Quoc Tran-Dinh

picture
 

Research interests:

  • Theory and Algorithms for Convex Optimization
  • Convex Optimization in Machine Learning and Compressive Sensing
  • Sequential Convex Programming (SCP)
  • Parametric and Online Optimization
  • Numerical Methods for Variational Inequality and Equilibrium Problems 

Contact Address 

My personal webpage: www.trandinhquoc.com

Biography 

 – 2001: Bsc  in Applied Mathematics and Informatics, Vietnam National University, Hanoi, Vietnam.

– 2003: Msc in Computer Science, Vietnam National University, Hanoi, Vietnam.

– 2009-2012: PhD in Electrical Engineering, Department of Electrical Engineering and Optimization in Engineering Center (OPTEC), KU Leuven, Belgium.

– 11/2012-9/2015: Postdoc researcher at Laboratory for Information and Inference Systems, EPFL, Lausanne, Switzerland.

– As of 10/2015: an assistant professor at the Departement of Statistics and Operations Research (University of North Carolina at Chapel Hill (UNC), North Carolina, USA.


Quoc Tran-Dinh; A. Kyrillidis; V. Cevher : A Single-Phase, Proximal Path-Following Framework; Mathematics Of Operations Research. 2018-11-01. DOI : 10.1287/moor.2017.0907.
Q. Tran Dinh; O. Fercoq; V. Cevher : A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization; SIAM Journal on Optimization. 2018-01-11. DOI : 10.1137/16M1093094.
Q. Tran Dinh; V. Cevher : Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization; Large-Scale and Distributed Optimization; Springer, 2018.
T. T. H. Pham; P. Rossi; H. D. K. Dinh; N. T. A. Pham; P. A. Tran et al. : Analysis of antibiotic multi-resistant bacteria and resistance genes in the effluent of an intensive shrimp farm (Long An, Vietnam); Journal of Environmental Management. 2018. DOI : 10.1016/j.jenvman.2018.02.089.
A. Alacaoglu; Q. Tran-Dinh; O. Fercoq; V. Cevher : Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization. 2017. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, December 4-9, 2017.
Q. Tran Dinh; A. Kyrillidis; V. Cevher : A single-phase, proximal path-following framework; Mathematics of Operations Research. 2017.
A. Kyrillidis; B. Bah; R. Hasheminezhad; Q. Tran Dinh; L. Baldassarre et al. : Convex block-sparse linear regression with expanders - provably. 2016. The 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016), Cadiz, Spain, May 7-11, 2016.
G. Odor; Y.-H. Li; A. Yurtsever; Y.-P. Hsieh; Q. Tran Dinh et al. : Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval. 2016. 41st IEEE International Conference on Acoustics, Speech and Signal Processing. p. 6230-6234.
Q. Tran Dinh; Y.-H. Li; V. Cevher : Composite convex minimization involving self-concordant-like cost functions. 2015. Modelling, Computation and Optimization in Information Systems and Management Sciences (MCO 2015), Metz, France, May 11-13, 2015.
Q. Tran Dinh; V. Cevher : An optimal first-order primal-dual gap reduction framework for constrained convex optimization. 2015.
Q. Tran Dinh; V. Cevher : Splitting the Smoothed Primal-Dual Gap: Optimal Alternating Direction Methods. 2015.
B. Gözcü; L. Baldassarre; Q. Tran Dinh; C. Aprile; V. Cevher : A Primal-dual Framework For Mixtures Of Regularisers. 2015. 23rd European Signal Processing Conference (EUSIPCO 2015), Nice, France, August 31 - September 4 2015.
Q. Tran Dinh; Y.-H. Li; V. Cevher : Composite convex minimization involving self-concordant-like cost functions; Tech. Report. 2015.
A. Yurtsever; Q. Tran Dinh; V. Cevher : A Universal Primal-Dual Convex Optimization Framework. 2015. 29th Annual Conference on Neural Information Processing Systems (NIPS2015), Montreal, Canada, December 7-12, 2015.
S. Srivastava; V. Cevher; Q. Tran Dinh; D. B. Dunson : WASP: Scalable Bayes via barycenters of subset posteriors. 2015. The 18th International Conference on Artificial Intelligence and Statistics, San Diego, USA, May 9-12, 2015.
Q. Tran Dinh; A. Kyrillidis; V. Cevher : Composite Self-Concordant Minimization; Journal of Machine Learning Research. 2015.
Q. Tran Dinh; V. Cevher : Constrained convex minimization via model-based excessive gap. 2014. Advances in Neural Information Processing Systems (NIPS) 2014, Montreal, Quebec, Canada, December 8-11, 2014.
Q. Tran Dinh; I. Necoara; M. Diehl : Path-following gradient-based decomposition algorithms for separable convex optimization; Journal of Global Optimization. 2014. DOI : 10.1007/s10898-013-0085-7.
Q. Tran Dinh; V. Cevher : A Primal-Dual Algorithmic Framework for Constrained Convex Minimization. 2014.
A. Kyrillidis; R. Karimi Mahabadi; Q. Tran Dinh; V. Cevher : Scalable sparse covariance estimation via self-concordance. 2014. Twenty-Eighth AAAI Conference on Artificial Intelligence, Quebec, Canada, July 27-31, 2014.
M. Signoretto; Q. Tran Dinh; L. De Lathauwer; J. A. K. Suykens : Learning with tensors: a framework based on convex optimization and spectral regularization; Machine Learning. 2014. DOI : 10.1007/s10994-013-5366-3.
Q. Tran Dinh; Y.-H. Li; V. Cevher : Barrier Smoothing for Nonsmooth Convex Minimization. 2014. IEEE International Conference on Acoustics, Speech, and Signal Processing, Florence, Italy, May 4-9, 2014.
Q. Tran Dinh; A. Kyrillidis; V. Cevher : An inexact proximal path-following algorithm for constrained convex minimization; Siam Journal On Optimization. 2014. DOI : 10.1137/130944539.
F. Debrouwere; W. Van Loock; G. Pipeleers; Q. Tran Dinh; M. Diehl et al. : Time-Optimal Path Following for Robots With Convex-Concave Constraints Using Sequential Convex Programming; Ieee Transactions On Robotics. 2013. DOI : 10.1109/Tro.2013.2277565.
M. McCoy; V. Cevher; Q. Tran Dinh; A. Asaei; L. Baldassarre : Convexity in source separation: Models, geometry, and algorithms; Signal Processing Magazine, IEEE. 2013. DOI : 10.1109/MSP.2013.2296605.
Q. Tran Dinh; A. Kyrillidis; V. Cevher : A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions. 2013. 30th International Conference on Machine Learning, Atlanta, GA, USA, June 16-19, 2013.