Siyi Tang

Siyi Tang

PhD Candidate in Electrical Engineering

Stanford University

Biography

I am a PhD candidate in Electrical Engineering at Stanford University advised by Prof. Daniel Rubin. My research interests lie in the intersection of machine learning and medicine. I develop machine learning methods for medical applications by leveraging the important characteristics in medical data. I work closely with clinical experts from the Stanford School of Medicine to better understand the clinical needs. Prior to Stanford, I was fortunate to be advised by Prof. Thomas Yeo at National University of Singapore.

Interests

  • Machine Learning in Healthcare
  • Biomedical Data Science
  • Deep Learning

Education

  • PhD in Electrical Engineering, 2018 - Present

    Stanford University

  • BEng in Electrical Engineering (Highest Distinction Honor), 2016

    National University of Singapore

Projects

Data Valuation Using Shapley Values for Medical Imaging Data

We quantify the value of data in a large public chest X-ray dataset using data Shapley values.

Modeling EEG Using A Graph Neural Network

We use a graph neural network to model the spatiotemporal relationship in Electroencephalography (EEG) signals.

Recent Publications

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Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology