Events
Past Event
WED@NICO WEBINAR: Lightning Talks with Northwestern Fellows and Scholars!
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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
Webinar:
Zoom link: https://northwestern.zoom.us/j/98719696231
Passcode: nico
Speakers:
Ifeoma Ozodiegwu - Postdoctoral Fellow, Feinberg School of Medicine
Chilochibi Chiziba - Research Assistant, Feinberg School of Medicine
Manuela Runge - Postdoctoral Fellow, Feinberg School of Medicine
David Sabin-Miller - Ph.D. candidate, McCormick School of Engineering
Yanxuan Shao - Ph.D. student, Weinberg College of Arts and Sciences
○ Ifeoma Ozodiegwu - Postdoctoral Fellow, Feinberg School of Medicine, Northwestern University
○ Chilochibi Chiziba - Research Assistant, Feinberg School of Medicine, Northwestern University
Title: Urban-rural differentials in the determinants of malaria transmission in Nigeria
Abstract: Nigeria accounted for roughly a quarter of global malaria cases and deaths in 2018. However, malaria transmission is heterogeneous at lower spatial scales, and understanding the drivers of transmission can inform decisions on where interventions should be prioritized. We aimed to identify factors associated with high levels of malaria transmission within urban and rural areas. We merged and analyzed cluster-level data collected in Nigeria in 2010, 2015, and 2018 by the Demographic Health Survey (DHS) program. Our analysis highlight similarities and differences in the determinants of transmission in urban and rural areas. Our findings provides supporting evidence for the positive impact of increased access to ACTs and suggest the need for greater intervention distribution in highly populated rural areas.
Bios: Ifeoma Ozodiegwu is a Postdoctoral Fellow with Dr. Jaline Gerardin Lab’s in the Department of Preventive Medicine. She received her Doctor of Public Health (DrPH) in Epidemiology from East Tennessee University (ETSU) in May 2019, where she was a Rotary International Global Grant recipient. At the Dr. Gerardin Lab, she leads dynamical modeling for understanding the impact of malaria intervention mixes in Nigeria, and supports analytical projects that evaluate spatial variation in malaria transmission within endemic countries.
Chilochibi Chiziba is a Research Assistant with Dr. Jaline Gerardin Lab in the Department of Preventive Medicine. He is currently in his final year pursuing a Master of Public Health at the University of Zambia and holds a Bachelor of Arts Economics with Demography from the same institution. His experience includes Data Analytics, Research, Monitoring, and Evaluation while working for Atlas Corps, Jhpiego, and Akros Research on studies and projects focused on Renewable Energy, HIV/AIDS, and Health Systems. He is currently working with Dr. Ozodiegwu to explore variations in malaria transmission intensity, and interventions between urban and rural areas in Nigeria.
○ Manuela Runge - Postdoctoral Fellow, Feinberg School of Medicine, Northwestern University
Title: Modelling COVID-19 transmission and health burden in Illinois
Abstract: COVID-19 continues to spread across many states in the US and reached 664,620 cases and 11,552 deaths in Illinois. Until a vaccine is available, social distancing, lockdowns, contact tracing, and mask wearing and testing are the only measures to contain the epidemic and to prevent exceeding hospital capacities and limit the public health burden. Epidemiological models are widely used for simulating the likely effect of available measures and forecasts to inform decision-makers and hospital capacity planning.
A stochastic compartmental transmission model was calibrated for the eleven COVID-19 regions in Illinois, to simulate number of cases and hospital bed demand. The model was calibrated using hospital census and deaths report data between March and November 2020. The model was used to simulate the effects of reducing delay in testing, increased testing, contact tracing, lockdown, and social distancing alone or in combination. Outcome measures included the number of cases, deaths, or probability of exceeding hospital bed capacities. During course of the epidemic, the modelling outputs provided valuable predictions to support the local health department and provided insights into local transmission and disease dynamics.
Bio: Manuela Runge is a postdoctoral researcher at Northwestern University. Her research focuses on simulating malaria interventions to inform malaria control strategies at the country level and the development and application of a COVID-19 transmission model to support the local health department.
○ David Sabin-Miller - Ph.D. candidate in the Department of Engineering Sciences & Applied Mathematics, McCormick School of Engineering, Northwestern University
Title: When pull turns to shove: modeling how tribalism and environmental bias form ideological distributions in large populations
Abstract: Accurate modeling of political opinion dynamics can help us understand polarization, and the conditions which cause it. We put forward a framework for modeling the ideological drift of individuals influenced by a heterogeneous but systematically biased environment. We show that a local-attraction/distal-repulsion dynamic, distorted by tribalist "in-group-out-group" bias, can explain both the current US ideological distribution and behavior under perturbation as seen in a recent experiment. This talk will be a short summary of work published this October in Physical Review Research.
Bio: David is a fourth-year Ph.D. candidate working with Prof. Daniel Abrams in ESAM. His current research interests are in modeling and stochastic numerical methods.
○ Yanxuan Shao - Ph.D. student in the Department of Physics and Astronomy, Weinberg College of Arts and Sciences, Northwestern University
Title: Spontaneous oscillations in microfluidic networks
Abstract: Microfluidic systems are broadly applicable to chemical analysis, flow cytometry, point-of-care diagnosis, chemical synthesis, etc. However, the precise manipulation of fluid motion usually requires external hardware, such as micropumps and microvalves. In our research, we have examined a simple microfluid network design that exhibits nonlinear flow dynamics without the need of external control components. In particular, the system exhibits the spontaneous emergence of flow-rate oscillations for fixed inlet and outlet pressures, which we demonstrate using both simulations of the Navier-Stokes equations and an analytical model that captures essential aspects of the dynamics. Our results may help improve the portability and performance of microfluidic chips.
Bio: Yanxuan is a PhD student in the Department of Physics and Astronomy at Northwestern University. She works in Prof. Adilson Motter's group.
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.
Time
Wednesday, December 2, 2020 at 12:00 PM - 1:00 PM
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
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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)