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    <title>Lars Ruthotto on Lars Ruthotto | Scientific Computing &amp; AI</title>
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      <title>Math 785R: Deep Generative Modeling</title>
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      <pubDate>Tue, 21 Jan 2025 00:00:00 +0000</pubDate>
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      <description>Mathematical foundations of deep generative models, emphasizing theoretical principles and connections to optimal transport, high-dimensional probability, and dynamical systems.</description>
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      <title>NSF CBMS 2025: Computational Mathematics and AI</title>
      <link>https://www.math.emory.edu/~lruthot/workshops/computational-math-ai-2025/</link>
      <pubDate>Mon, 01 Dec 2025 00:00:00 +0000</pubDate>
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      <description>Short introduction to research topics at the intersection of computational mathematics and artificial intelligence, held in Houston, December 2025, supported by NSF CBMS Award</description>
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      <title>MATH 789R - RTG Seminar on Computational Mathematics for Data Science</title>
      <link>https://www.math.emory.edu/~lruthot/course/math789r-fa21/</link>
      <pubDate>Sun, 04 Oct 2020 21:19:11 +0000</pubDate>
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      <description>In this seminar, we discuss one recent work at the interface of applied mathematics and machine learning with the goal of exposing new research questions.</description>
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      <title>Introduction to Deep Generative Modeling</title>
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      <pubDate>Tue, 06 Apr 2021 21:19:11 +0000</pubDate>
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      <description>Interactive three-hour mini-course held most recently in the  2021 Spring School on Models and Data, University of South Carolina.</description>
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      <title>MATH 789R - Reading Seminar on Mathematics of Machine Learning</title>
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      <pubDate>Sun, 04 Oct 2020 21:19:11 +0000</pubDate>
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      <description>In this seminar, we discuss one recent work at the interface of applied mathematics and machine learning with the goal of exposing new research questions.</description>
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      <title>CS 584 - Numerical Methods for Deep Learning</title>
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      <pubDate>Sun, 04 Oct 2020 21:19:11 +0000</pubDate>
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      <description>This course provides students with the mathematical background needed to analyze and further develop numerical methods at the heart of deep learning.</description>
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      <title>Numerical Methods for Deep Learning</title>
      <link>https://www.math.emory.edu/~lruthot/workshops/pisa/</link>
      <pubDate>Sun, 04 Oct 2020 21:19:11 +0000</pubDate>
      <guid>https://www.math.emory.edu/~lruthot/workshops/pisa/</guid>
      <description>Mini-course most recently held at the Scuola Normale Superiore, Pisa (2019) and previously at the TU Berlin (2017) and TU Chemnitz (2018).</description>
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      <title>MATH 347 - Introduction to Nonlinear Optimization</title>
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      <pubDate>Sun, 04 Oct 2020 21:19:11 +0000</pubDate>
      <guid>https://www.math.emory.edu/~lruthot/course/math347/</guid>
      <description>This advanced undergraduate course introduces nonlinear optimization problems, optimality conditions, and examples from different domains including finance, machine learning, and imaging.</description>
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      <title>MATH 571 - Numerical Optimization</title>
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      <pubDate>Sun, 04 Oct 2020 21:19:11 +0000</pubDate>
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      <description>This course provides students with an overview of state-of-the-art numerical methods for solving both unconstrained and constrained, large-scale optimization problems.</description>
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