Upcoming Seminars

AI Journey with Intel Workshop

Tue, Sep 17, 2019, 12:00 pm

AI Journey with Intel Workshop

Tuesday, September 17, 12:00-2:00 pm

399 Julis Romo Rabinowitz Building

[Lunch will be provided. RSVP now!]


Regina Y. Liu

Beyond Supervised Learning for Biomedical Imaging

Wed, Oct 2, 2019, 4:30 pm
Many biomedical imaging tasks, such as 3D reconstruction, denoising, detection, registration, and segmentation, are ill-posed inverse problems. In this talk, I will present a flexible machine learning-based framework that has allowed us to derive efficient solutions for a variety of such problems, without relying on heavy supervision. I will...

Control with Learning On the Fly: First Toy Problems

Thu, Oct 10, 2019, 4:30 pm
How can we control a system without knowing beforehand what the controls do? In particular, how should we balance the imperatives to "explore" (learn what the controls do) and "exploit" (use what we've learned so far to make the system do what we want)? We won't have enough data to apply deep learning. The talk poses several toy problems and...

Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands

Mon, Oct 14, 2019, 12:30 pm
We study deep neural networks and their use in semiparametric inference. We prove valid inference after first-step estimation with deep learning, a result new to the literature. We provide new rates of convergence for deep feedforward neural nets and, because our rates are sufficiently fast (in some cases minimax optimal), obtain valid...

TensorFlow & PyTorch User Group Talks [Two 20-minute talks]

Thu, Oct 17, 2019, 4:30 pm

Meisam Razaviyayn

Thu, Oct 17, 2019, 4:30 pm

Bio: I am an assistant professor at the department of Industrial and Systems Engineering at the University of Southern California. Prior to joining USC, I was a postdoctoral research fellow in the Department of Electrical Engineering at Stanford University working with Professor David Tse.


Previous Seminars

TensorFlow and PyTorch User Group

Thu, Sep 12, 2019, 4:30 pm

TensorFlow and PyTorch User Group

JAX: Accelerated machine-learning research via composable function transformations in Python
Thursday, September 12, 4:30-5:30 pm, 120 Lewis Science Library
Peter Hawkins, Google AI Princeton


Multiscale Model Reduction in Physics with Deep Networks

Mon, May 13, 2019, 4:15 pm
Approximating high-dimensional functionals with low-dimensional models is a central issue of machine learning and physics. This talk shows deep convolutional neural network architectures take advantage of scale separation, symmetries and sparse representations. We introduce simplified architectures which can be analyzed mathematically. Scale...

Inherent Trade-Offs in Algorithmic Fairness

Fri, Mar 29, 2019, 12:00 pm to 1:00 pm
Recent discussion in the public sphere about classification by algorithms has involved tension between competing notions of what it means for such a classification to be fair to different groups. We consider several of the key fairness conditions that lie at the heart of these debates, and discuss recent research establishing inherent trade-offs...

Physics-informed Machine Learning For Climate, Urban, And Environmental Sciences

Thu, Mar 14, 2019, 12:30 pm to 1:30 pm
At the confluence of scientific simulation and modern machine learning there exists an opportunity to develop a “middle path” that leverages the strengths of both approaches to build machine-learning emulators of numerical simulation models. From the perspective of machine learning, incorporating simulation data may significantly reduce the need...

Cosmology with galaxy surveys: from precision to accuracy with data-driven models

Tue, Mar 12, 2019, 3:00 pm to 4:00 pm
Immense surveys of the night sky are being conducted to test the origins and evolutions of the Universe as a whole, using distant galaxies and quasars (bright galaxies with active supermassive black holes at their center). This requires modeling the spatial and spectral statistics of those sources in detail using millions of noisy observations,...

Fitting Convex Sets to Data

Mon, Mar 11, 2019, 4:30 pm to 5:30 pm
A number of problems in signal processing may be viewed conceptually as fitting a convex set to data.  In vision and learning, the task of identifying a collection of features or atoms that provide a concise description of a dataset has been widely studied under the title of dictionary learning or sparse coding.  In convex-geometric terms, this...

The Complexities of Astronomical Data

Fri, Mar 8, 2019, 2:00 pm to 3:00 pm
The analysis of astronomical data set reveals an astonishing diversity of astrophysical processes but also a dizzying array of observational complications. I will introduce several approaches to deal with complications like missing data, non-linear error propagation, and overlapping objects. I will concentrate on proximal optimization algorithms...

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...

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...

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.