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. I am also working on proximal interior-point methods for convex constrained programming with applications in conic programming and SDP relaxations.
My publications and working papers can be found HERE.
I am still looking for new Ph.D. candidates to work with me at STAT & OR – UNC.
Currently, I have two Ph.D. students:
- Sun Tianxiao (Ph.D. student, co-advisor with Shu Lu)
- Zhu Melody (Ph.D. student, co-advisor with Gabor Pataki)
Master students: 1 master student (Aditya Balaram, finished in May 2017).
- NSF grant No. 161984: Efficient Methods for Large-Scale Self-Concordant Convex Minimization (July 1, 2016 – June 30, 2019).
- UNC Junior Faculty Development Award (Jan. 1, 2016-Dec. 31, 2016).