BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:PUBLISH X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VTIMEZONE TZID:Central Standard Time BEGIN:STANDARD DTSTART:16011104T020000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010311T020000 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT CLASS:PUBLIC CREATED:20240209T181813Z DESCRIPTION:Speaker:\n \nNiall Mangan\, Assistant Professor of Engineering Sciences and Applied Mathematics\, Northwestern University\n\nTitle:\n \nI dentifying Models from Data: Traditional and Sparse-Selection Based Approa ches\n\nAbstract:\n \nBuilding models for biological\, chemical\, and phys ical systems has traditionally relied on domain specific intuition about w hich interaction and features most strongly influence a system. Statistica l methods based in information criteria provide a framework to balance lik elihood and model complexity. Recently developed for and applied to dynami cal systems\, sparse optimization strategies can select a subset of terms from a library that best describe data\, automatically interfering model s tructure. I will discuss my group's application and development of data dr iven methods for model selection to 1) find simple statistical models to u se wastewater surveillance to track the COVID pandemic and 2) recover chao tic systems models from data with hidden variables. I'll briefly discuss c urrent preliminary work and roadblocks in developing new methods for model selection of biological metabolic and regulatory networks.\n\n\nSpeaker B io:\nā€‹\nNiall 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 Pro fessor of engineering sciences and applied mathematics with Northwestern U niversity\, where she works at the interface of mechanistic modeling\, mac hine learning\, and statistical inference. Her group applies these methods to many applications including metabolic and regulatory networks to accel erate engineering.\n\n\nLocation:\n \nIn person: Chambers Hall \, 600 Foster Street\, Lower Level\nR emote option: https://northwestern.zoom.us/j/91243465578 \nPasscod e: NICO2024\n \nAbout the Speaker Series:\n \nWednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems a nd data science. It brings together attendees ranging from graduate studen ts to senior faculty who span all of the schools across Northwestern\, fro m applied math to sociology to biology and every discipline in-between. Pl ease visit: https://bit.ly/WedatNICO for information on future speakers.\n \n DTEND;TZID="Central Standard Time":20240214T130000 DTSTAMP:20240209T181813Z DTSTART;TZID="Central Standard Time":20240214T120000 LAST-MODIFIED:20240209T181813Z LOCATION:In person at Chambers Hall\, or remote via Zoom PRIORITY:5 RECURRENCE-ID;TZID="Central Standard Time":20240214T120000 SEQUENCE:0 SUMMARY;LANGUAGE=en-us:Wed@NICO\, Niall Mangan\, 2/14 TRANSP:OPAQUE UID:040000008200E00074C5B7101A82E00800000000202005935749DA01000000000000000 010000000C542FE758110E148AB21372FB34E3B53 X-ALT-DESC;FMTTYPE=text/html:

Speaker:

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Niall Mangan\, Assistant Professor of E ngineering Sciences and Applied Mathematics\, Northwestern University
< br>Title:

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Identifying Models from Data: Traditional and Sparse-Sele ction Based Approaches

Abstract:

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Building models for biologi cal\, chemical\, and physical systems has traditionally relied on domain s pecific intuition about which interaction and features most strongly influ ence 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\, automati cally interfering model structure. I will discuss my group's application a nd development of data driven methods for model selection to 1) find simpl e statistical models to use wastewater surveillance to track the COVID pan demic and 2) recover chaotic systems models from data with hidden variable s. I'll briefly discuss current preliminary work and roadblocks in develop ing new methods for model selection of biological metabolic and regulatory networks.

Speaker Bio:

ā€‹

Niall M. Mangan received the Dual BS degrees in mathema tics 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 an d as an Acting Assistant Professor at the University of Washington\, Seatt le 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 st atistical inference. Her group applies these methods to many applications including metabolic and regulatory networks to accelerate engineering.< br style='mso-special-character:line-break'>

Location:

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In person: Chambers Hall\, 600 Foster Street\, Low er Level
Remote option: https://northwestern.zoo m.us/j/91243465578

Passcode: NICO2024

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About the Speaker Series:

 \;

Wednesdays@NIC O 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 o f 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.

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