Upcoming Events

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Events Archive

Binary star science with sparse, noisy, and missing data

Many open questions in astrophysics hinge on a better understanding of binary star populations (their occurrence rate, orbital parameter distributions, and how these vary with environment).Yet, most of what we know about binary star statistics comes from just a few hundred stars nearest to the sun. Contemporary astronomical surveys have the...
Location: Peyton Auditorium, Room 145
Speaker(s):
Tags: Seminars

Machine Learning and Causal Inference for Heterogeneous Treatment Effects

This talk will review recently developed methods to apply machine learning methods to causal inference problems, including the problems of estimating heterogeneous treatment effects, for example, in A/B testing, as well as in estimating optimal treatment assignment policies.
Speaker(s):
Tags: Seminars

Undergraduate Certificate - Submission of Independent Work Title and Abstract

Deadline for Submission of Independent Work Title and Abstract.

Location: 26 Prospect Ave, Princeton NJ

Decoding the Milky Way Galaxy

Stars residing in the Milky Way halo hold the key to the origin of our Galaxy. The stellar halo has traditionally been inaccessible as it contains only one percent of stars in the Galaxy. However, novel data sets have enabled us to confidently identify thousands of halo stars in a high-dimensional space of stellar positions, velocities and...
Location: Peyton Hall, Room 145
Tags: Seminars

Sparse matrices in sparse analysis

In this talk, I will give two vignettes on the theme of sparse matrices in sparse analysis. The first vignette covers work from compressive sensing in which we want to design sparse matrices (i.e., matrices with few non-zero entries) that we use to (linearly) sense or measure compressible signals.
Location: B205 Engineering Quadrangle
Speaker(s):
Tags: Seminars

Rethinking the Role of Optimization in Learning

In this talk, I will overview our recent progress towards understanding how we learn large capacity machine learning models, especially deep neural networks. In the modern practice of deep learning, many successful models have far more trainable parameters compared to the number of training examples.
Location: B205 Engineering Quadrangle
Speaker(s):
Tags: Seminars

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):

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