Events
Past Event
WED@NICO WEBINAR: Tina Eliassi-Rad, Northeastern University
Northwestern Institute on Complex Systems (NICO)
12:00 PM
Details
Speaker:
Tina Eliassi-Rad, Professor, Khoury College of Computer Sciences, Northeastern University
Title:
Geometric and Topological Graph Analysis for Machine Learning
Abstract:
This talk has two parts: (1) geometric analysis for graph embedding and (2) topological analysis for graph distances. First, graph embedding seeks to build an accurate low-dimensional representation of a graph. This low-dimensional representation is then used for various downstream tasks such as link prediction. One popular approach is Laplacian Eigenmaps, which constructs a graph embedding based on the spectral properties of the Laplacian matrix of a graph. The intuition behind it, and many other embedding techniques, is that the embedding of a graph must respect node similarity: similar nodes must have embeddings that are close to one another. We dispose of this distance-minimization assumption. In its place, we use the Laplacian matrix to find an embedding with geometric properties (instead of spectral ones) by leveraging the simplex geometry of the graph. We introduce Geometric Laplacian Eigenmap Embedding (or GLEE for short) and demonstrate that it outperforms various other techniques (including Laplacian Eigenmaps) in the tasks of graph reconstruction and link prediction. This work is joint with Leo Torres and Kevin Chan, and was published in the Journal of Complex Networks in March 2020. Second, measuring graph distance is a fundamental task in graph mining. For graph distance, determining the structural dissimilarity between networks is an ill-defined problem, as there is no canonical way to compare two networks. Indeed, many of the existing approaches for network comparison differ in their heuristics, efficiency, interpretability, and theoretical soundness. Thus, having a notion of distance that is built on theoretically robust first principles and that is interpretable with respect to features ubiquitous in complex networks would allow for a meaningful comparison between different networks. We rely on the theory of the length spectrum function from algebraic topology, and its relationship to the non-backtracking cycles of a graph, in order to introduce the Non-Backtracking Spectral Distance (NBD) for measuring the distance between undirected, unweighted graphs. NBD is interpretable in terms of features of complex networks such as presence of hubs and triangles. We showcase the ability of NBD to discriminate between networks in both real and synthetic data sets. This work is joint with Leo Torres and Pablo Suarez-Serrato, and was published in the Journal of Applied Network Science in June 2019.
Speaker Bio:
Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University in Boston, MA. She is also a core faculty member at Northeastern's Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is at the intersection data mining, machine learning, and network science. She has over 100 peer-reviewed publications (including a few best paper and best paper runner-up awardees); and has given over 200 invited talks and 14 tutorials. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, and ethics in machine learning. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2017, Tina served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (a.k.a. KDD, which is the premier conference on data mining) and as the program co-chair for the International Conference on Network Science (a.k.a. NetSci, which is the premier conference on network science). In 2020, she served as the program co-chair for the International Conference on Computational Social Science (a.k.a. IC2S2, which is the premier conference on computational social science). Tina received an Outstanding Mentor Award from the Office of Science at the US Department of Energy in 2010; became a Fellow of the ISI Foundation in Turin Italy in 2019; and was named one of the 100 Brilliant Women in AI Ethics for 2021.
Webinar:
Webinar link: https://northwestern.zoom.us/j/95878198317
Passcode: nico
ID: 958 7819 8317
About the Speaker Series:
Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems and data science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.
Time
Wednesday, February 3, 2021 at 12:00 PM - 1:00 PM
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
WED@NICO SEMINAR: Ágnes Horvát, Northwestern University "Science on the Web: How networks bias academic communication online"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level, Chambers Hall
Details
Speaker:
Ágnes Horvát - Assistant Professor, Communication Studies, School of Communication, Northwestern University
Title:
Science on the Web: How networks bias academic communication online
Abstract:
Most academics are promoting their work online. At the same time, the public, journalists, and interested governments increasingly turn to the Web for scientific information. It thus becomes ever more critical that we better understand the dynamics of online science dissemination networks. My talk presents our latest results about (1) how scientific publications spread on various types of online platforms, losing essential information; (2) how gender and ethnic inequalities impact the coverage of scholarship; and (3) how subsequently retracted articles receive more attention online. Our findings highlight crucial biases in the online sharing of science. They inform efforts to close gaps in scholars' success and curb the online spread of science-related misinformation.
Speaker Bio:
Ágnes Horvát is an Assistant Professor in Communication and Computer Science (by courtesy) at Northwestern University, where she directs the Technology and Social Behavior PhD program. Her research lies at the intersection of computational social science, social computing, and communication. Using interdisciplinary approaches from network and data science, her research group, the Lab on Innovation, Networks, and Knowledge (LINK), investigates how networks induce biased information production, sharing, and processing on digital platforms. For example, they study the impact of networks and diversity on scholarly communication, identify expressions of collective intelligence and opportunities for innovation in crowdsourcing communities, and develop tools to support creativity and predict success in culture industries. Professor Horvát received her PhD in Physics from the University of Heidelberg, Germany.
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/95881985279
Passcode: NICO23
About the Speaker Series:
Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems and data science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.
Time
Wednesday, February 8, 2023 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
WED@NICO SEMINAR: Yong-Yeol "YY" Ahn, Indiana University Bloomington "Science of science, law of law, and patterns of patents: universal citation dynamics in knowledge systems"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level, Chambers Hall
Details
Speaker:
Yong-Yeol "YY" Ahn, Associate Professor, Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington
Title:
Science of science, law of law, and patterns of patents: universal citation dynamics in knowledge systems
Abstract:
Citation is a fundamental way for humans to acquire and expand on existing knowledge. Although many laws and regularities of citation dynamics have been discovered from scientific citations, it is unclear whether and to what extent these regularities are inherent in how humans seek, use, and create knowledge. We show that, despite many stark differences between these systems, the citation dynamics in science, law, and patents share universal patterns. Given the differences in procedure and incentives that exist between judges, inventors, and scientists, our findings suggest that universal citation dynamics may be innate to any cumulative human knowledge system. Our model demonstrates that the evolution of collective attention and a handful of fundamental mechanisms can produce observed universal patterns of citation dynamics.
Speaker Bio:
Yong-Yeol (YY) Ahn is an Associate Professor at Indiana University School of Informatics, Computing, and Engineering. He was a Visiting Professor at MIT during 2020-2021. Before joining Indiana University, he worked as a postdoctoral research associate at the Center for Complex Network Research at Northeastern University and as a visiting researcher at the Center for Cancer Systems Biology at Dana-Farber Cancer Institute after earning his PhD in Statistical Physics from KAIST in 2008. His research focuses on developing network science and machine learning methods, and applying them to complex social and biological systems. He is a recipient of several awards including Microsoft Research Faculty Fellowship and LinkedIn Economic Graph Challenge.
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/93818374439
Passcode: NICO23
About the Speaker Series:
Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems and data science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.
Time
Wednesday, February 15, 2023 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
WED@NICO SEMINAR: Chris Kuzawa, Northwestern University "Fetal developmental plasticity as signal-noise problem: the case of nutrients and stress physiology"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
//
Lower Level, Chambers Hall
Details
Speaker:
Chris Kuzawa, John D. MacArthur Professor, Department of Anthroplogy, Northwestern University
Title:
Fetal developmental plasticity as signal-noise problem: the case of nutrients and stress physiology
Abstract:
The stage of human development marked by greatest sensitivity and developmental plasticity occurs prior to birth, when the developing embryo and fetus are embedded within the highly regulated intrauterine environment maintained by the mother’s body. In this talk, I will discuss the timescales of maternal experience that modify this milieu, with a focus on two crucial systems: nutrient metabolism and stress physiology. I will argue that elaborate and redundant maternal buffering of nutrient delivery uncouples short term variability in what the mother eats – whether negative changes like famine or improvements in the form of nutritional supplementation - from fetal nutrition and development, which instead track changes in maternal nutrition on a longer, generational timescale. In contrast, stress physiology is by design highly responsive to the mother’s short-term experiences and the responsiveness of these systems have broad spill-over effects on fetal development, thus linking offspring biological and health outcomes to acute variability in maternal stress during pregnancy. I will discuss the relevance of these principles for understanding the evolution of the flow of ecological information across generations and the design of interventions aimed at harnessing early life plasticity to improve future population health.
Speaker Bio:
Chris Kuzawa and his students and collaborators use principles from anthropology and evolutionary biology to gain insights into the biological and health impacts of human developmental plasticity. Thier primary field research is conducted in Cebu, the Philippines, where they work with a large birth cohort study that enrolled more than 3,000 pregnant women in 1983 and has since followed their offspring into adulthood (now 30 years old). They use the nearly 3 decades of data available for each study participant, and recruitment of generation 3 (the grandoffspring of the original mothers), to gain a better understanding of the long-term and intergenerational impacts of early life environments on adult biology, life history, reproduction, and health. A theme of much of this work is the application of principles of developmental plasticity and evolutionary biology to issues of health. Professor Kuzawa is a member of the National Academy of Sciences and American Academy of Arts and Sciences.
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/91503621521
Passcode: NICO23
About the Speaker Series:
Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems and data science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.
Time
Wednesday, February 22, 2023 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
WED@NICO SEMINAR: Daniel Abrams, Northwestern University "Tractable mathematical modeling of social systems: some successes and failures"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level, Chambers Hall
Details
Speaker:
Daniel Abrams, Professor of Engineering Sciences and Applied Mathematics and (by courtesy) Physics and Astronomy, Northwestern University
Title:
Tractable mathematical modeling of social systems: some successes and failures
Abstract:
TBA
Speaker Bio:
Daniel Abrams has broad scientific interests ranging from coupled oscillators to mathematical geoscience to the physics of social systems. He tries to approach these wide-ranging problems by creating greatly simplified mathematical models where rigorous analysis is possible, hopefully capturing some essential properties of the system. The work in different fields is generally connected by similar mathematical techniques drawn from the study of nonlinear dynamics.
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/98183822887
Passcode: NICO23
About the Speaker Series:
Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems and data science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.
Time
Wednesday, March 1, 2023 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
WED@NICO SEMINAR: Alina Arseniev-Koehler, Purdue University "Stigma's Uneven Decline"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
//
Lower Level, Chambers Hall
Details
Speaker:
Alina Arseniev-Koehler, Assistant Professor, Sociology, Purdue University
Title:
Stigma’s Uneven Decline
Abstract:
Has the stigma targeting diseases declined? We analyze 4.7 million news articles to create new measures of stigma for 106 health conditions from 1980-2018, using word embedding methods for text analysis. We then examine how this stigma changed for different types of conditions across time using mixed effects regression modeling. We find that in the 1980s, most diseases were marked by strong connotations of disgust, immorality, and negative personality traits. Since then, stigma declined dramatically for chronic illnesses: cancers, neurological conditions, genetic diseases, and many other conditions have shed most of their negative connotations. But for other types of conditions, stigma proved especially resistant to change. Across the decades, behavioral health conditions (mental illnesses, addictions, and eating disorders) persistently connoted immorality and negative personality traits. Infectious diseases remained strongly linked to attributions of disgust. Stigma has transformed from a sea of negative connotations surrounding most diseases to a narrower set of judgments targeting conditions where the primary symptoms are aberrant behaviors. (This talk is based on research with Rachel Best at the University of Michigan).
Speaker Bio:
Alina Arseniev-Koehler is a computational and cultural sociologist with substantive interests in language, health, and social categories. Alina strives to clarify core concepts and debates about cultural meaning in sociology. For example, how do individuals learn and deploy stereotypes? Empirically, Alina focuses on cases where meaning is linked to inequality and health, such as the moral meanings attached to body weight, the stigmatizing meanings of disease, and gender stereotypes. To investigate these topics, Alina uses computational methods and machine learning, especially computational text analysis.
Alina’s work also circles around a methodological question: how can scientists measure meanings encoded in text data, such as news articles and social media posts? Computational text analysis requires scientists to mathematically model the nuanced ways in which human language encodes and conveys meaning. As highlighted by Alina’s work, innovation in computational text analysis is tightly intertwined with innovation in theoretical understanding of meanings.
Alina received a B.A. in Sociology from University of Washington in 2014, and a master’s and Ph.D. in Sociology from the University of California, Los Angeles in 2022.
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/91034727443
Passcode: NICO23
About the Speaker Series:
Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems and data science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.
Time
Wednesday, March 8, 2023 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)