Yiwen Yuan

Yiwen Yuan

Palo Alto, California, United States
705 followers 500+ connections

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  • Kumo.AI Graphic

    Kumo.AI

    Mountain View, California, United States

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    Mountain View, California, United States

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    Palo Alto, California, United States

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    Greater Pittsburgh Area

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    Greater Seattle Area

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    Greater Pittsburgh Area

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    Shanghai

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    Greater Pittsburgh Area

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    Shanghai City, China

Bildung

  • Carnegie Mellon University Graphic

    Carnegie Mellon University

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    Activities and Societies: Research based master. Advised by Prof. Fei Fang

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    Activities and Societies: Classical Guitar Ensemble, Photography Club

Publications

Courses

  • Advanced Deep Learning

    10707

  • Algorithm Analysis

    15451

  • Chamber Music: Guitar

    57-232

  • Convex Optimization

    10725

  • Great Practical Ideas in Computer Science

    15-131

  • Intro to Machine Learning

    10701

  • Introduction to Programming

    15-112

  • Logical Foundations of Cyber-Physical Systems

    15-824

  • Machine Learning with Large Datasets

    15405

  • Mathematical Foundation for Computer Science

    15-151

  • Matrix and Linear Algebra

    21-241

  • Modern Regression

    36401

  • Parallel Computer Architecture and Programming

    15418

  • Principles of Functional Programming

    15-150

  • Principles of Imperative Computation

    15-122

  • Probabilistical Graphical Model

    10708

  • Probability and Random Process

    36-217

Projects

  • PyTorch Frame

    I co-created pytorch-frame(https://github.com/pyg-team/pytorch-frame), a tabular deep learning library for PyTorch.

  • Using Deep Kernels in Time Varying Networks for Reverse-Engineering of Gene Interaction

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    This is my course project for 10-708 Probabilistical Graphical Model taught by Prof. Eric Xing.
    I led a team of three in solving the task of reverse engineering the gene interaction of Drosophila. We used pairwise Markov Random Field to model the time-varying interaction between 588 genes during 66 time steps. In particular, deep kernels using logistic regression and LSTM are used to compare to the results of non-parametric RBF kernel for weighting the time steps. Our results have shown…

    This is my course project for 10-708 Probabilistical Graphical Model taught by Prof. Eric Xing.
    I led a team of three in solving the task of reverse engineering the gene interaction of Drosophila. We used pairwise Markov Random Field to model the time-varying interaction between 588 genes during 66 time steps. In particular, deep kernels using logistic regression and LSTM are used to compare to the results of non-parametric RBF kernel for weighting the time steps. Our results have shown that deep kernels can achieve faster convergence than the non-parametric RBF kernel.

    See project

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