), a research network funded by the Jacobs Foundation and the German Ministry of Education. 

Abstract: In the last decade, social network analysis has gained popularity in educational research. Relational approaches have advanced the study of equity and effectiveness issues in complex educational settings. In this presentation, I will review recent applications with focus on the study of students' social integration, teacher-student interactions and teacher collaboration. 

Website: https://ciae.uchile.cl/index.php?page=view_personas_investigadores&case=fichaBio&id=381&langSite=en

Bio: Alice Schwarze is an applied mathematician with interests in networks and complex systems. She received her DPhil (PhD) in mathematics from the University of Oxford in 2019, and has subsequently conducted postdoctoral research at the Department of Biology at the University of Washington (2019-2021) and at the Department of Mathematics at Dartmouth College (2021- present). 

Alice Schwarze is committed to improving diversity, equity, and inclusion in academia and higher education. Since 2020, she has convened the  Women in Network Science seminar to improve the visibility of women researchers in network science and further recognition for their work. In 2021, she was elected president of the Women in Network Science Society. She joined the board of the Network Science Society in 2023.

Abstract: I will discuss why our research community needs Women in Network Science and what our society has achieved in recent years. With the growth of its membership, Women in Network Science has become an international community with a distinct culture that is unique in the academic environment. I will be reviewing how this culture has developed over a wealth of online events that the society has organized between 2021 and 2023, and I will give an outlook on the opportunities and challenges that our society faces as we transition to in-person meetings again. With this look behind the scenes of the Women in Network Science leadership team, I hope to encourage audience members to consider joining our leadership team in the upcoming elections.

Website: aliceschwarze.gitlab.io/

Abstract: The clinical research community has raised the alarm on the importance of studying the role of clearance in Alzheimer’s disease. We heed this alarm by developing and analysing the first network reaction-diffusion dynamical system coupling clearance and proteopathy. Analytical insights, and computational results on high-resolution brain graphs constructed from human data, demonstrate the connections between clearance and neurodegeneration. Our results suggest clearance deficits may play an important role in the onset and trajectory of Alzheimer's.

Bio: I am currently at the University of Oxford on the InFoMM (Industrially Focused Mathematical Modelling) CDT in the final year of my DPhil. I am working in partnership with Simula Research Laboratory and I am supervised by Professor Alain Goriely FRS from Oxford, and Professor Marie E. Rognes from Simula. My research is advancing the understanding of the clearance of toxic proteins in Alzheimer’s disease by developing, and analysing, the first mathematical network models that include specific modes of clearance. I collaborate with mathematicians and clinicians alike who provide state-of-the-art human clearance data.

Bio: D'Souza uses the tools of statistical physics and applied mathematics to develop mathematical models capturing the interplay between the structure and function of networks, including dynamical processes unfolding on networks. Her focus is on the abrupt onset of large-scale connectivity in networks, network synchronization behaviors and models of cascading failure. The general principles derived provide insights into the behaviors of real-world networks such as infrastructure networks and social networks, and opportunities to identify small interventions to control the self-organizing, collective behaviors displayed in these systems. She collaborates broadly with faculty within the college and in physics, statistics, political science and the Primate Center. 

Website: https://mae.engr.ucdavis.edu/dsouza/