Events
Past Event
WED@NICO SEMINAR: Niall Mangan, Northwestern Engineering "Identifying Models from Data: Traditional and Sparse-Selection Based Approaches"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level, Chambers Hall
Details
Speaker:
Niall Mangan, Assistant Professor of Engineering Sciences and Applied Mathematics, Northwestern University
Title:
Identifying Models from Data: Traditional and Sparse-Selection Based Approaches
Abstract:
Building models for biological, chemical, and physical systems has traditionally relied on domain specific intuition about which interaction and features most strongly influence a system. Statistical methods based in information criteria provide a framework to balance likelihood and model complexity. Recently developed for and applied to dynamical systems, sparse optimization strategies can select a subset of terms from a library that best describe data, automatically interfering model structure. I will discuss my group's application and development of data driven methods for model selection to 1) find simple statistical models to use wastewater surveillance to track the COVID pandemic and 2) recover chaotic systems models from data with hidden variables. I'll briefly discuss current preliminary work and roadblocks in developing new methods for model selection of biological metabolic and regulatory networks.
Speaker Bio:
Niall M. Mangan received the Dual BS degrees in mathematics and physics, with a minor in chemistry, from Clarkson University, Potsdam, NY, USA, in 2008, and the PhD degree in systems biology from Harvard University, Cambridge, MA, USA, in 2013. Dr. Mangan worked as a postdoctoral associate in the Photovoltaics Lab at MIT from 2013-2015 and as an Acting Assistant Professor at the University of Washington, Seattle from 2016-2017. She is currently an Assistant Professor of engineering sciences and applied mathematics with Northwestern University, where she works at the interface of mechanistic modeling, machine learning, and statistical inference. Her group applies these methods to many applications including metabolic and regulatory networks to accelerate engineering.
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/91243465578
Passcode: NICO2024
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, February 14, 2024 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
No classes - Memorial Day - University offices are closed
University Academic Calendar
All Day
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No classes - Memorial Day - University offices are closed
Time
Monday, May 25, 2026
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Calendar
University Academic Calendar
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
University Academic Calendar
All Day
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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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All Day
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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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All Day
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Fall 2026 Classes Begin
Time
Wednesday, September 23, 2026
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University Academic Calendar