Upcoming Events

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Events Archive

Introduction to NumPy with Vineet Bansal, Research Computing Bootcamp

This session covers the basics of NumPy, the package that underlies most scientific computing done in Python. It will explain the NumPy array, the principal data type in the NumPy package, and how it differs from similar Python structures like lists. There will be particular emphasis on understanding the two core features of NumPy arrays –...
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Reproducible Research Reports with R Markdown with Daisy Huang, Research Computing Bootcamp

Do you use LaTeX or Microsoft Word to write your analysis report? Have you ever wished that all your research results (e.g., data analysis, graphs, result discussions) can be included in one place and can be updated effortlessly? Are you tired of all the copying and pasting that you have to do between R and LaTeX/Microsoft Word?
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Intro to Data Analysis Using R w/ Brian Arnold & Andrzej Zuranski (Schmidt DataX), Research Computing Bootcamp

This session in an introduction to data analysis using the R programming language, aimed at people who have ever used R or RStudio before. It will briefly cover different facets of data analysis and their execution using basic R. The style is fairly hands-on, with participants executing the examples on their own laptops alongside the instructors....
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Deep Networks from First Principles

In this talk, we offer an entirely “white box’’ interpretation of deep (convolutional) networks from the perspective of data compression. In particular, we show how modern deep architectures, linear (convolution) operators and nonlinear activations, and parameters of each layer can be derived from the principle of rate reduction (and invariance).
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Breaking the Sample Size Barrier in Statistical Inference and Reinforcement Learning

A proliferation of emerging data science applications require efficient extraction of information from complex data. The unprecedented scale of relevant features, however, often overwhelms the volume of available samples, which dramatically complicates statistical inference and decision making.

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HEE Seminar- Taylor Faucett-UCI-Physics Learning from Machines Learning

Machine Learning methods are extremely powerful but often function as black-box problem solvers, providing improved performance at the expense of clarity. Our work describes a new machine learning approach which translates the strategy of a deep neural network into simple functions that are meaningful and intelligible to the physicist, without...

Location: Zoom
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Using Code Ocean in the Sciences and Engineering: Bringing computational reproducibility to your research collaborations

Computational analyses are playing an increasingly central role in research. However, many researchers have not received training in best practices and tools for reproducibly managing and sharing their code and data. This is a step-by-step, practical workshop on managing your research code and data for computationally reproducible collaboration...

Conditional Sampling with Monotone GANs: Modifying Generative Models to Solve Inverse Problems

The One World Seminar Series on the Mathematics of Machine Learning is an online platform for research seminars, workshops and seasonal schools in theoretical machine learning.

Location: https://www.oneworldml.org/home
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Metamaterials Design and Manufacturing: Perspectives From Biology and Artificial Intelligence

After billions of years of evolution, it is no surprise that biological materials are treated as an invaluable source of inspiration in the search for new materials. Additionally, developments in computation spurred the fourth paradigm of materials discovery and design using artificial intelligence. Our research aims to advance design and...

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