Seminars

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Previous Seminars

Hydrological modeling in the era of big data and artificial intelligence

Wed, Oct 14, 2020, 8:00 pm
Nowadays, all sorts of sensors, from ground to space, collect a huge volume of data about the Earth. Recent advances in artificial intelligence (AI) provide unprecedented opportunities for data-driven hydrological modeling using such “Big Earth Data”. However, many critical issues remain to be addressed. For example, there lacks efficient...
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Geometric Insights into Spectral Clustering by Graph Laplacian Embeddings

Wed, Sep 23, 2020, 12:00 pm

We present new theoretical results for procedures identifying coarse structures in a given data set by means of appropriate spectral embeddings. We combine ideas from spectral geometry, metastability, optimal transport, and spectral analysis of weighted graph Laplacians to describe the embedding geometry.

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Towards a Secure Collaborative Learning Platform

Tue, Sep 22, 2020, 12:30 pm
Multiple organizations often wish to aggregate their sensitive data and learn from it, but they cannot do so because they cannot share their data. For example, banks wish to run joint anti-money laundering algorithms over their aggregate transaction data because criminals hide their traces across different banks. Bio: Raluca Ada Popa is an...
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Analysis of Gradient Descent on Wide Two-Layer ReLU Neural Networks

Wed, Aug 26, 2020, 12:00 pm

In this talk, we propose an analysis of gradient descent on wide two-layer ReLU neural networks that leads to sharp characterizations of the learned predictor. The main idea is to study the dynamics when the width of the hidden layer goes to infinity, which is a Wasserstein gradient flow.

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Uniform Error Estimates for the Lanczos Method

Mon, Aug 24, 2020, 1:30 pm

Abstract:            The computation of extremal eigenvalues of large, sparse matrices has proven to be one of the most important problems in numerical linear algebra.

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A Few Thoughts on Deep Network Approximation

Wed, Aug 12, 2020, 12:00 pm

Deep network approximation is a powerful tool of function approximation via composition. We will present a few new thoughts on deep network approximation from the point of view of scientific computing in practice: given an arbitrary width and depth of neural networks, what is the optimal approximation rate of various function...

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Data Wrangling: How to Keep Your Data Workflows Orderly and Efficient

Thu, Jul 30, 2020, 12:00 pm

This webinar will provide several practical considerations to help you better manage your research data between the points of collection and analysis. We will review the principles of open research and cover best practices for documentation and metadata generation amidst collation, aggregation, and cleaning tasks.

Tradeoffs between Robustness and Accuracy

Wed, Jul 29, 2020, 12:00 pm

Standard machine learning produces models that are highly accurate on average but that degrade dramatically when the test distribtion deviates from the training distribution. While one can train robust models, this often comes at the expense of standard accuracy (on the training distribution).

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Thematic Day on the Mean Field Training of Deep Neural Networks

Sat, Jul 25, 2020, 12:00 pm to 3:00 pm

12pm: Roberto I. Oliveira – TBA 

1pm: Konstantinos Spiliopoulos  - Mean field limits of neural networks: typical behavior and fluctuations

2pm: Huy Tuan Pham - A general framework for the mean field limit of multilayer neural networks

Managing Research Data

Thu, Jul 23, 2020, 12:00 pm

This webinar will go over tips on how to keep track of your data files more efficiently, better organize your data files, and how to manage your data, code and other research materials, to save yourself headaches down the road.

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