Upcoming Events

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Events Archive

Astrophysics as a Testbed for Statistical Method Development

There have been many efforts to apply methods from machine learning and statistics to make discoveries in astrophysics and throughout the physical sciences. While it is clear that the use of these methods has advanced our science goals, I will argue that these collaborations can also advance research in machine learning.

Location: Jadwin Hall Room 407, Princeton Center for Theoretical Science (PCTS)
Speaker(s):
Tags: Seminars

The Many Faces of Regularization: from Signal Recovery to Online Algorithms

In optimization, regularization plays several distinct roles. In the first part of the talk, we consider sample-efficient recovery of signals with low-dimensional structure, which is ill-posed without regularization.

Location: B205 Engineering Quadrangle
Speaker(s):
Tags: Seminars

Barks, Bubbles and Brownies

Location: CSML Lounge
Tags: Dogs

Disruption Prediction in Tokamak Fusion Reactors via Deep Learning at Scale

The prediction and avoidance of large-scale plasma instabilities called “disruptions” is a crucial step towards successful power generation from magnetic confinement fusion in tokamaks.

Location: CSML Classroom 103, 26 Prospect Ave.
Speaker(s):

Machine Learning and the Physical World

Machine learning is a data driven endeavor, but real world systems are physical and mechanistic. In this talk we will review approaches to integrating machine learning with real world systems. Our focus will be on emulation (otherwise known as surrogate modeling).

Location: Julius Romo Rabinowitz 399
Speaker(s):

Guest Lecture: Algorithms of Oppression: How Search Engines Reinforce Racism

In Algorithms of Oppression: How Search Engines Reinforce Racism Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities.

Location: 010 Pine

Colloquium: Data-driven Models for the Physical Sciences

There is immense hype, and immense promise, in machine learning for physics and astronomy. I use the case of stellar astrophysics as an example area in which to explore these ideas. It is an ideal field, because there are both very large data sets and incredibly detailed and successful physical models. And yet these models are nonetheless...

Location: Peyton Hall , Room 145
Speaker(s):

Colloquium: Machine Learning at Facebook

Machine intelligence for processing big data sets is big business. A statistical mathematician's point of view has led to (1) effective large-scale principal component analysis and singular value decomposition, and (2) some theoretical foundations for convolutional networks (convolutional networks underpin the recent revolution in artificial...

Location: 214 Fine Hall
Speaker(s):

Graduate Certificate Information Session

Open to anyone interested in learning more about CSML's Graduate Certificate Program.  CSML Director Peter Ramadge will be available to answer questions.

Location: 26 Prospect Ave, Princeton NJ

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