Events
Upcoming Events
Rising Stars! - CS Colloquium
Interplay between topics from math with such neighboring areas as physics, statistics, finance, and engineering.
Speaker: Palle Jorgensen, Dept. of Mathematics
Past Events
Constrained Optimization Methods for Machine Learning with Fairness Constraints
Speaker: Qihang Lin, Dept. of Business Analytics
The University of Iowa Computing Conference (UICC) 2023
"The UICC is hosted by the students and for the students to promote computing as a science and a profession."
Date: Feb. 24-25
You can learn about this year's speakers and schedule below and on ACM@UIOWA site!
Check out past conferences! [ 2016, 2017, 2018, 2019, 2020, 2021, 2022]
Schedule Friday, Feb. 24, 2023 5:30pm Talk 1 (Keynote): Shambaugh, Main Library Joe Kearney 7:15pm Activity + Food: (Adler Journalism Building Rotunda + AJB E120 + AJB E126) S...Multiscale Methods in Numerical Analysis
Speaker: Wayne Polyzou, Dept. of Physics and Astronomy
Exploring quantum physics with quantum computers
Speaker: Yannick Meurice, Dept. of Physics and Astronomy
Introduction to scientific machine learning
Speaker: Xueyu Zhu, Dept. of Mathematics
Insights from Mathematical Modeling of Infectious Diseases
Speaker: Herbert Hethcote Emeritus Professor of Mathematics, University of Iowa
Number Theory and its applications
Speaker: Yangbo Ye, Dept. of Mathematics
Attractor-like dynamics extracted from brain recordings underlie bistable perception in auditory streaming
Speaker: Rodica Curtu, Dept. of Mathematics
Knotted Proteins
Speaker: Isabel Darcy, Dept. of Mathematics
CS Colloquium - Probabilistic machine learning for predictive models of mobile health data: a use case on menstrual cycle length prediction
Iñigo Urteaga
AbstractMobile health (mHealth) apps, such as menstrual trackers, provide a rich source of self-reported observations: they provide day-to-day health indicators and behaviors, which can help shed light onto an individual's wellness and health over time. However, self-tracked data collected via mHealth apps have questionable reliability, as they hinge on user adherence. Because mHealth app users may skip tracking relevant health information, disentangling physiological...