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)
WED@NICO SEMINAR: Lightning Talks w/ Northwestern Scholars!
Northwestern Institute on Complex Systems (NICO)
12:00 PM
//
Lower Level, Chambers Hall
Details
Speakers:
Yessica Herrera, Visiting Scholar, Northwestern Institute on Complex Systems
Talk Title: The Body Speaks: Visual Cues of Psychological Stress in Bodily Expressions
Abstract: Emotions shape body movement, yet the visual cues that signal psychological stress—distinct from other emotional states—remain poorly understood. Acute stress alters motor patterns and may produce subtle expressive markers. In this study, dancers performed creative improvisations under stress (induced via the Trier Social Stress Test) and in a control condition. Movements were video-recorded and rated by 25 non-expert observers (ages 18–23, all female) using qualitative parameters from Laban Movement Analysis—Weight, Flow, and Rhythm— alongside perceived stress levels. Our study shows that observers reliably identified stressed performances, associating stress with tense, less fluid, and rhythmically altered movement. These findings reveal nuanced visual cues of psychosocial stress in expressive motion and have implications for fields like dance, clinical assessment, and emotionally intelligent systems. In particular, this work supports the growing efforts to make robotic movement more meaningful to humans by applying insights from movement perception studies to improve the design of expressive and more likable robotic technologies.
Aakriti Kumar, Postdoctoral Fellow, Kellogg School of Management and the Northwestern Institute on Complex Systems
Talk Title: Large language models can provide expert-aligned judgments of empathic communication
Abstract: Large language models (LLMs) appear to excel at empathic communication in text-based conversations. But, how reliably can machines judge the nuances of empathic communication? We compare annotations by experts, crowd workers, and LLMs based on four empathic communication frameworks applied to four different datasets. Specifically, we investigate the inter-rater reliability of these three groups across 1,050 annotations of 200 conversations where one partner is sharing a problem, and the other is offering empathetic support. We find high but imperfect reliability between experts across most sub-components of empathic communication; inter-rater reliability between experts varies based on the clarity, complexity, and subjectivity of these sub-components. Furthermore, we find that LLMs approach expert level inter-rater reliability and surpass the inter-rater reliability between crowd workers and experts. Finally, we demonstrate that evaluating subjective annotation can be misleading with traditional classification metrics but clear and robust when evaluating with inter-rater reliability contextualized by an empirical ceiling.
Tingyu "Mark" Zhao, PhD Student, Department of Industrial Engineering and Management Sciences
Talk Title: Noise Filtering in Complex Networks
Abstract: Networks are powerful representations of complex systems, yet real-world network data are often corrupted by edge-level measurement inaccuracies, sampling biases, and incomplete observations, compromising analytical validity. Here, we introduce the Network Wiener Filter (NetWF), a principled method to filter edge noise that jointly leverages both network topology and explicit noise characterization, thereby enhancing downstream analyses and inferences. We demonstrate the efficacy of NetWF in two distinct settings: the Enron Corpus email network and the genetic interaction network of the yeast \textit{Saccharomyces cerevisiae}, noting promising results in both studies. Equipped with technologies such as NetWF, we advocate for error-aware network analysis, with the hope to usher in a new chapter of network science, one that embraces data imperfection as an inherent feature and learns to navigate it effectively.
Sign Up:
Sign up to present at a future Lightning Talk session. NICO Lightning Talks are open to graduate students, postdoctoral fellows, and visiting scholars.
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/95387714084
Passcode: NICO25
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, May 14, 2025 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
WED@NICO SEMINAR: Rosemary Braun, Northwestern University "The Scale of Life"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
//
Lower Level, Chambers Hall
Details
Speaker:
Rosemary Braun, Associate Professor, Department of Molecular Biosciences, Northwestern University
Title:
The Scale of Life
Abstract:
Living systems exhibit surprising and beautiful self-organization at all scales. At the atomic level, proteins self-assemble into macromolecular complexes. The function of these machines is orchestrated within the cell by regulatory networks, whose activity is in turn dictated by, and coordinated with, the cells environment. This coordination takes place across large spans of space and time: the size and lifetime of organisms as large as the blue whale. Populations and ecosystems of many organisms in turn exhibit remarkable emergent dynamics. Today, advances in single-cell assays enable us to probe the molecular state of every cell in a sample in high-dimensional detail. But is this the correct scale at which to probe living systems? What can we learn from this data, and how can we abstract from the microscopic details to macroscopic phenotypes? In this talk, I will discuss some of our recent work bridging the cell and tissue/organism scales, and discuss some challenges and opportunities for the future.
Speaker Bio:
Rosemary Braun is an Associate Professor of Molecular Biosciences, Applied Mathematics [ESAM], and Physics at Northwestern University. A theoretical physicist by training, she earned her PhD in Physics from the University of Illinois, followed by a Masters in Biostatistics from Johns Hopkins University. She completed her postdoctoral training at the National Cancer Institute (NIH) before joining Northwestern as a faculty member. Today, she works at the intersection of statistics, mathematics, and biology to develop computational tools for analyzing high-dimensional data. In addition to her Northwestern affiliations, she is also Associate Director of the National Institute for Theory and Mathematics in Biology, as well as external faculty of the Santa Fe Institute.
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/97015976754
Passcode: NICO25
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, May 21, 2025 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)