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)
CANCELLED - WED@NICO WEBINAR: Fabiana Zollo, Ca' Foscari University of Venice "Polarization, Misinformation, and Hate: Investigating Social Dynamics with Online Data"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
Details
Due to unforseen circumstances we regret that this webinar is cancelled. We hope to reschedule at a later date.
Speaker:
Fabiana Zollo, Assistant Professor, Dept. of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice
Title:
Polarization, Misinformation, and Hate: Investigating Social Dynamics with Online Data
Abstract:
The COVID-19 pandemic has highlighted the challenge of conveying and communicating complexity and uncertainty to the public, also given the increasing central role of the Internet and social media. Designed to maximise users' presence on the platform and to deliver targeted advertising, social media transformed the information landscape and have rapidly become the main information sources for many of their users. Information spreads faster and farther online, in a flow-through system where users have immediate access to unlimited content. This may facilitate the proliferation of mis- and dis-information, generating chaos, and limiting access to correct information. In this talk, I will provide an overview of how social dynamics and behavioural patterns can be investigated and analysed with online data, and discuss how data-driven insights can be used to design effective strategies to counter misinformation and improve the overall quality of the information system.
Speaker Bio:
Fabiana Zollo is an Assistant Professor in the Dept. of Environmental Sciences, Informatics and Statistics and Research Fellow in the Center for the Humanities & Social Change at Ca’ Foscari University of Venice. Her research investigates information and misinformation spreading, social dynamics, and the emergence of collective narratives on online social media. She collected several papers on this topic, with both national and International co-authors. Since 2019, she is serving as an External Expert to the European Food Safety Authority (EFSA). She is a member of the Task Force “Data Science” established by AGCOM, the Italian Communications Regulatory Authority, and a member of the Working Group established by the Italian Presidency of Council to study the phenomenon of disinformation on COVID-19.
Webinar:
CANCELLED
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, October 27, 2021 at 12:00 PM - 1:00 PM
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
Data Science Nights - October 2021 Meeting (Speaker: Omkar Ranadive of Alchera Labs)
Northwestern Institute on Complex Systems (NICO)
5:15 PM
Details
OCTOBER MEETING: Thursday, October 28, 2021 at 5:30pm (US Central) via Zoom and Gather
DATA SCIENCE NIGHTS are monthly talks on data science techniques or applications, organized by Northwestern University graduate students and scholars. Aspiring, beginning, and advanced data scientists are welcome!
AGENDA:
5:15: Data Science Night Meet + Greet
* Zoom Link: https://northwestern.zoom.us/j/97300898035
5:30: Presentation by Omkar Ranadive of Alchera Labs
6:00: Code+Cookies on Gather Town
* Gather link: https://gather.town/app/UCTJAHOgQi2FLx4O/DSN
SPEAKER:
Omkar Ranadive is an Applied Scientist at Alchera Labs. He completed his Master's in Computer Science at Northwestern University in 2021. His areas of research include Machine Learning, Deep Learning and Reinforcement Learning.
TOPIC: Using machine learning to detect wildfires
Wildfires are a huge problem in the state of California. In 2021 alone, there have been 7,000+ wildfires in California, which have burned down over 2 million acres of land. An automated system that detects wildfires early, before they spread, can help save lives and minimize damage to land and structures. In this talk, we will discuss Alchera's Wildfire Alert System, which uses machine learning to perform early detection of wildfires. The system analyzes images in real-time from over 800 cameras and sends immediate alerts to users.
For more info: data-science-nights.org
Supporting Groups:
This event is supported by the Northwestern Institute for Complex Systems and the Northwestern Data Science Initiative.
Time
Thursday, October 28, 2021 at 5:15 PM - 7:30 PM
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
WED@NICO WEBINAR: Katherine Amato, Northwestern U "Integrating evolutionary perspectives into microbiome research"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
Details
Speaker:
Katherine Amato, Assistant Professor, Department of Anthropology, Northwestern University
Title:
Integrating evolutionary perspectives into microbiome research
Abstract:
TBA
Speaker Bio:
Katie Amato is a biological anthropologist studying the gut microbiota in the broad context of host ecology and evolution. She is particularly interested in understanding how changes in the gut microbiota impact human nutrition and health in populations around the world, especially those with limited access to nutritional resources. She also uses non-human primates as models for studying host-gut microbe interactions in selective environments and to determine whether the human gut microbiota has characteristics that are unique among primates. Her current research focuses on microbial contributions to host nutrition during periods of reduced food availability or increased nutritional demands, as well as microbial influences on brain growth. She has worked in the field extensively with black howler monkeys in southeastern Mexico, and is currently establishing projects with both humans and non-human primates in other parts of the world.
Webinar:
https://northwestern.zoom.us/j/96195982317
Passcode: nico
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, November 3, 2021 at 12:00 PM - 1:00 PM
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
WED@NICO WEBINAR: Konrad Kording, University of Pennsylvania "Quasiexperimental causality in neuroscience"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
Details
Speaker:
Konrad Kording, Penn Integrated Knowledge Professor, University of Pennsylvania
Title:
Quasiexperimental causality in neuroscience
Abstract:
TBA
Speaker Bio:
Konrad Kording is interested in the question of how the brain solves the credit assignment problem and similarly how we should assign credit in the real world (through causality). In extension of this main thrust he is interested in applications of causality in biomedical research. Konrad has trained as student at ETH Zurich with Peter Konig, as postdoc at UCL London with Daniel Wolpert and at MIT with Josh Tenenbaum. After a decade at Northwestern University he is now PIK professor at UPenn.
Webinar:
https://northwestern.zoom.us/j/92583896352
Passcode: nico
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, November 10, 2021 at 12:00 PM - 1:00 PM
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)