Events
Past Event
WED@NICO SEMINAR: Samuel Scarpino, University of Vermont "On the Predictability of Infectious Disease Outbreaks"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level Chambers Hall
Details
Title:
On the Predictability of Infectious Disease Outbreaks
Speaker:
Samuel Scarpino - Assistant Professor of Mathematics & Statistics, University of Vermont
Talk Abstract:
Infectious disease outbreaks recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and their shared environment. As a result, predicting when, where, and how far diseases will spread requires a complex systems approach to modeling. Recent studies have demonstrated that predicting different components of outbreaks is feasible. Therefore, advancing both the science and practice of disease forecasting now requires testing for the presence of fundamental limits to outbreak prediction. To investigate the question of outbreak prediction, we study the information theoretic limits to forecasting across a broad set of infectious diseases using permutation entropy as a model independent measure of predictability. Studying the predictability of a diverse collection of historical outbreaks we identify a fundamental entropy barrier for infectious disease time series forecasting. However, we find that for most diseases this barrier to prediction is often well beyond the time scale of single outbreaks, implying prediction is likely to succeed. We also find that the forecast horizon varies by disease and demonstrate that both shifting model structures and social network heterogeneity are the most likely mechanisms for the observed differences in predictability across contagions. Our results highlight the importance of moving beyond time series forecasting, by embracing dynamic modeling approaches to prediction and suggest challenges for performing model selection across long disease time series. We further anticipate that our findings will contribute to the rapidly growing field of epidemiological forecasting and may relate more broadly to the predictability of complex adaptive systems.
Live Stream:
To join the Meeting: bluejeans.com/8474912527
To join via Browser: bluejeans.com/8474912527/browser
Time
Wednesday, May 24, 2017 at 12:00 PM - 1:00 PM
Location
Lower Level Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
Data Science Nights - MAY 2026 - Speaker: Xudong Tang, Computer Science and NICO
Northwestern Institute on Complex Systems (NICO)
5:30 PM
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M416, Technological Institute
Details
MAY MEETING: Thursday, May 28, 2026 at 5:30pm (US Central)
LOCATION:
ESAM Conference Room, Tech M416
2145 Sheridan Road, Evanston, IL 60208
AGENDA:
5:30pm - Meet and greet with refreshments
6:00pm - Talk with Xudong Tang, PhD Student, Computer Science, NICO, and the Human-AI Collaboration Lab, Northwestern University
TALK TITLE:
Human and Machine Perception of Voice Similarity
ABSTRACT:
Modern voice cloning systems generate synthetic speech that listeners frequently cannot identify as being synthetic. But a voice can sound natural without sounding like the intended person, and what determines whether a clone is heard as a particular person is an open question. Here we report a large-scale preregistered experiment in which we collected 92,239 responses from 175 participants on their perception of pairs of real recordings, voice clones, and continuously morphed voices drawn from 100 contemporary celebrities across 20 speaker groups. We find that voice clones do not reliably preserve perceived speaker identity, reducing same-speaker judgments by 12.7 percentage points even though the clones are produced by a state-of-the-art text-to-speech model, while leaving different-speaker judgments unchanged. Using continuously morphed stimuli, we find that speakers vary substantially in how much variation their perceived identity tolerates, and that this variation is not predicted by speaker demographics. Speaker embeddings account for 58.9\% (95\% CI = [55.7, 61.9]) of variance in identity judgments, which is more than acoustic features, social attributes, and clone status combined. Once all these observed features are accounted for, clone status adds no additional predictive power. These results shows that the perceptual impact of voice cloning is positional rather than categorical: we can model how listeners judge a voice by how close it falls to the perceptual boundary that defines each speaker's recognizable voice, applying the same criterion to real and synthetic speech alike.
DATA SCIENCE NIGHTS are monthly meetings featuring presentations and discussions about data-driven science and complex systems, organized by Northwestern University graduate students and scholars. Students and researchers of all levels are welcome! For more information: http://bit.ly/nico-dsn
FUTURE DATES:
Data Science Nights will return in September!
Time
Thursday, May 28, 2026 at 5:30 PM - 7:00 PM
Location
M416, Technological Institute Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
Spring 2026 Commencement
University Academic Calendar
All Day
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Spring 2026 Commencement
Time
Sunday, June 14, 2026
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Juneteenth - University Closed
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Juneteenth - University Closed
Time
Friday, June 19, 2026
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University Academic Calendar
Independence Day (observed) - University Closed
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Independence Day (observed) - University Closed
Time
Friday, July 3, 2026
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Fall 2026 Classes Begin
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Fall 2026 Classes Begin
Time
Wednesday, September 23, 2026
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University Academic Calendar