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Yixu Chen

Yixu Chen

Princeton University · Art and Archaeology

Active 1998–2025

h-index36
Citations5.6k
Papers244120 last 5y
Funding$2.5M1 active

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

Yixu Eliza Chen is a Ph.D. candidate specializing in the art and visual culture of late imperial and modern China. Her research interests include transmediality, reproduction, antiquarianism, and the visual dimensions of knowledge production. Her dissertation examines how ink rubbing—a medium long valued for reproducing and transmitting antiquities—evolved during the nineteenth and early twentieth centuries into a transmedial site of artistic and epistemic change. This evolution is explored through the intersection of rubbing practices with photography, photomechanical printing, and graphic design. Drawing on approaches from media studies and the history of science, her research investigates how media technologies reshaped ways of seeing and knowing the past. Her work considers the dialogue between literati and popular visual cultures and the transcultural flows of people, images, and ideas, highlighting the impact of technological and cultural exchanges on visual and epistemic practices.

Research topics

  • Artificial Intelligence
  • Computer Science
  • Mathematics
  • Algorithm
  • Statistics
  • Mathematical optimization

Selected publications

  • Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization

    SIAM Journal on Optimization · 2020-01-01 · 97 citations

    articleOpen access1st author

    This paper studies noisy low-rank matrix completion: given partial and noisy entries of a large low-rank matrix, the goal is to estimate the underlying matrix faithfully and efficiently. Arguably one of the most popular paradigms to tackle this problem is convex relaxation, which achieves remarkable efficacy in practice. However, the theoretical support of this approach is still far from optimal in the noisy setting, falling short of explaining its empirical success. We make progress towards dem…

    PublisherDOI

  • Wearable Microphone Jamming

    2020-04-21 · 60 citations

    article1st author

    We engineered a wearable microphone jammer that is capable of disabling microphones in its user's surroundings, including hidden microphones. Our device is based on a recent exploit that leverages the fact that when exposed to ultrasonic noise, commodity microphones will leak the noise into the audible range.

    PublisherDOI

  • MagNet: A Machine Learning Framework for Magnetic Core Loss Modeling

    2020-11-09 · 56 citations

    article

    This paper presents a two-stage machine learning framework – MagNet – for magnetic core loss modeling. The first stage of MagNet is a waveform transformation network, which generates 2-D images (tensors) and extracts both the frequency and time domain features from the magnetic excitation waveforms; the second stage of MagNet is a convolutional neural network (CNN), which is trained to recognize the patterns in the 2-D images and predict the core loss based on regression. MagNet is supported by…

    PublisherDOI

  • Using LLMs for Automated Privacy Policy Analysis: Prompt Engineering, Fine-Tuning and Explainability

    ArXiv.org · 2025-03-16 · 1 citations

    preprintOpen access1st authorCorresponding

    Privacy policies are widely used by digital services and often required for legal purposes. Many machine learning based classifiers have been developed to automate detection of different concepts in a given privacy policy, which can help facilitate other automated tasks such as producing a more reader-friendly summary and detecting legal compliance issues. Despite the successful applications of large language models (LLMs) to many NLP tasks in various domains, there is very little work studying…

    PublisherOA PDFDOI

  • Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods

    SIAM Journal on Optimization · 2025-06-18 · 1 citations

    articleSenior author
    PublisherDOI

Recent grants

Frequent coauthors

Education

  • Postdoc, Statistics

    Stanford University

    2017

  • Ph.D, Electrical Engineering

    Stanford University

    2015

  • Ph.D. minor, Management Science and Engineering

    Stanford University

    2015

  • M.A., Statistics

    Stanford University

    2013

  • M.S., Electrical and Computer Engineering

    University of Texas at Austin

    2010

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