Youngsuk Park

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Ph.D. Candidate
Department of Electrical Engineering, Stanford University
Co-advisors: Stephen Boyd and Jure Leskovec

Office: Parkard 243, Stanford, CA 94305
Email: youngsuk [at] cs [dot] stanford [dot] edu


I defended with “Topics in Convex Optimization for Machine Learning.”

Feb. 2020. Our paper “Structured Policy Iteration for Linear Quadratic Regulator” was submitted to ICML.

Jan. 2020. Our paper “Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize.” was accepted to ICASSP.

Jan. 2020. Our paper “Optimal Operation of a Plug-in Hybrid Vehicle with Battery Thermal and Degradation Model.” was accepted to ACC.

Jan. 2020. Our paper “Linear Convergence of Cyclic SAGA.” was published in Optimization Letter.

Research Interests

  • Machine Learning

  • Optimization

  • Reinforcement Learning and Control

  • Information Theory


  • Adobe Research, Data Science Research Intern, Summer 2019.

    • Reinforcement learning for continuous space task with cloud resource management application.



  • Hyundai Global Forum, 1st-rank Presenter in AI Session, 2018

  • Kwanjeong Graduate Fellowship, 2013–2015

  • Fulbright Fellowship (Declined)