BEGIN:VCALENDAR
X-WR-TIMEZONE:America/Chicago
PRODID:-//University of Iowa//Events 1.0//EN
VERSION:2.0
CALSCALE:GREGORIAN
BEGIN:VEVENT
DTSTAMP:20240527T054015Z
DTSTART:20221104T160000
DTEND:20221104T170000
SUMMARY:2022 Graduate Research Symposium Keynote
DESCRIPTION:Keynote Speaker: Bijaya Adhikari \n\nTitle: Spectral Optimization on Dynamic Networks for Diffusion Analysis\n\nAbstract:\n\nWhom should we vaccinate in a large population contact network to mitigate an epidemic outbreak? Which places should one avoid visiting to save oneself from potential infections? How to compute who gets infected in a large network as quickly as possible? These are important questions in epidemiology and public health. Epidemic outbreaks are often modeled as diffusion processes over underlying contact networks. The large size and the dynamic nature of real-world contact networks make the analysis of the diffusion process\, and thus answering the questions above\, a major challenge. In this talk\, we explore provably near-optimal approaches which leverage the spectral radius of contact networks to facilitate diffusion analysis. The first half of the talk will focus on generating smaller representation of a large contact network to speed up existing techniques and the second half will focus on directly answering questions related to vaccination and quarantining in people-location networks.\n\nBio: Bijaya Adhikari is an Assistant Professor in the department of Computer Science at the University of Iowa. He received PhD and master's degree in computer science from Virginia Tech and bachelor's degree from Vistula University in Warsaw\, Poland. He works at the intersection of epidemiology and AI/ML. His research focuses on building new algorithms and deep learning architectures incorporating epidemiological domain knowledge to solve challenging problems in the real world. He has published at both top data mining and domain-specific venues (SIGKDD\, ICDM\, WWW\, SDM\, AAAI\, AAMAS\, PNAS\, PLoS Computational Biology\, PNAS etc). His work in epidemic forecasting has won multiple awards. He is also a member of the interdisciplinary Computational Epidemiology group at the University of Iowa.\n\n2022 Prospective Student Visit Day and Graduate Research Symposium details here\n\n\nhttps://events.uiowa.edu/74188
LOCATION:University Capitol Centre\, 2520D\, 200 South Capitol Street\, Iowa City\, IA 52240
UID:edu.uiowa.events-prod-74188
X-ALT-DESC;FMTTYPE=text/html:**Keynote Speaker:** Bijaya Adhikari

\n\n**Title: **Spectral Optimization on Dynamic Networks for Diffusion Analysis

\n\n**Abstract:**

\n\nWhom should we vaccinate in a large population contact network to mitigate an epidemic outbreak? Which places should one avoid visiting to save oneself from potential infections? How to compute who gets infected in a large network as quickly as possible? These are important questions in epidemiology and public health. Epidemic outbreaks are often modeled as diffusion processes over underlying contact networks. The large size and the dynamic nature of real-world contact networks make the analysis of the diffusion process\, and thus answering the questions above\, a major challenge. In this talk\, we explore provably near-optimal approaches which leverage the spectral radius of contact networks to facilitate diffusion analysis. The first half of the talk will focus on generating smaller representation of a large contact network to speed up existing techniques and the second half will focus on directly answering questions related to vaccination and quarantining in people-location networks.

\n\n**Bio:** Bijaya Adhikari is an Assistant Professor in the department of Computer Science at the University of Iowa. He received PhD and master's degree in computer science from Virginia Tech and bachelor's degree from Vistula University in Warsaw\, Poland. He works at the intersection of epidemiology and AI/ML. His research focuses on building new algorithms and deep learning architectures incorporating epidemiological domain knowledge to solve challenging problems in the real world. He has published at both top data mining and domain-specific venues (SIGKDD\, ICDM\, WWW\, SDM\, AAAI\, AAMAS\, PNAS\, PLoS Computational Biology\, PNAS etc). His work in epidemic forecasting has won multiple awards. He is also a member of the interdisciplinary Computational Epidemiology group at the University of Iowa.

\n\n2022 Prospective Student Visit Day and Graduate Research Symposium details here

\n

https://events.uiowa.edu/74188
END:VEVENT
END:VCALENDAR