Current interests:

 Matrix Optimization Problems, in particular, large scale convex quadratic semidefinite programming
 Efficient algorithms for large scale optimization problems in data science
 Optimization and decision making under uncertainty
Papers and preprints:

 An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for Linear Programming (with Defeng Sun and KimChuan Toh), arXiv:1903.09546, 2019
 On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming (with Liang Chen, Defeng Sun, and KimChuan Toh), arXiv:1803.10803, 2018
 Estimation of Markov chain via rankconstrained likelihood (with Mengdi Wang and Anru Zhang), Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, PMLR 80:30393048, 2018, Supplementary PDF
 On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope (with Defeng Sun and KimChuan Toh), arXiv:1702.05934, Mathematical Programming, in print, 2018
 On efficiently solving the subproblems of a levelset method for fused lasso problems (with Defeng Sun and KimChuan Toh), SIAM Journal on Optimization, 28 (2018), pp. 1842–1866
 A block symmetric GaussSeidel decomposition theorem for convex composite quadratic programming and its applications (with Defeng Sun and KimChuan Toh), Mathematical Programming, in print, arXiv:1703.06629, 2017, Springer Nature SharedIT
 A highly efficient semismooth Newton augmented Lagrangian method for solving Lasso problems (with Defeng Sun and KimChuan Toh), SIAM Journal on Optimization, 28 (2018), pp. 433–458
 QSDPNAL: A twophase augmented Lagrangian method for convex quadratic semidefinite programming (with Defeng Sun and KimChuan Toh), Mathematical Programming Computation, 10 (2018) 703–743, arXiv:1512.08872, Springer Nature SharedIT
 On the convergence of a majorized ADMM for the linearly constrained convex optimization problems of coupled objective functions (with Ying Cui, Defeng Sun, and KimChuan Toh), Journal of Optimization Theory and Applications, 169 (2016), pp. 1013–1041
 A Schur complement based proximal ADMM for convex quadratic conic programming and extensions (with Defeng Sun and KimChuan Toh), Mathematical Programming, 155 (2016), pp. 333–373