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
Lightning Talk Speakers:
○ Sugat Dabholkar - PhD Candidate in the Learning Sciences program at the School of Education and Social Policy, Northwestern University.
Title: Emergent Systems Microworlds to teach and learn about complex emergent phenomena
Abstract: Learning and reasoning about complex systems is not simple. Both novices and experts often fall into the trap of level-slippage while reasoning about complex natural phenomena. Level-slippage is the confusion that arises when one expects emergent macro-level patterns to be similar to local micro-level patterns. Such confusion is people’s source of a deep misunderstanding of several patterns and phenomena in the world.
In my work, I seek to address this issue for high school science students by combining two powerful design approaches in Learning Sciences, namely, agent-based modeling of emergent systems and constructionism. We call this design approach Emergent Systems Microworlds (ESM). This approach is based on Wilensky and Papert’s restructuration theory (2010), which argues for the importance of representational infrastructure for changing fundamental aspects of knowledge encodings in a disciplinary domain. In this talk, I will discuss how agent-based restructurations in an ESM allow learners to develop fundamentally deeper insights into complex phenomena.
In an ESM-based curriculum, students explore and learn about emergent phenomena, using agent-based computational models that are designed in NetLogo (Wilensky, 1999) in the form of a microworld. In such models, an agent is a computational object with particular properties and actions. An ‘emergent’ phenomenon is modeled in terms of agents and their interactions. Microworlds are encapsulated open-ended computational exploratory environments in which a set of concepts can be explored, through interactions that lead to knowledge construction. In this talk, I will present some examples of ESM-based curricula that I designed for high school students to learn about genetics and evolution, two fundamental ideas in biology. I will discuss how the agent-based representational architecture in the ESMs allowed the students to engage in reasoning about complex systems principles in the context of the phenomena they were studying.
Bio: Sugat Dabholkar is a doctoral candidate in the Learning Sciences program at the School of Education and Social Policy, Northwestern University. His work involves designing technology-enhanced learning environments for learning scientific thinking, computational thinking, and complex systems thinking. Over the past four years, Sugat has developed several computational agent-based models, many of which have been incorporated into curricular units for high school students. He has conducted Professional Development programs for teachers focusing on designing Computational Thinking integrated STEM curricular units. These curricular units have been used in school settings in the US as well as in India.
○ Xuan Ma - Postdoctoral Fellow at the Feinberg School of Medicine, Northwestern University
Title: Probing motor control during naturalistic movements for extending BCI use
Abstract: Most existing sensorimotor and brain computer interface (BCI) studies have investigated the mapping from motor cortex (M1) to muscles by training monkeys to perform a few instructed movements in highly restricted conditions. Motivated by the demand of extending those in-lab studies to a wider realm, we propose to explore motor control during more natural movements of unrestrained monkeys. We simultaneously recorded M1 neural activity and electromyograph (EMG) wirelessly while the monkey was in a plastic telemetry cage in which it could perform various free-form movements. We then investigated the features of these signals and the consistency of the relationship between them. In this talk, I will describe progress we have made and challenges we are facing to extend BCI use in more naturalistic contexts, and will also introduce our efforts with deep learning methods to address those challenges.
Bio: Xuan Ma is currently a postdoctoral fellow in Feinberg School of Medicine, Northwestern University. He received the Ph.D. degree in control science and engineering from Huazhong University of Science and Technology, Wuhan, China, in 2017. His research interests include cortically-controlled functional electric stimulation, neural motor control system modeling, and biomedical signal processing.
○ Suman Kalyan Maity - Postdoctoral Fellow at the Kellogg School of Management, CSSI and NICO
Title: Winners, Losers, and Future Achievement
Abstract: One of the most robust findings on human performance is that past achievement predicts future achievements. Indeed, prior achievements may reflect underlying, differentiating characteristics rendering past winners more predictably outperforming their non-winning counterparts in future competitions. Further, the Matthew effect posits that past victories bring reputation and recognition that can translate into tangible assets, which increase the chance for future victories. Hence even if the deck was not stacked against some in favor of others, positive feedback operating on arbitrary initial advantage can increasingly set apart winners from losers. These mechanisms lead to one fundamental principle with crucial implications: Between past winners and losers, it is the former that are more likely to win in the future. Indeed, partly due to the robustness of this principle, the idea of selecting on winners has become one of the most commonly used heuristics in identifying and nurturing talents across a remarkably wide range of domains. In this talk, we systematically test against the principle of selecting between winners and losers across various settings. We observe that whenever there existed a reward based milestone (being on the podium, entering the main draw of a Tennis tournament etc.), the athletes who had just missed it ended up outperforming the athletes who narrowly achieved the milestone in future endeavors.
Bio: Suman Kalyan Maity is a Postdoctoral Fellow at the Kellogg School of Management, The Center for Science of Science & Innovation (CSSI), and NICO.
○ Rebeka O. Szabo - Pre-Doctoral Fellow at the Kellogg School of Management, Northwestern University
Title: The Micro-Dynamic Nature of Team Interaction
Abstract: Teams have become a popular organization form since well-functioning task-focused groups are basic pillars of successful organizations. While there is much interest in contemporary social science in understanding team processes that lead to efficiency, most of these researches rely heavily on self-reported data yielding static and potentially biased information and tends to overlook actual interaction processes. We propose a novel approach that allows portraying a nuanced, complex picture of problem-solving group behavior by measuring performance dynamics as it evolves in real-time, in a controlled environment. The research aims to explore how collaboration networks of small project teams evolve across time and team members, and how it relates to successful task performance. We investigate interaction patterns in escape rooms, where all teams are video recorded during the task-solving process in the same experimental environment. We expected that homogeneous distribution of interaction ties across time and team members fosters successful problem-solving. Concerning the impact of the initial social roles on the dynamics of the interaction pattern, we hypothesized that flexible, less hierarchical team structures favor for problem-solving. This research aims to advance the new science of teams' by focusing on the network micro-mechanisms that allows us to treat teams as dynamic, adaptive, task-performing systems.
Bio: Rebeka O. Szabo is a visiting Pre-Doctoral Fellow at the Kellogg School of Management. She is a PhD candidate at the Central European University's Department of Network and Data Science.
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.
Live Stream:
Time
Wednesday, March 4, 2020 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 with NU Scholars!
Northwestern Institute on Complex Systems (NICO)
12:00 PM
//
Lower Level, Chambers Hall
Details
May 20th Speakers:
Yulin Yu, Postdoctoral Fellow, Kellogg School of Management
Feihong Xu, PhD Candidate, McCormick School of Engineering
Maalvika Bhat, PhD Student, School of Communication
Rochana Chaturvedi, Postdoctoral Fellow, Kellogg School of Management
NICO Lightning talks are open to Northwestern graduate students, postdoctoral fellows, and visiting scholars! If you are interested in signing up for a future session, please fill out this short survey.
Talk Titles and Abstracts:
Yulin Yu
Postdoctoral Fellow
Kellogg School of Management &
Northwestern Institute on Complex Systems
Human–AI Creative Pathways: How People and Machines Differ in Creative Strategy
Generative AI offers the promise of amplifying creativity by recombining knowledge at a scale far beyond human capacity, yet humans still hold key advantages in flexibility and contextual reasoning. To understand how each achieves novelty, we analyzed more than 5,000 responses to the Divergent Association Task from both humans and AI systems using network-based methods. We find that while individual humans use fewer and simpler conceptual categories than machines, the collective diversity of human ideas is substantially higher. Human creative pathways tend to follow a one-directional but highly unpredictable trajectory, whereas AI systems rely on repetitive, back-and-forth exploration patterns. Finally, both humans and machines show anchoring effects—early ideas shape later responses—but in opposite ways: humans anchor low, while machines anchor high.
Feihong Xu
PhD Candidate
Engineering Sciences & Applied Mathematics
McCormick School of Engineering
A Well-Calibrated Model Similarity Measure for Arbitrary Neural Networks
Deep learning approaches have transformed biological and biomedical image analysis, but model opacity and fragility remain major obstacles to trustworthy use. One barrier is the lack of a well-calibrated measure of similarity across arbitrary neural networks trained with different architectures, checkpoints, random initializations, and training strategies. Existing notions of model similarity span functional and representational domains, often rely on heuristic assumptions, and are susceptible to spurious signals introduced by probing samples, making principled cross-model meta-analysis difficult. Here, we clarify prevailing notions of deep neural network similarity and benchmark their robustness under extensive out-of-distribution perturbations. We then introduce the Ahmad RV coefficient on chain weight matrices (wARV), a theoretically grounded weight-space similarity measure that combines chain-normalized weights with the RV coefficient. wARV is sample-agnostic, symmetric, computationally efficient, and better calibrated than current measures. Across benchmarks varying random initialization, training checkpoint, architecture, and training strategy, wARV more faithfully tracks functional similarity while avoiding confounding effects from probing data. Applying wARV to deep neural network models on both generic and medical image classification tasks, we uncover substantial learning heterogeneity and instability even among models with similar predictive performance.
Maalvika Bhat
PhD Student
Technology and Social Behavior
School of Communication &
McCormick School of Engineering
Scholars See Clickbait as a Greater Threat to Science Than to Their Own Work
As scientific research competes for attention in a media landscape driven by sensationalism, the risks of misrepresentation grow. This study examines whether academics, while widely recognizing clickbait as a threat to science broadly, tend to downplay its relevance to their own work. Surveying 5,603 U.S.-based researchers, we find a consistent perception gap between systemic and personal risk, one that varies by career stage and disciplinary context. Early-career scholars show a pronounced version of this asymmetry: they express heightened concern about clickbait’s harms to science while rating its relevance to their own work as comparatively lower, a pattern that leaves them most exposed at a stage when reputational stakes are highest.
Rochana Chaturvedi
Postdoctoral Fellow
Kellogg School of Management &
Northwestern Institute on Complex Systems
Who Gets the Callback? Generative Artificial Intelligence and Gender Bias
Large language models are increasingly embedded in hiring workflows, raising concerns about their potential to amplify societal biases — yet how these biases manifest within and across occupations, and the role of model 'personality' in shaping these biases, remains unexplored. We introduce a three-part attribution framework applied to 332,044 real-world job ads, measuring gender-based callback bias, associations of skills and traits with gendered stereotypes in LLMs, and the effect of simulated recruiter personas. We find that LLMs systematically favor men, especially in higher-wage roles, with their decisions tracking traditional gendered language cues in job postings. Notably, assigning a low-agreeableness persona reduces model bias, implicating sycophancy as a mechanism reinforcing societal stereotypes; at the same time, controversial personas trigger internal guardrails leading to more cautious and less-biased outputs. These findings highlight how alignment choices in AI-driven hiring systems shape bias, with important implications for fairness and diversity.
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/98031689779
About the Speaker Series:
Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems, networks, and artificial intelligence. 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 20, 2026 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)