• March 14, 2017: Together with my student, Tianxiao Sun, we have posted a new paper “Generalized self-concordant functions: A recipe for Newton-type methods” on arxiv.org (https://arxiv.org/pdf/1703.04599.pdf [PDF]).
  • In Spring 2017, I am teaching a special topic course for graduate students at our department
    • Title: Selected topics in modern convex optimization: theory, algorithms and applications.
    • The tentative syllabus is available at [PDF].


Department of Statistics and Operations Research    
333 Hanes Hall CB #3260
The University of North Carolina at Chapel Hill     
Chapel Hill, NC 27599
E-mail: quoctdATemailDOTuncDOTedu
Phone: +1-919-843-6023

About me

I am an assistant professor at the Department of Statistics and Operations Research, where I joined in July 2015. I was a postdoctoral researcher at Laboratory for Information and Inference Systems (LIONS), École Polytechnique Fédérale de Lausanne (EPFL),  Switzerland from November 2012 to June 2015. I completed my PhD in Optimization in Engineering in November 2012 at the Department of Electrical Engineering (ESAT) and Optimization in Engineering Center (OPTEC) under the supervision of Prof. Moritz Diehl.


My research

My research is on numerical optimization: theory, algorithms, and applications. I have been working on equilibrium problems and variational inequalities; sequential convex programming (SCP) for nonlinear optimization and applications in model predictive control, optimal control, and static output feedback control; and first order and second order decomposition methods for [large-scale] convex optimization.  My current research focuses on efficient methods for convex optimization and matrix optimization, with applications in signal/image processing, statistics, and machine learning.

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