Graduate

Forum on Global Public Health: Plastic pollution, the environment, and public health

Presented by Aaroogya Foundation and Silicon Valley Global Health in cooperation with the US-Asia Technology Management Center at Stanford University, Pinga Healthtech, and Hockey India

-- details not available at time of publishing -- 

-- details not available at time of publishing -- 

-- details not available at time of publishing -- 

Algorithms often have tunable parameters that have a considerable impact on their runtime and solution quality. A growing body of research has demonstrated that data-driven algorithm design can lead to significant gains in runtime and solution quality. Data-driven algorithm design uses a training set of problem instances sampled from an unknown, application-specific distribution and returns a parameter setting with strong average performance on the training set. We provide a broadly applicable theory for deriving generalization guarantees for data-driven algorithm design, which bound the difference between the algorithm's expected performance and its average performance over the training set.

The challenge is that for many combinatorial algorithms, performance is a volatile function of the parameters: slightly perturbing the parameters can cause a cascade of changes in the algorithm’s behavior. Prior research has proved generalization bounds by employing case-by-case analyses of parameterized greedy algorithms, clustering algorithms, integer programming algorithms, and selling mechanisms. We uncover a unifying structure which we use to prove very general guarantees, yet we recover the bounds from prior research. Our guarantees apply whenever an algorithm's performance is a piecewise-constant, -linear, or — more generally — piecewise-structured function of its parameters. As we demonstrate, our theory also implies novel bounds for dynamic programming algorithms used in computational biology and voting mechanisms from economics.

This talk is based on joint work with Nina Balcan, Dan DeBlasio, Travis Dick, Carl Kingsford, and Tuomas Sandholm.

Mobility: Asia Moves Forward in the 4th Industrial Revolution

The shift of IT to the cloud and the growth of new tools such as AI and edge computing have already caused major advances in mobility; for example, smartphones now serve as platforms for banking, education, ride hailing, ID authentication, wellness monitoring, etc. This series examines new technology-and-business solutions that may shape the future of mobility, e.g. smart city infrastructure for autonomous vehicles, intelligent prosthetics for physical mobility, autonomous delivery robots, new propulsion and navigation systems, new applications of mobile IT devices, and more.

The series begins September 23, 2021 and continues weekly until December 2, 2021. Seminars will be held online via Zoom and in-person for students, Thursdays, 5:30 PM - 7:00 PM, with informal networking following until approximately 7:30 PM (PST).

Last day of classes, Winter Quarter 2021-22

Winter quarter begins

Last day of classes, Autumn quarter 2021-22

Dates are subject to change at the discretion of the University.

First day of classes, Autumn Quarter 2021-22

Dates are subject to change at the discretion of the University.