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Astrophysical Inference with Complex, Stochastic Time Series

Astrophysical Inference with Complex, Stochastic Time Series
Tue, Feb 26, 2019, 3:00 pm to 4:00 pm
Astronomical time series—measurements of a celestial source’s brightness as a function of time—are key to probing the physical processes governing the behaviour of these sources on a large range of scales, from small asteroids in our solar system, to supermassive black holes at the centres of galaxies. Modern time domain surveys like the Zwicky...
Speaker(s):
Daniela Huppenkothen
DIRAC Institute and eScience Institute, University of Washington

Binary star science with sparse, noisy, and missing data

Binary star science with sparse, noisy, and missing data
Fri, Feb 22, 2019, 2:00 pm to 3:00 pm
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...
Speaker(s):
Adrian Price-Whelan
Lyman Spitzer, Jr. Postdoctoral Fellow

Machine Learning and Causal Inference for Heterogeneous Treatment Effects

Machine Learning and Causal Inference for Heterogeneous Treatment Effects
Fri, Feb 22, 2019, 12:00 pm to 1:00 pm
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):
Susan Athey
The Economics of Technology Professor, Stanford Graduate School of Business

Decoding the Milky Way Galaxy

Decoding the Milky Way Galaxy
Fri, Feb 15, 2019, 2:00 pm to 3:00 pm
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...
Speaker(s):

Sparse matrices in sparse analysis

Sparse matrices in sparse analysis
Wed, Feb 13, 2019, 4:30 pm
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.
Speaker(s):
Anna Gilbert
University of Michigan
Sparse matrices in sparse analysis

Rethinking the Role of Optimization in Learning

Rethinking the Role of Optimization in Learning
Mon, Feb 11, 2019, 4:30 pm
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.
Speaker(s):
Suriya Gunasekar
Toyota Technical Institute

Astrophysics as a Testbed for Statistical Method Development

Astrophysics as a Testbed for Statistical Method Development
Wed, Feb 6, 2019, 11:00 am

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.

Speaker(s):
Dan Foreman-Mackey
Center for Computational Astrophysics
Astrophysics as a Testbed for Statistical Method Development

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

The Many Faces of Regularization: from Signal Recovery to Online Algorithms
Wed, Jan 16, 2019, 4:30 pm

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.

Speaker(s):
Maryam Fazel
University of Washington
The Many Faces of Regularization: from Signal Recovery to Online Algorithms

Machine Learning and the Physical World

Machine Learning and the Physical World
Mon, Dec 10, 2018, 4:00 pm

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).

Speaker(s):
Neil Lawrence
University of Sheffield & Amazon
Machine Learning and the Physical World

Seminar: Latent statistical structure in large-scale neural data: how to find it, and when to believe it

Seminar: Latent statistical structure in large-scale neural data: how to find it, and when to believe it
Tue, Oct 16, 2018, 4:30 pm

One central challenge in neuroscience is to understand how neural populations represent and produce the remarkable computational abilities of our brains.  Indeed, neuroscientists increasingly form scientific hypotheses that can only be studied at the level of the neural population, and exciting new large-scale datasets have followed. ...

Speaker(s):

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