# Seminars

## Upcoming Seminars

No upcoming events found.

## Previous Seminars

### Astrophysical Inference with Complex, Stochastic Time Series

### Binary star science with sparse, noisy, and missing data

### Machine Learning and Causal Inference for Heterogeneous Treatment Effects

### Decoding the Milky Way Galaxy

### Sparse matrices in sparse analysis

### Rethinking the Role of Optimization in Learning

### 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.

### 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.

### 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).

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

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. ...