Postdoc Research Associate
University of Massachusetts Lowell
Welcome
Duy Nhat Phan is currently a postdoc at the Department of Mathematics and Statistics, University of Massachusetts Lowell. He obtained a Ph.D. in numerical optimization at the University of Lorraine, France. After that, he worked there as a Research Fellow for two years. Then, he worked for one year and a half at the Dynamic Decision-Making laboratory at Carnegie Mellon University.
Interests
- Convex optimization, nonconvex optimization
- Machine learning, stochastic learning
- Deep learning, reinforcement learning
Education
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PhD in Applied Mathematics
University of Lorraine
-
MS in Mathematics
University of Strasbourg
-
BS in Mathematics
HCMC University of Education
Journal Articles
(2022).
Inertial block majorization minimization framework for nonconvex nonsmooth optimization.
Journal of Machine Learning Research (Accepted).
(2022).
Inertial alternating direction method of multipliers for non-convex non-smooth optimization.
Computational Optimization and Applications, 83:247–285.
(2022).
SpeedyIBL: A comprehensive, precise, and fast implementation of instance-based learning theory.
Behavior Research Methods.
(2022).
Block Bregman Majorization Minimization with Extrapolation.
SIAM Journal on Mathematics of Data Science, 4:1-25.
(2021).
An accelerated IRNN-Iteratively Reweighted Nuclear Norm algorithm for nonconvex nonsmooth low-rank minimization problems.
Journal of Computational and Applied Mathematics, 396.
(2021).
Novel DCA based algorithms for a special class of nonconvex problems with application in machine learning.
Applied Mathematics and Computation, 409.
(2021).
DCA based approaches for bi-level variable selection and application for estimate multiple sparse covariance matrices.
Neurocomputing, 466:162-177.
(2020).
Stochastic DCA for minimizing a large sum of DC functions with application to multi-class logistic regression.
Neural Networks, 132: 220-231.
(2019).
Group variable selection via L_p,0 regularization and application to optimal scoring.
Neural Networks, 118: 220-234.
(2017).
Sparse Covariance Matrix Estimation by DCA-Based Algorithms.
Neural Computation, 29: 3040-3077.
(2017).
DC programming and DCA for sparse Fisher linear discriminant analysis.
Neural Computing and Applications, 28:2809-2822.
(2016).
DC programming and DCA for sparse optimal scoring problem.
Neurocomputing, 186:170-181.
Conference Articles
(2021).
A Cognitive Hysteretic-IBL Model for Coordinated Multi-Agent Transportation Problems.
Collective Intelligence.
(2018).
Accelerated Difference of Convex functions Algorithm and its Application to Sparse Binary Logistic Regression.
IJCAI.
(2017).
A Novel Approach for Estimating Multiple Sparse Precision Matrices Using L_0,0 Regularization.
DSAA.
(2017).
Stochastic DCA for the large-sum of non-convex functions problem and its application to group variable selection in classification.
ICML.
(2017).
Efficient bi-level variable selection and application to estimation of multiple covariance matrices.
PAKDD.
(2017).
Stochastic DCA for Sparse Multiclass Logistic Regression.
ICCSAMA.
(2015).
A DC Programming Approach for Sparse Optimal Scoring.
PAKDD.
(2014).
A dc programming approach for sparse linear discriminant analysis.
ICCSAMA.
Preprints
(2021).
DCA based Algorithm with Extrapolation for Nonconvex Nonsmooth Optimization.
Submitted.
(2021).
Advanced Difference-of-Convex functions Algorithms.
Submitted.
Contact
- duynhat_phan[AT]uml.edu
- 1 University Ave, Lowell, MA 01854