Events
Past Event
WED@NICO SEMINAR: Lightning Talks with Northwestern Fellows and Scholars!
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level, Chambers Hall
Details
NICO is hosting a lightning talk seminar each term as a part of our Wednesdays@NICO seminar series. Northwestern graduate students and postdoctoral fellows are invited to participate. To sign up for future lightning talks, please visit: https://bit.ly/2lRqSXK
FALL 2019 SPEAKERS
Diego Gómez-Zará - Ph.D. Candidate, Technology and Social Behavior
Title: A Network Approach to the Formation of Self-assembled Teams
Abstract: Which individuals in a network make the most appealing teammates? Which invitations are most likely to be accepted? And which are most likely to be rejected? This study explores the factors that are most likely to explain the selection, acceptance, and rejection of invitations in self-assembling teams. We conducted a field study with 780 participants using an online platform that enables people to form teams. Participants completed an initial survey assessing traits, relationships, and skills. Next, they searched for and invited others to join a team. Recipients could then accept, reject, or ignore invitations. Using Exponential Random Graph Models (ERGMs), we studied how traits and social networks influence teammate choices. Our results demonstrated that (a) agreeable leaders with high psychological collectivism send invitations most frequently, (b) previous collaborators, leaders, competent workers, females, and younger individuals receive the most invitations, and (c) rejections are concentrated in the hands of a few.
Alex Mercanti - Ph.D Candidate, Engineering Sciences & Applied Mathematics
Title: Protecting your privacy in machine learning using randomness.
Abstract: Machine learning models are commonly trained over datasets that contain personal information about people and their daily routine, health, online activity, and social behavior. Although these models play a crucial role in modern software applications, the extent to which trained machine learning models leak private and sensitive features of their respective training data remains poorly understood. In this talk, I will discuss the privacy risks associated with publicly releasing trained machine learning models and will demonstrate that the addition of random noise to training algorithms guarantees privacy for each individual in the training dataset at minimal cost to the accuracy of the model.
Kyosuke Tanaka - Ph.D Candidate, Media, Technology, and Society
Title: How dispositional and positional factors affect an individual’s ability to efficiently route messages in a network
Abstract: Milgram’s small-world experiment provided evidence for six degrees of separation, on average a chain of five contacts separated any two random people in the world. However, this was only true for those messages that reached the final destination. While, in theory, the small-world phenomenon is structurally common in social networks, empirical evidence shows that human navigation of small-world social networks is remarkably challenging. Messages often reach the intended destination via a longer path than expected, get enmeshed in loops, and/or often never reach it. This leads to painful consequences for organizations that require information routing to share (or retrieve) knowledge among their members. Extreme examples of these failures contributed to the loss of the space shuttles Challenger and Columbia. Here, I present a study of an understudied type of error—network routing—and introduce network acuity to conceptualize and operationalize an individual’s ability to efficiently route messages. Using, 6-DoS (Six Degrees of Separation), a network routing task based on Milgram’s small-world experiment with 435 individuals organized into 25 networks, I explored two types of factors that impact an individual’s network acuity: positional factors (where you are in the network) and dispositional factors (who you are). Results show that (a) those in the core or brokerage position had high network acuity than did peripheral or non-bridge members, (b) neuroticism was positively associated with acuity, (c) conscientiousness was negatively associated with acuity. Further, individuals’ network positions impacted network acuity more than dispositional characteristics. The results of this experimental study illustrate not only the usefulness of the concept of network acuity to characterize network routing errors but also advance our understanding of factors that explain variance in individuals' network acuity.
Alexandria Volkening - NSF-Simons Fellow, NSF-Simons Center for Quantitative Biology
Title: Forecasting U.S. elections with compartmental models of infection
Abstract: U.S. election forecasting involves polling likely voters, making assumptions about voter turnout, and accounting for various features such as state demographics and voting history. While political elections in the United States are decided at the state level, errors in forecasting are correlated between states. With the goal of shedding light on the forecasting process and exploring how states influence each other, we develop a framework for forecasting elections in the U.S. from the perspective of dynamical systems. Through an interdisciplinary approach that borrows ideas from epidemiology, we show how to combine a compartmental model with public polling data from HuffPost and RealClearPolitics to forecast gubernatorial, senatorial, and presidential elections at the state level. Our results for the 2012 and 2016 U.S. races are largely in agreement with those of popular sources, and we use our model to explore how subjective choices about uncertainty impact results. We conclude by comparing our forecasts for the 2018 midterms with those of popular analysts, and we discuss future directions related to the 2020 elections. This is joint work with Daniel Linder (Augusta Univ.), Mason Porter (UCLA), and Grzegorz Rempala (Ohio State Univ.).
Live Stream:
Time
Wednesday, December 4, 2019 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
No Classes - Martin Luther King Jr. Day (University Offices Closed)
University Academic Calendar
All Day
Details
No Classes - Martin Luther King Jr. Day (University Offices Closed)
Time
Monday, January 20, 2025
Contact
Calendar
University Academic Calendar
WED@NICO SEMINAR: István Kovács, Northwestern University "The Brain as a Critical Spatial Network"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level, Chambers Hall
Details
Speaker:
István Kovács, Assistant Professor, Department of Physics and Astronomy, Northwestern University
Title:
The Brain as a Critical Spatial Network
Abstract:
Recent cellular-level volumetric brain reconstructions have revealed petabytes of information about the astronomical level of anatomic complexity. Determining which structural aspects of the brain to focus on, especially when comparing with computational models and other organisms, remains a major challenge. Recently, we utilized tools from statistical physics to show that cellular brain anatomy satisfies universal scaling laws, establishing the notion of "structural criticality" in the cellular structure of the brain. For example, we obtain estimates for critical exponents in the human, mouse and fruit fly brains and show that they are consistent between organisms, to the extent that data limitations allow. Such universal quantities are robust to many of the microscopic details of the cellular structures of individual brains, providing a key step towards generative computational models, and also clarifying in which sense one animal may be a suitable anatomic model for another. Therefore, our framework provides clear guidance in selecting informative structural properties of cellular brain anatomy. Similarly, in terms of the complex interplay between the spatial and topological aspects of the neural connectome, we showed that brain networks share simple organizing principles across the studied organisms. We used these observations to design scalable generative network models, and demonstrated predictive power beyond the input data, as they capture several additional biological and network characteristics, like synaptic weights and graphlet statistics. Currently, with our experimental collaborators, we are working on incorporating transcriptomics data into our models to also understand the underlying genetic wiring rules of brain organization. As in the brain the hardware is the software, even with all the remaining open questions, our results are expected to have broad implications on brain function and dynamics.
References:
[1] H. S. Ansell and I. A. Kovács (2024) Unveiling universal aspects of the cellular anatomy of the brain, Communications Physics, 7, 184
[2] A. Salova and I. A. Kovács (2024) Combined topological and spatial constraints are required to capture the structure of neural connectomes, Network Neuroscience, 1-41
Speaker Bio:
István Kovács is Assistant Professor in the Department of Physics and Astronomy at Northwestern University, a core member of NICO and NITMB, with a courtesy appointment in the Department of Engineering Science and Applied Mathematics. He is a recipient of the 2025 NSF CAREER Award, the Karl Rosengren Faculty Mentoring Award in 2021 and 2023, and was selected for the 2021-2022 Faculty Honor Roll at Northwestern University, for powerful and exceptional impact on student experience. Previously he was a postdoctoral fellow in the Network Science Institute at Northeastern University, a visiting researcher in the Center for Cancer Systems Biology at the Dana-Farber Cancer Institute and at University of Toronto, as well as at the Department of Network and Data Science of the Central European University. He received a PhD in Physics from the Eötvös Loránd University in Hungary, working at the Wigner Research Centre for Physics. His group develops novel methodologies to predict the emerging structural and functional patterns in problems ranging from systems biology to quantum physics, in close collaboration with experimental groups.
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: ZOOM TBA
Passcode: TBA
About the Speaker Series:
Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems, data science and network 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, January 22, 2025 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
Winter Classes End
University Academic Calendar
All Day
Details
Winter Classes End
Time
Saturday, March 15, 2025
Contact
Calendar
University Academic Calendar
Spring Classes Begin - Northwestern Monday: Classes scheduled to meet on Mondays meet on this day.
University Academic Calendar
All Day
Details
Spring Classes Begin - Northwestern Monday: Classes scheduled to meet on Mondays meet on this day.
Time
Tuesday, April 1, 2025
Contact
Calendar
University Academic Calendar