Dogyoon Song

Dogyoon Song

Ph.D. Student in EECS

Massachusetts Institue of Technology

About Me

I am a Ph.D. student at MIT LIDS. I am fortunate to be advised by Prof. Pablo Parrilo and Prof. Devavrat Shah. My research is supported by Samsung Scholarship.

My research interests revolve around geometric understanding of statistical modeling and inference as well as the associated optimization problems.


  • Optimization
  • Statistical Modeling & Inference
  • Machine Learning


  • Ph.D. in EECS (expected), 2021

    Massachusetts Institute of Technology

  • S.M. in EECS, 2016

    Massachusetts Institute of Technology

  • B.S. in ECE/Math/Physics and B.A. in Economics, 2013

    Seoul National University

Unpublished Works

The Average Hardness of Approximating the PSD Cone Measured in the Gaussian Width

Dogyoon Song, Pablo Parrilo (Working paper)

Learning RUMs: Reducing Mixture to Single Component via PCA [PDF]

Devavrat Shah, Dogyoon Song (ArXiv preprint)

Deconvolution with Unknown Error Distribution Interpreted as Blind Isotonic Regression [PDF]

Devavrat Shah, Dogyoon Song (ArXiv preprint)

Awards & Honors

[2014 - 2019] Samsung Scholar
[2017 - 2018] Hewlett Packard Fellowship, MIT
[2015 - 2016] Siebel Scholar
[2014 - 2015] Advanced Television and Signal Processing Fellowship, MIT


I was a TA for the following courses:

MIT 6.256 Algebraic Techniques and Semidefinite Optimization (Spring 2019)
  • graduate-level research-oriented course focused on algebraic and computational techniques for polynomial optimization problems
MIT 6.437 Inference and Information (Spring 2018)
  • graduate-level introduction to the principles of statistical inference with an emphasis on information theoretic perspectives
MIT 6.252 Nonlinear Programming (Spring 2017)
  • graduate-level introduction to the fundamentals of nonlinear optimization theory and methods
SNU 033.015 Engineering Mathematics 2 (Spring 2014)
  • undergraduate-level introduction to mathematical tools for students in engineering
SNU 033.014 Engineering Mathematics 1 (Fall 2013)
  • undergraduate-level introduction to mathematical tools for students in engineering