Our third and final session will have two presentations “Prediction of hospital demand from the COVID-19 death curve: IHME Model” and “Molecular epidemiology of SARS-Cov-2: what…

Principal components analysis (PCA) is a well-known technique for approximating a data set represented by a matrix by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets…

Friday, May 2
2:20 p.m. – Presentation – 253 Rhodes Hall
Philippe Rigollet
The statistical price to pay for computational efficiency in sparse PCA
Computational limitations of…

Tuesday, February 4, 2014 at 4:15pm
Upson Hall, B17
ORIE Colloquium: John Duchi (UC Berkeley) - Machine Learning: a Discipline of Resource Tradeoffs
Joint colloquium with Computer Science.
How…

Many operational problems in data-rich environments can be characterized by three primitives: data on uncertain quantities of interest such as simultaneous demands, concurrent auxiliary data such as…

Computing reliable solutions to inverse problems is important in many applications such as biomedical imaging, computer graphics, and security. Regularization by incorporating prior knowledge is…

ORIE Colloquium: Guy Lebanon (Georgia Tech) - Stochastic m-Estimators and the Tradeoff Between Statistical Accuracy and Computational Complexity
Tuesday, February 5, 2013 at 4:15pm
Frank H. T.…

Friday, September 20, 2013 at 12:00pm
Frank H. T. Rhodes Hall, 253
Ezra's Round Table/Systems Seminar: Timothy Simpson (Penn State) - Many Objective Visual Analytics: The Power and Pitfalls of…

Ezra's Round Table/Systems Engineering Seminar: Joel Cutcher-Gershenfeld (Illinois) - Stakeholder Alignment in Complex Systems: First Principles for Robust 21st Century Institutions
Friday,…

Kenneth Regan
Department of Computer Science and Engineering
University at Buffalo
Friday, November 16, 2012
Skill Inference and Chess Cheating Detection from Big Data
We describe a…